mirrors/z3
3
0
Fork 0
mirror of https://github.com/Z3Prover/z3 synced 2025-04-10 19:27:06 +00:00
Code Activity

Partial cleanup of util/lp/*

Browse source
This commit is contained in:
Christoph M. Wintersteiger 2017-09-17 16:00:06 +01:00
parent 00651f8f21
commit d61b722b68
109 changed files with 3503 additions and 2023 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

22
src/shell/lp_frontend.cpp
View file

@ -1,7 +1,7 @@
/*++
Copyright (c) 2016 Microsoft Corporation
Author:
Author:
Lev Nachmanson 2016-10-27
@ -17,7 +17,7 @@ Author:
#include "util/gparams.h"
#include <signal.h>
static lean::lp_solver<double, double>* g_solver = 0;
static lp::lp_solver<double, double>* g_solver = 0;
static void display_statistics() {
if (g_solver && g_solver->settings().print_statistics) {
@ -42,7 +42,7 @@ static void on_timeout() {
}
}
struct front_end_resource_limit : public lean::lp_resource_limit {
struct front_end_resource_limit : public lp::lp_resource_limit {
reslimit& m_reslim;
front_end_resource_limit(reslimit& lim):
@ -54,7 +54,7 @@ struct front_end_resource_limit : public lean::lp_resource_limit {
void run_solver(lp_params & params, char const * mps_file_name) {
reslimit rlim;
reslimit rlim;
unsigned timeout = gparams::get().get_uint("timeout", 0);
unsigned rlimit = gparams::get().get_uint("rlimit", 0);
front_end_resource_limit lp_limit(rlim);
@ -64,14 +64,14 @@ void run_solver(lp_params & params, char const * mps_file_name) {
scoped_timer timer(timeout, &eh);
std::string fn(mps_file_name);
lean::mps_reader<double, double> reader(fn);
lp::mps_reader<double, double> reader(fn);
reader.set_message_stream(&std::cout); // can be redirected
reader.read();
if (!reader.is_ok()) {
std::cerr << "cannot process " << mps_file_name << std::endl;
return;
}
lean::lp_solver<double, double> * solver = reader.create_solver(false); // false - to create the primal solver
lp::lp_solver<double, double> * solver = reader.create_solver(false); // false - to create the primal solver
solver->settings().set_resource_limit(lp_limit);
g_solver = solver;
if (params.min()) {
@ -80,20 +80,20 @@ void run_solver(lp_params & params, char const * mps_file_name) {
solver->settings().set_message_ostream(&std::cout);
solver->settings().report_frequency = params.rep_freq();
solver->settings().print_statistics = params.print_stats();
solver->settings().simplex_strategy() = lean:: simplex_strategy_enum::lu;
solver->settings().simplex_strategy() = lp:: simplex_strategy_enum::lu;
solver->find_maximal_solution();
*(solver->settings().get_message_ostream()) << "status is " << lp_status_to_string(solver->get_status()) << std::endl;
if (solver->get_status() == lean::OPTIMAL) {
if (solver->get_status() == lp::OPTIMAL) {
if (params.min()) {
solver->flip_costs();
}
solver->print_model(std::cout);
}
// #pragma omp critical (g_display_stats)
{
{
display_statistics();
register_on_timeout_proc(0);
g_solver = 0;

318
src/smt/theory_lra.cpp
View file

@ -38,7 +38,7 @@ Revision History:
#include "util/nat_set.h"
#include "tactic/filter_model_converter.h"
namespace lp {
namespace lra_lp {
enum bound_kind { lower_t, upper_t };
std::ostream& operator<<(std::ostream& out, bound_kind const& k) {
@ -50,7 +50,7 @@ namespace lp {
}
class bound {
smt::bool_var m_bv;
smt::bool_var m_bv;
smt::theory_var m_var;
rational m_value;
bound_kind m_bound_kind;
@ -111,7 +111,7 @@ namespace lp {
namespace smt {
typedef ptr_vector<lp::bound> lp_bounds;
typedef ptr_vector<lra_lp::bound> lp_bounds;
class theory_lra::imp {
@ -133,7 +133,7 @@ namespace smt {
delayed_atom(unsigned b, bool t): m_bv(b), m_is_true(t) {}
};
class resource_limit : public lean::lp_resource_limit {
class resource_limit : public lp::lp_resource_limit {
imp& m_imp;
public:
resource_limit(imp& i): m_imp(i) { }
@ -198,7 +198,7 @@ namespace smt {
}
};
typedef vector<std::pair<rational, lean::var_index>> var_coeffs;
typedef vector<std::pair<rational, lp::var_index>> var_coeffs;
struct delayed_def {
vector<rational> m_coeffs;
svector<theory_var> m_vars;
@ -208,11 +208,11 @@ namespace smt {
m_coeffs(coeffs), m_vars(vars), m_coeff(r), m_var(v) {}
};
svector<lean::var_index> m_theory_var2var_index; // translate from theory variables to lar vars
svector<lp::var_index> m_theory_var2var_index; // translate from theory variables to lar vars
svector<theory_var> m_var_index2theory_var; // reverse map from lp_solver variables to theory variables
svector<theory_var> m_term_index2theory_var; // reverse map from lp_solver variables to theory variables
var_coeffs m_left_side; // constraint left side
mutable std::unordered_map<lean::var_index, rational> m_variable_values; // current model
mutable std::unordered_map<lp::var_index, rational> m_variable_values; // current model
enum constraint_source {
inequality_source,
@ -233,10 +233,10 @@ namespace smt {
expr* m_not_handled;
ptr_vector<app> m_underspecified;
unsigned_vector m_var_trail;
vector<ptr_vector<lp::bound> > m_use_list; // bounds where variables are used.
vector<ptr_vector<lra_lp::bound> > m_use_list; // bounds where variables are used.
// attributes for incremental version:
u_map<lp::bound*> m_bool_var2bound;
u_map<lra_lp::bound*> m_bool_var2bound;
vector<lp_bounds> m_bounds;
unsigned_vector m_unassigned_bounds;
unsigned_vector m_bounds_trail;
@ -258,15 +258,15 @@ namespace smt {
struct var_value_hash {
imp & m_th;
var_value_hash(imp & th):m_th(th) {}
unsigned operator()(theory_var v) const { return (unsigned)std::hash<lean::impq>()(m_th.get_ivalue(v)); }
unsigned operator()(theory_var v) const { return (unsigned)std::hash<lp::impq>()(m_th.get_ivalue(v)); }
};
int_hashtable<var_value_hash, var_value_eq> m_model_eqs;
svector<scope> m_scopes;
lp::stats m_stats;
lra_lp::stats m_stats;
arith_factory* m_factory;
scoped_ptr<lean::lar_solver> m_solver;
scoped_ptr<lp::lar_solver> m_solver;
resource_limit m_resource_limit;
lp_bounds m_new_bounds;
@ -282,10 +282,10 @@ namespace smt {
void init_solver() {
if (m_solver) return;
lp_params lp(ctx().get_params());
m_solver = alloc(lean::lar_solver);
m_solver = alloc(lp::lar_solver);
m_theory_var2var_index.reset();
m_solver->settings().set_resource_limit(m_resource_limit);
m_solver->settings().simplex_strategy() = static_cast<lean::simplex_strategy_enum>(lp.simplex_strategy());
m_solver->settings().simplex_strategy() = static_cast<lp::simplex_strategy_enum>(lp.simplex_strategy());
reset_variable_values();
m_solver->settings().bound_propagation() = BP_NONE != propagation_mode();
m_solver->set_propagate_bounds_on_pivoted_rows_mode(lp.bprop_on_pivoted_rows());
@ -487,8 +487,8 @@ namespace smt {
return v;
}
lean::var_index get_var_index(theory_var v) {
lean::var_index result = UINT_MAX;
lp::var_index get_var_index(theory_var v) {
lp::var_index result = UINT_MAX;
if (m_theory_var2var_index.size() > static_cast<unsigned>(v)) {
result = m_theory_var2var_index[v];
}
@ -537,20 +537,20 @@ namespace smt {
return true;
}
void add_eq_constraint(lean::constraint_index index, enode* n1, enode* n2) {
void add_eq_constraint(lp::constraint_index index, enode* n1, enode* n2) {
m_constraint_sources.setx(index, equality_source, null_source);
m_equalities.setx(index, enode_pair(n1, n2), enode_pair(0, 0));
++m_stats.m_add_rows;
}
void add_ineq_constraint(lean::constraint_index index, literal lit) {
void add_ineq_constraint(lp::constraint_index index, literal lit) {
m_constraint_sources.setx(index, inequality_source, null_source);
m_inequalities.setx(index, lit, null_literal);
++m_stats.m_add_rows;
TRACE("arith", m_solver->print_constraint(index, tout); tout << "\n";);
}
void add_def_constraint(lean::constraint_index index, theory_var v) {
void add_def_constraint(lp::constraint_index index, theory_var v) {
m_constraint_sources.setx(index, definition_source, null_source);
m_definitions.setx(index, v, null_theory_var);
++m_stats.m_add_rows;
@ -561,7 +561,7 @@ namespace smt {
st.vars().append(d.m_vars);
st.coeffs().append(d.m_coeffs);
init_left_side(st);
add_def_constraint(m_solver->add_constraint(m_left_side, lean::EQ, -d.m_coeff), d.m_var);
add_def_constraint(m_solver->add_constraint(m_left_side, lp::EQ, -d.m_coeff), d.m_var);
}
void internalize_eq(theory_var v1, theory_var v2) {
@ -573,7 +573,7 @@ namespace smt {
st.coeffs().push_back(rational::one());
st.coeffs().push_back(rational::minus_one());
init_left_side(st);
add_eq_constraint(m_solver->add_constraint(m_left_side, lean::EQ, rational::zero()), n1, n2);
add_eq_constraint(m_solver->add_constraint(m_left_side, lp::EQ, rational::zero()), n1, n2);
TRACE("arith",
tout << "v" << v1 << " = " << "v" << v2 << ": "
<< mk_pp(n1->get_owner(), m) << " = " << mk_pp(n2->get_owner(), m) << "\n";);
@ -583,7 +583,7 @@ namespace smt {
for (unsigned i = m_bounds_trail.size(); i > old_size; ) {
--i;
unsigned v = m_bounds_trail[i];
lp::bound* b = m_bounds[v].back();
lra_lp::bound* b = m_bounds[v].back();
// del_use_lists(b);
dealloc(b);
m_bounds[v].pop_back();
@ -626,7 +626,7 @@ namespace smt {
else {
init_left_side(st);
theory_var v = mk_var(term);
lean::var_index vi = m_theory_var2var_index.get(v, UINT_MAX);
lp::var_index vi = m_theory_var2var_index.get(v, UINT_MAX);
if (vi == UINT_MAX) {
vi = m_solver->add_term(m_left_side, st.coeff());
m_theory_var2var_index.setx(v, vi, UINT_MAX);
@ -691,22 +691,22 @@ namespace smt {
ctx().set_var_theory(bv, get_id());
expr* n1, *n2;
rational r;
lp::bound_kind k;
lra_lp::bound_kind k;
theory_var v = null_theory_var;
if (a.is_le(atom, n1, n2) && is_numeral(n2, r) && is_app(n1)) {
v = internalize_def(to_app(n1));
k = lp::upper_t;
k = lra_lp::upper_t;
}
else if (a.is_ge(atom, n1, n2) && is_numeral(n2, r) && is_app(n1)) {
v = internalize_def(to_app(n1));
k = lp::lower_t;
k = lra_lp::lower_t;
}
else {
TRACE("arith", tout << "Could not internalize " << mk_pp(atom, m) << "\n";);
found_not_handled(atom);
return true;
}
lp::bound* b = alloc(lp::bound, bv, v, r, k);
lra_lp::bound* b = alloc(lra_lp::bound, bv, v, r, k);
m_bounds[v].push_back(b);
updt_unassigned_bounds(v, +1);
m_bounds_trail.push_back(v);
@ -723,23 +723,23 @@ namespace smt {
ctx().set_var_theory(bv, get_id());
expr* n1, *n2;
rational r;
lp::bound_kind k;
lra_lp::bound_kind k;
theory_var v = null_theory_var;
scoped_internalize_state st(*this);
if (a.is_le(atom, n1, n2) && is_numeral(n2, r) && is_app(n1)) {
v = internalize_def(to_app(n1), st);
k = lp::upper_t;
k = lra_lp::upper_t;
}
else if (a.is_ge(atom, n1, n2) && is_numeral(n2, r) && is_app(n1)) {
v = internalize_def(to_app(n1), st);
k = lp::lower_t;
k = lra_lp::lower_t;
}
else {
TRACE("arith", tout << "Could not internalize " << mk_pp(atom, m) << "\n";);
found_not_handled(atom);
return true;
}
lp::bound* b = alloc(lp::bound, bv, v, r, k);
lra_lp::bound* b = alloc(lra_lp::bound, bv, v, r, k);
m_bounds[v].push_back(b);
updt_unassigned_bounds(v, +1);
m_bounds_trail.push_back(v);
@ -830,7 +830,7 @@ namespace smt {
unsigned old_size = m_scopes.size() - num_scopes;
del_bounds(m_scopes[old_size].m_bounds_lim);
for (unsigned i = m_scopes[old_size].m_var_trail_lim; i < m_var_trail.size(); ++i) {
lean::var_index vi = m_theory_var2var_index[m_var_trail[i]];
lp::var_index vi = m_theory_var2var_index[m_var_trail[i]];
if (m_solver->is_term(vi)) {
unsigned ti = m_solver->adjust_term_index(vi);
m_term_index2theory_var[ti] = UINT_MAX;
@ -1023,14 +1023,14 @@ namespace smt {
return m_solver->var_is_registered(m_theory_var2var_index[v]);
}
lean::impq get_ivalue(theory_var v) const {
lean_assert(can_get_ivalue(v));
lean::var_index vi = m_theory_var2var_index[v];
lp::impq get_ivalue(theory_var v) const {
SASSERT(can_get_ivalue(v));
lp::var_index vi = m_theory_var2var_index[v];
if (!m_solver->is_term(vi))
return m_solver->get_value(vi);
const lean::lar_term& term = m_solver->get_term(vi);
lean::impq result(term.m_v);
const lp::lar_term& term = m_solver->get_term(vi);
lp::impq result(term.m_v);
for (const auto & i: term.m_coeffs) {
result += m_solver->get_value(i.first) * i.second;
}
@ -1040,12 +1040,12 @@ namespace smt {
rational get_value(theory_var v) const {
if (!can_get_value(v)) return rational::zero();
lean::var_index vi = m_theory_var2var_index[v];
lp::var_index vi = m_theory_var2var_index[v];
if (m_variable_values.count(vi) > 0) {
return m_variable_values[vi];
}
if (m_solver->is_term(vi)) {
const lean::lar_term& term = m_solver->get_term(vi);
const lp::lar_term& term = m_solver->get_term(vi);
rational result = term.m_v;
for (auto i = term.m_coeffs.begin(); i != term.m_coeffs.end(); ++i) {
result += m_variable_values[i->first] * i->second;
@ -1068,7 +1068,7 @@ namespace smt {
}
bool assume_eqs() {
svector<lean::var_index> vars;
svector<lp::var_index> vars;
theory_var sz = static_cast<theory_var>(th.get_num_vars());
for (theory_var v = 0; v < sz; ++v) {
if (th.is_relevant_and_shared(get_enode(v))) {
@ -1169,7 +1169,7 @@ namespace smt {
}
is_sat = make_feasible();
}
else if (m_solver->get_status() != lean::lp_status::OPTIMAL) {
else if (m_solver->get_status() != lp::lp_status::OPTIMAL) {
is_sat = make_feasible();
}
switch (is_sat) {
@ -1266,7 +1266,7 @@ namespace smt {
propagate_bound(bv, is_true, b);
#endif
if (!m_delay_constraints) {
lp::bound& b = *m_bool_var2bound.find(bv);
lra_lp::bound& b = *m_bool_var2bound.find(bv);
assert_bound(bv, is_true, b);
}
@ -1279,7 +1279,7 @@ namespace smt {
/*for (; qhead < m_asserted_atoms.size() && !ctx().inconsistent(); ++qhead) {
bool_var bv = m_asserted_atoms[qhead].m_bv;
bool is_true = m_asserted_atoms[qhead].m_is_true;
lp::bound& b = *m_bool_var2bound.find(bv);
lra_lp::bound& b = *m_bool_var2bound.find(bv);
propagate_bound_compound(bv, is_true, b);
}*/
@ -1314,7 +1314,7 @@ namespace smt {
int new_num_of_p = m_solver->settings().st().m_num_of_implied_bounds;
(void)new_num_of_p;
CTRACE("arith", new_num_of_p > num_of_p, tout << "found " << new_num_of_p << " implied bounds\n";);
if (m_solver->get_status() == lean::lp_status::INFEASIBLE) {
if (m_solver->get_status() == lp::lp_status::INFEASIBLE) {
set_conflict();
}
else {
@ -1324,7 +1324,7 @@ namespace smt {
}
}
bool bound_is_interesting(unsigned vi, lean::lconstraint_kind kind, const rational & bval) const {
bool bound_is_interesting(unsigned vi, lp::lconstraint_kind kind, const rational & bval) const {
theory_var v;
if (m_solver->is_term(vi)) {
v = m_term_index2theory_var.get(m_solver->adjust_term_index(vi), null_theory_var);
@ -1341,7 +1341,7 @@ namespace smt {
}
lp_bounds const& bounds = m_bounds[v];
for (unsigned i = 0; i < bounds.size(); ++i) {
lp::bound* b = bounds[i];
lra_lp::bound* b = bounds[i];
if (ctx().get_assignment(b->get_bv()) != l_undef) {
continue;
}
@ -1354,11 +1354,11 @@ namespace smt {
return false;
}
struct local_bound_propagator: public lean::lp_bound_propagator {
struct local_bound_propagator: public lp::lp_bound_propagator {
imp & m_imp;
local_bound_propagator(imp& i) : lp_bound_propagator(*i.m_solver), m_imp(i) {}
bool bound_is_interesting(unsigned j, lean::lconstraint_kind kind, const rational & v) {
bool bound_is_interesting(unsigned j, lp::lconstraint_kind kind, const rational & v) {
return m_imp.bound_is_interesting(j, kind, v);
}
@ -1368,10 +1368,10 @@ namespace smt {
};
void propagate_lp_solver_bound(lean::implied_bound& be) {
void propagate_lp_solver_bound(lp::implied_bound& be) {
theory_var v;
lean::var_index vi = be.m_j;
lp::var_index vi = be.m_j;
if (m_solver->is_term(vi)) {
v = m_term_index2theory_var.get(m_solver->adjust_term_index(vi), null_theory_var);
}
@ -1392,7 +1392,7 @@ namespace smt {
lp_bounds const& bounds = m_bounds[v];
bool first = true;
for (unsigned i = 0; i < bounds.size(); ++i) {
lp::bound* b = bounds[i];
lra_lp::bound* b = bounds[i];
if (ctx().get_assignment(b->get_bv()) != l_undef) {
continue;
}
@ -1455,28 +1455,28 @@ namespace smt {
}
}
literal is_bound_implied(lean::lconstraint_kind k, rational const& value, lp::bound const& b) const {
if ((k == lean::LE || k == lean::LT) && b.get_bound_kind() == lp::upper_t && value <= b.get_value()) {
literal is_bound_implied(lp::lconstraint_kind k, rational const& value, lra_lp::bound const& b) const {
if ((k == lp::LE || k == lp::LT) && b.get_bound_kind() == lra_lp::upper_t && value <= b.get_value()) {
// v <= value <= b.get_value() => v <= b.get_value()
return literal(b.get_bv(), false);
}
if ((k == lean::GE || k == lean::GT) && b.get_bound_kind() == lp::lower_t && b.get_value() <= value) {
if ((k == lp::GE || k == lp::GT) && b.get_bound_kind() == lra_lp::lower_t && b.get_value() <= value) {
// b.get_value() <= value <= v => b.get_value() <= v
return literal(b.get_bv(), false);
}
if (k == lean::LE && b.get_bound_kind() == lp::lower_t && value < b.get_value()) {
if (k == lp::LE && b.get_bound_kind() == lra_lp::lower_t && value < b.get_value()) {
// v <= value < b.get_value() => v < b.get_value()
return literal(b.get_bv(), true);
}
if (k == lean::LT && b.get_bound_kind() == lp::lower_t && value <= b.get_value()) {
if (k == lp::LT && b.get_bound_kind() == lra_lp::lower_t && value <= b.get_value()) {
// v < value <= b.get_value() => v < b.get_value()
return literal(b.get_bv(), true);
}
if (k == lean::GE && b.get_bound_kind() == lp::upper_t && b.get_value() < value) {
if (k == lp::GE && b.get_bound_kind() == lra_lp::upper_t && b.get_value() < value) {
// b.get_value() < value <= v => b.get_value() < v
return literal(b.get_bv(), true);
}
if (k == lean::GT && b.get_bound_kind() == lp::upper_t && b.get_value() <= value) {
if (k == lp::GT && b.get_bound_kind() == lra_lp::upper_t && b.get_value() <= value) {
// b.get_value() <= value < v => b.get_value() < v
return literal(b.get_bv(), true);
}
@ -1484,7 +1484,7 @@ namespace smt {
return null_literal;
}
void mk_bound_axioms(lp::bound& b) {
void mk_bound_axioms(lra_lp::bound& b) {
if (!ctx().is_searching()) {
//
// NB. We make an assumption that user push calls propagation
@ -1495,19 +1495,19 @@ namespace smt {
return;
}
theory_var v = b.get_var();
lp::bound_kind kind1 = b.get_bound_kind();
lra_lp::bound_kind kind1 = b.get_bound_kind();
rational const& k1 = b.get_value();
lp_bounds & bounds = m_bounds[v];
lp::bound* end = 0;
lp::bound* lo_inf = end, *lo_sup = end;
lp::bound* hi_inf = end, *hi_sup = end;
lra_lp::bound* end = 0;
lra_lp::bound* lo_inf = end, *lo_sup = end;
lra_lp::bound* hi_inf = end, *hi_sup = end;
for (unsigned i = 0; i < bounds.size(); ++i) {
lp::bound& other = *bounds[i];
lra_lp::bound& other = *bounds[i];
if (&other == &b) continue;
if (b.get_bv() == other.get_bv()) continue;
lp::bound_kind kind2 = other.get_bound_kind();
lra_lp::bound_kind kind2 = other.get_bound_kind();
rational const& k2 = other.get_value();
if (k1 == k2 && kind1 == kind2) {
// the bounds are equivalent.
@ -1515,7 +1515,7 @@ namespace smt {
}
SASSERT(k1 != k2 || kind1 != kind2);
if (kind2 == lp::lower_t) {
if (kind2 == lra_lp::lower_t) {
if (k2 < k1) {
if (lo_inf == end || k2 > lo_inf->get_value()) {
lo_inf = &other;
@ -1541,14 +1541,14 @@ namespace smt {
}
void mk_bound_axiom(lp::bound& b1, lp::bound& b2) {
void mk_bound_axiom(lra_lp::bound& b1, lra_lp::bound& b2) {
theory_var v = b1.get_var();
literal l1(b1.get_bv());
literal l2(b2.get_bv());
rational const& k1 = b1.get_value();
rational const& k2 = b2.get_value();
lp::bound_kind kind1 = b1.get_bound_kind();
lp::bound_kind kind2 = b2.get_bound_kind();
lra_lp::bound_kind kind1 = b1.get_bound_kind();
lra_lp::bound_kind kind2 = b2.get_bound_kind();
bool v_is_int = is_int(v);
SASSERT(v == b2.get_var());
if (k1 == k2 && kind1 == kind2) return;
@ -1556,8 +1556,8 @@ namespace smt {
parameter coeffs[3] = { parameter(symbol("farkas")),
parameter(rational(1)), parameter(rational(1)) };
if (kind1 == lp::lower_t) {
if (kind2 == lp::lower_t) {
if (kind1 == lra_lp::lower_t) {
if (kind2 == lra_lp::lower_t) {
if (k2 <= k1) {
mk_clause(~l1, l2, 3, coeffs);
}
@ -1578,7 +1578,7 @@ namespace smt {
}
}
}
else if (kind2 == lp::lower_t) {
else if (kind2 == lra_lp::lower_t) {
if (k1 >= k2) {
// k1 >= lo_inf, k1 >= x or lo_inf <= x
mk_clause(l1, l2, 3, coeffs);
@ -1636,21 +1636,21 @@ namespace smt {
iterator begin1 = occs.begin();
iterator begin2 = occs.begin();
iterator end = occs.end();
begin1 = first(lp::lower_t, begin1, end);
begin2 = first(lp::upper_t, begin2, end);
begin1 = first(lra_lp::lower_t, begin1, end);
begin2 = first(lra_lp::upper_t, begin2, end);
iterator lo_inf = begin1, lo_sup = begin1;
iterator hi_inf = begin2, hi_sup = begin2;
iterator lo_inf1 = begin1, lo_sup1 = begin1;
iterator hi_inf1 = begin2, hi_sup1 = begin2;
bool flo_inf, fhi_inf, flo_sup, fhi_sup;
ptr_addr_hashtable<lp::bound> visited;
ptr_addr_hashtable<lra_lp::bound> visited;
for (unsigned i = 0; i < atoms.size(); ++i) {
lp::bound* a1 = atoms[i];
lo_inf1 = next_inf(a1, lp::lower_t, lo_inf, end, flo_inf);
hi_inf1 = next_inf(a1, lp::upper_t, hi_inf, end, fhi_inf);
lo_sup1 = next_sup(a1, lp::lower_t, lo_sup, end, flo_sup);
hi_sup1 = next_sup(a1, lp::upper_t, hi_sup, end, fhi_sup);
lra_lp::bound* a1 = atoms[i];
lo_inf1 = next_inf(a1, lra_lp::lower_t, lo_inf, end, flo_inf);
hi_inf1 = next_inf(a1, lra_lp::upper_t, hi_inf, end, fhi_inf);
lo_sup1 = next_sup(a1, lra_lp::lower_t, lo_sup, end, flo_sup);
hi_sup1 = next_sup(a1, lra_lp::upper_t, hi_sup, end, fhi_sup);
if (lo_inf1 != end) lo_inf = lo_inf1;
if (lo_sup1 != end) lo_sup = lo_sup1;
if (hi_inf1 != end) hi_inf = hi_inf1;
@ -1669,24 +1669,24 @@ namespace smt {
}
struct compare_bounds {
bool operator()(lp::bound* a1, lp::bound* a2) const { return a1->get_value() < a2->get_value(); }
bool operator()(lra_lp::bound* a1, lra_lp::bound* a2) const { return a1->get_value() < a2->get_value(); }
};
lp_bounds::iterator first(
lp::bound_kind kind,
lra_lp::bound_kind kind,
iterator it,
iterator end) {
for (; it != end; ++it) {
lp::bound* a = *it;
lra_lp::bound* a = *it;
if (a->get_bound_kind() == kind) return it;
}
return end;
}
lp_bounds::iterator next_inf(
lp::bound* a1,
lp::bound_kind kind,
lra_lp::bound* a1,
lra_lp::bound_kind kind,
iterator it,
iterator end,
bool& found_compatible) {
@ -1694,7 +1694,7 @@ namespace smt {
iterator result = end;
found_compatible = false;
for (; it != end; ++it) {
lp::bound * a2 = *it;
lra_lp::bound * a2 = *it;
if (a1 == a2) continue;
if (a2->get_bound_kind() != kind) continue;
rational const & k2(a2->get_value());
@ -1710,15 +1710,15 @@ namespace smt {
}
lp_bounds::iterator next_sup(
lp::bound* a1,
lp::bound_kind kind,
lra_lp::bound* a1,
lra_lp::bound_kind kind,
iterator it,
iterator end,
bool& found_compatible) {
rational const & k1(a1->get_value());
found_compatible = false;
for (; it != end; ++it) {
lp::bound * a2 = *it;
lra_lp::bound * a2 = *it;
if (a1 == a2) continue;
if (a2->get_bound_kind() != kind) continue;
rational const & k2(a2->get_value());
@ -1732,7 +1732,7 @@ namespace smt {
void propagate_basic_bounds() {
for (auto const& bv : m_to_check) {
lp::bound& b = *m_bool_var2bound.find(bv);
lra_lp::bound& b = *m_bool_var2bound.find(bv);
propagate_bound(bv, ctx().get_assignment(bv) == l_true, b);
if (ctx().inconsistent()) break;
@ -1747,11 +1747,11 @@ namespace smt {
// x <= hi -> x <= hi'
// x <= hi -> ~(x >= hi')
void propagate_bound(bool_var bv, bool is_true, lp::bound& b) {
void propagate_bound(bool_var bv, bool is_true, lra_lp::bound& b) {
if (BP_NONE == propagation_mode()) {
return;
}
lp::bound_kind k = b.get_bound_kind();
lra_lp::bound_kind k = b.get_bound_kind();
theory_var v = b.get_var();
inf_rational val = b.get_value(is_true);
lp_bounds const& bounds = m_bounds[v];
@ -1761,12 +1761,12 @@ namespace smt {
literal lit1(bv, !is_true);
literal lit2 = null_literal;
bool find_glb = (is_true == (k == lp::lower_t));
bool find_glb = (is_true == (k == lra_lp::lower_t));
if (find_glb) {
rational glb;
lp::bound* lb = 0;
lra_lp::bound* lb = 0;
for (unsigned i = 0; i < bounds.size(); ++i) {
lp::bound* b2 = bounds[i];
lra_lp::bound* b2 = bounds[i];
if (b2 == &b) continue;
rational const& val2 = b2->get_value();
if ((is_true ? val2 < val : val2 <= val) && (!lb || glb < val2)) {
@ -1775,14 +1775,14 @@ namespace smt {
}
}
if (!lb) return;
bool sign = lb->get_bound_kind() != lp::lower_t;
bool sign = lb->get_bound_kind() != lra_lp::lower_t;
lit2 = literal(lb->get_bv(), sign);
}
else {
rational lub;
lp::bound* ub = 0;
lra_lp::bound* ub = 0;
for (unsigned i = 0; i < bounds.size(); ++i) {
lp::bound* b2 = bounds[i];
lra_lp::bound* b2 = bounds[i];
if (b2 == &b) continue;
rational const& val2 = b2->get_value();
if ((is_true ? val < val2 : val <= val2) && (!ub || val2 < lub)) {
@ -1791,7 +1791,7 @@ namespace smt {
}
}
if (!ub) return;
bool sign = ub->get_bound_kind() != lp::upper_t;
bool sign = ub->get_bound_kind() != lra_lp::upper_t;
lit2 = literal(ub->get_bv(), sign);
}
TRACE("arith",
@ -1811,27 +1811,27 @@ namespace smt {
++m_stats.m_bounds_propagations;
}
void add_use_lists(lp::bound* b) {
void add_use_lists(lra_lp::bound* b) {
theory_var v = b->get_var();
lean::var_index vi = get_var_index(v);
lp::var_index vi = get_var_index(v);
if (m_solver->is_term(vi)) {
lean::lar_term const& term = m_solver->get_term(vi);
lp::lar_term const& term = m_solver->get_term(vi);
for (auto i = term.m_coeffs.begin(); i != term.m_coeffs.end(); ++i) {
lean::var_index wi = i->first;
lp::var_index wi = i->first;
unsigned w = m_var_index2theory_var[wi];
m_use_list.reserve(w + 1, ptr_vector<lp::bound>());
m_use_list.reserve(w + 1, ptr_vector<lra_lp::bound>());
m_use_list[w].push_back(b);
}
}
}
void del_use_lists(lp::bound* b) {
void del_use_lists(lra_lp::bound* b) {
theory_var v = b->get_var();
lean::var_index vi = m_theory_var2var_index[v];
lp::var_index vi = m_theory_var2var_index[v];
if (m_solver->is_term(vi)) {
lean::lar_term const& term = m_solver->get_term(vi);
lp::lar_term const& term = m_solver->get_term(vi);
for (auto i = term.m_coeffs.begin(); i != term.m_coeffs.end(); ++i) {
lean::var_index wi = i->first;
lp::var_index wi = i->first;
unsigned w = m_var_index2theory_var[wi];
SASSERT(m_use_list[w].back() == b);
m_use_list[w].pop_back();
@ -1845,7 +1845,7 @@ namespace smt {
// have been assigned we may know the truth value of the inequality by using simple
// bounds propagation.
//
void propagate_bound_compound(bool_var bv, bool is_true, lp::bound& b) {
void propagate_bound_compound(bool_var bv, bool is_true, lra_lp::bound& b) {
theory_var v = b.get_var();
TRACE("arith", tout << mk_pp(get_owner(v), m) << "\n";);
if (static_cast<unsigned>(v) >= m_use_list.size()) {
@ -1861,7 +1861,7 @@ namespace smt {
// x >= 0, y >= 1 -> x + y >= 1
// x <= 0, y <= 2 -> x + y <= 2
literal lit = null_literal;
if (lp::lower_t == vb->get_bound_kind()) {
if (lra_lp::lower_t == vb->get_bound_kind()) {
if (get_glb(*vb, r) && r >= vb->get_value()) { // vb is assigned true
lit = literal(vb->get_bv(), false);
}
@ -1895,30 +1895,30 @@ namespace smt {
}
}
bool get_lub(lp::bound const& b, inf_rational& lub) {
bool get_lub(lra_lp::bound const& b, inf_rational& lub) {
return get_bound(b, lub, true);
}
bool get_glb(lp::bound const& b, inf_rational& glb) {
bool get_glb(lra_lp::bound const& b, inf_rational& glb) {
return get_bound(b, glb, false);
}
std::ostream& display_bound(std::ostream& out, lp::bound const& b) {
std::ostream& display_bound(std::ostream& out, lra_lp::bound const& b) {
return out << mk_pp(ctx().bool_var2expr(b.get_bv()), m);
}
bool get_bound(lp::bound const& b, inf_rational& r, bool is_lub) {
bool get_bound(lra_lp::bound const& b, inf_rational& r, bool is_lub) {
m_core.reset();
m_eqs.reset();
m_params.reset();
r.reset();
theory_var v = b.get_var();
lean::var_index vi = m_theory_var2var_index[v];
lp::var_index vi = m_theory_var2var_index[v];
SASSERT(m_solver->is_term(vi));
lean::lar_term const& term = m_solver->get_term(vi);
lp::lar_term const& term = m_solver->get_term(vi);
for (auto const coeff : term.m_coeffs) {
lean::var_index wi = coeff.first;
lean::constraint_index ci;
lp::var_index wi = coeff.first;
lp::constraint_index ci;
rational value;
bool is_strict;
if (coeff.second.is_neg() == is_lub) {
@ -1945,24 +1945,24 @@ namespace smt {
return true;
}
void assert_bound(bool_var bv, bool is_true, lp::bound& b) {
if (m_solver->get_status() == lean::lp_status::INFEASIBLE) {
void assert_bound(bool_var bv, bool is_true, lra_lp::bound& b) {
if (m_solver->get_status() == lp::lp_status::INFEASIBLE) {
return;
}
scoped_internalize_state st(*this);
st.vars().push_back(b.get_var());
st.coeffs().push_back(rational::one());
init_left_side(st);
lean::lconstraint_kind k = lean::EQ;
lp::lconstraint_kind k = lp::EQ;
switch (b.get_bound_kind()) {
case lp::lower_t:
k = is_true ? lean::GE : lean::LT;
case lra_lp::lower_t:
k = is_true ? lp::GE : lp::LT;
break;
case lp::upper_t:
k = is_true ? lean::LE : lean::GT;
case lra_lp::upper_t:
k = is_true ? lp::LE : lp::GT;
break;
}
if (k == lean::LT || k == lean::LE) {
if (k == lp::LT || k == lp::LE) {
++m_stats.m_assert_lower;
}
else {
@ -1983,7 +1983,7 @@ namespace smt {
// Then the equality v1 == v2 is propagated to the core.
//
typedef std::pair<lean::constraint_index, rational> constraint_bound;
typedef std::pair<lp::constraint_index, rational> constraint_bound;
vector<constraint_bound> m_lower_terms;
vector<constraint_bound> m_upper_terms;
typedef std::pair<rational, bool> value_sort_pair;
@ -1991,16 +1991,16 @@ namespace smt {
typedef map<value_sort_pair, theory_var, value_sort_pair_hash, default_eq<value_sort_pair> > value2var;
value2var m_fixed_var_table;
void propagate_eqs(lean::var_index vi, lean::constraint_index ci, lean::lconstraint_kind k, lp::bound& b) {
void propagate_eqs(lp::var_index vi, lp::constraint_index ci, lp::lconstraint_kind k, lra_lp::bound& b) {
if (propagate_eqs()) {
rational const& value = b.get_value();
if (k == lean::GE) {
if (k == lp::GE) {
set_lower_bound(vi, ci, value);
if (has_upper_bound(vi, ci, value)) {
fixed_var_eh(b.get_var(), value);
}
}
else if (k == lean::LE) {
else if (k == lp::LE) {
set_upper_bound(vi, ci, value);
if (has_lower_bound(vi, ci, value)) {
fixed_var_eh(b.get_var(), value);
@ -2021,16 +2021,16 @@ namespace smt {
bool use_tableau() const { return lp_params(ctx().get_params()).simplex_strategy() < 2; }
void set_upper_bound(lean::var_index vi, lean::constraint_index ci, rational const& v) { set_bound(vi, ci, v, false); }
void set_upper_bound(lp::var_index vi, lp::constraint_index ci, rational const& v) { set_bound(vi, ci, v, false); }
void set_lower_bound(lean::var_index vi, lean::constraint_index ci, rational const& v) { set_bound(vi, ci, v, true); }
void set_lower_bound(lp::var_index vi, lp::constraint_index ci, rational const& v) { set_bound(vi, ci, v, true); }
void set_bound(lean::var_index vi, lean::constraint_index ci, rational const& v, bool is_lower) {
void set_bound(lp::var_index vi, lp::constraint_index ci, rational const& v, bool is_lower) {
if (!m_solver->is_term(vi)) {
// m_solver already tracks bounds on proper variables, but not on terms.
return;
}
lean::var_index ti = m_solver->adjust_term_index(vi);
lp::var_index ti = m_solver->adjust_term_index(vi);
auto& vec = is_lower ? m_lower_terms : m_upper_terms;
if (vec.size() <= ti) {
vec.resize(ti + 1, constraint_bound(UINT_MAX, rational()));
@ -2043,15 +2043,15 @@ namespace smt {
}
}
bool has_upper_bound(lean::var_index vi, lean::constraint_index& ci, rational const& bound) { return has_bound(vi, ci, bound, false); }
bool has_upper_bound(lp::var_index vi, lp::constraint_index& ci, rational const& bound) { return has_bound(vi, ci, bound, false); }
bool has_lower_bound(lean::var_index vi, lean::constraint_index& ci, rational const& bound) { return has_bound(vi, ci, bound, true); }
bool has_lower_bound(lp::var_index vi, lp::constraint_index& ci, rational const& bound) { return has_bound(vi, ci, bound, true); }
bool has_bound(lean::var_index vi, lean::constraint_index& ci, rational const& bound, bool is_lower) {
bool has_bound(lp::var_index vi, lp::constraint_index& ci, rational const& bound, bool is_lower) {
if (m_solver->is_term(vi)) {
lean::var_index ti = m_solver->adjust_term_index(vi);
lp::var_index ti = m_solver->adjust_term_index(vi);
theory_var v = m_term_index2theory_var.get(ti, null_theory_var);
rational val;
TRACE("arith", tout << vi << " " << v << "\n";);
@ -2094,7 +2094,7 @@ namespace smt {
if (static_cast<unsigned>(v2) < th.get_num_vars() && !is_equal(v1, v2)) {
auto vi1 = get_var_index(v1);
auto vi2 = get_var_index(v2);
lean::constraint_index ci1, ci2, ci3, ci4;
lp::constraint_index ci1, ci2, ci3, ci4;
TRACE("arith", tout << "fixed: " << mk_pp(get_owner(v1), m) << " " << mk_pp(get_owner(v2), m) << " " << bound << " " << has_lower_bound(vi2, ci3, bound) << "\n";);
if (has_lower_bound(vi2, ci3, bound) && has_upper_bound(vi2, ci4, bound)) {
VERIFY (has_lower_bound(vi1, ci1, bound));
@ -2148,19 +2148,19 @@ namespace smt {
if (m_solver->A_r().row_count() > m_stats.m_max_rows)
m_stats.m_max_rows = m_solver->A_r().row_count();
TRACE("arith_verbose", display(tout););
lean::lp_status status = m_solver->find_feasible_solution();
lp::lp_status status = m_solver->find_feasible_solution();
m_stats.m_num_iterations = m_solver->settings().st().m_total_iterations;
m_stats.m_num_factorizations = m_solver->settings().st().m_num_factorizations;
m_stats.m_need_to_solve_inf = m_solver->settings().st().m_need_to_solve_inf;
switch (status) {
case lean::lp_status::INFEASIBLE:
case lp::lp_status::INFEASIBLE:
return l_false;
case lean::lp_status::FEASIBLE:
case lean::lp_status::OPTIMAL:
case lp::lp_status::FEASIBLE:
case lp::lp_status::OPTIMAL:
// SASSERT(m_solver->all_constraints_hold());
return l_true;
case lean::lp_status::TIME_EXHAUSTED:
case lp::lp_status::TIME_EXHAUSTED:
default:
TRACE("arith", tout << "status treated as inconclusive: " << status << "\n";);
@ -2170,14 +2170,14 @@ namespace smt {
}
}
vector<std::pair<rational, lean::constraint_index>> m_explanation;
vector<std::pair<rational, lp::constraint_index>> m_explanation;
literal_vector m_core;
svector<enode_pair> m_eqs;
vector<parameter> m_params;
// lean::constraint_index const null_constraint_index = UINT_MAX; // not sure what a correct fix is
// lp::constraint_index const null_constraint_index = UINT_MAX; // not sure what a correct fix is
void set_evidence(lean::constraint_index idx) {
void set_evidence(lp::constraint_index idx) {
if (idx == UINT_MAX) {
return;
}
@ -2327,16 +2327,16 @@ namespace smt {
}
theory_lra::inf_eps value(theory_var v) {
lean::impq ival = get_ivalue(v);
lp::impq ival = get_ivalue(v);
return inf_eps(0, inf_rational(ival.x, ival.y));
}
theory_lra::inf_eps maximize(theory_var v, expr_ref& blocker, bool& has_shared) {
lean::var_index vi = m_theory_var2var_index.get(v, UINT_MAX);
vector<std::pair<rational, lean::var_index> > coeffs;
lp::var_index vi = m_theory_var2var_index.get(v, UINT_MAX);
vector<std::pair<rational, lp::var_index> > coeffs;
rational coeff;
if (m_solver->is_term(vi)) {
const lean::lar_term& term = m_solver->get_term(vi);
const lp::lar_term& term = m_solver->get_term(vi);
for (auto & ti : term.m_coeffs) {
coeffs.push_back(std::make_pair(ti.second, ti.first));
}
@ -2346,7 +2346,7 @@ namespace smt {
coeffs.push_back(std::make_pair(rational::one(), vi));
coeff = rational::zero();
}
lean::impq term_max;
lp::impq term_max;
if (m_solver->maximize_term(coeffs, term_max)) {
blocker = mk_gt(v);
inf_rational val(term_max.x + coeff, term_max.y);
@ -2361,7 +2361,7 @@ namespace smt {
}
expr_ref mk_gt(theory_var v) {
lean::impq val = get_ivalue(v);
lp::impq val = get_ivalue(v);
expr* obj = get_enode(v)->get_owner();
rational r = val.x;
expr_ref e(m);
@ -2393,11 +2393,11 @@ namespace smt {
}
app_ref mk_obj(theory_var v) {
lean::var_index vi = m_theory_var2var_index[v];
lp::var_index vi = m_theory_var2var_index[v];
bool is_int = a.is_int(get_enode(v)->get_owner());
if (m_solver->is_term(vi)) {
expr_ref_vector args(m);
const lean::lar_term& term = m_solver->get_term(vi);
const lp::lar_term& term = m_solver->get_term(vi);
for (auto & ti : term.m_coeffs) {
theory_var w = m_var_index2theory_var[ti.first];
expr* o = get_enode(w)->get_owner();
@ -2428,9 +2428,9 @@ namespace smt {
bool_var bv = ctx().mk_bool_var(b);
ctx().set_var_theory(bv, get_id());
// ctx().set_enode_flag(bv, true);
lp::bound_kind bkind = lp::bound_kind::lower_t;
if (is_strict) bkind = lp::bound_kind::upper_t;
lp::bound* a = alloc(lp::bound, bv, v, r, bkind);
lra_lp::bound_kind bkind = lra_lp::bound_kind::lower_t;
if (is_strict) bkind = lra_lp::bound_kind::upper_t;
lra_lp::bound* a = alloc(lra_lp::bound, bv, v, r, bkind);
mk_bound_axioms(*a);
updt_unassigned_bounds(v, +1);
m_bounds[v].push_back(a);
@ -2462,7 +2462,7 @@ namespace smt {
}
}
void display_evidence(std::ostream& out, vector<std::pair<rational, lean::constraint_index>> const& evidence) {
void display_evidence(std::ostream& out, vector<std::pair<rational, lp::constraint_index>> const& evidence) {
for (auto const& ev : evidence) {
expr_ref e(m);
SASSERT(!ev.first.is_zero());

31
src/util/lp/binary_heap_priority_queue.h
View file

@ -1,13 +1,28 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
#include "util/debug.h"
#include "util/lp/lp_utils.h"
namespace lean {
namespace lp {
// the elements with the smallest priority are dequeued first
template <typename T>
class binary_heap_priority_queue {
@ -22,7 +37,7 @@ class binary_heap_priority_queue {
void put_at(unsigned i, unsigned h);
void decrease_priority(unsigned o, T newPriority);
public:
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
bool is_consistent() const;
#endif
public:
@ -60,10 +75,10 @@ public:
/// return the first element of the queue and removes it from the queue
unsigned dequeue();
unsigned peek() const {
lean_assert(m_heap_size > 0);
SASSERT(m_heap_size > 0);
return m_heap[1];
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
void print(std::ostream & out);
#endif
};

48
src/util/lp/binary_heap_priority_queue.hpp
View file

@ -1,11 +1,26 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/vector.h"
#include "util/lp/binary_heap_priority_queue.h"
namespace lean {
// is is the child place in heap
namespace lp {
// this is the child place in the heap
template <typename T> void binary_heap_priority_queue<T>::swap_with_parent(unsigned i) {
unsigned parent = m_heap[i >> 1];
put_at(i >> 1, m_heap[i]);
@ -29,12 +44,12 @@ template <typename T> void binary_heap_priority_queue<T>::decrease_priority(unsi
}
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T> bool binary_heap_priority_queue<T>::is_consistent() const {
for (int i = 0; i < m_heap_inverse.size(); i++) {
int i_index = m_heap_inverse[i];
lean_assert(i_index <= static_cast<int>(m_heap_size));
lean_assert(i_index == -1 || m_heap[i_index] == i);
SASSERT(i_index <= static_cast<int>(m_heap_size));
SASSERT(i_index == -1 || m_heap[i_index] == i);
}
for (unsigned i = 1; i < m_heap_size; i++) {
unsigned ch = i << 1;
@ -49,13 +64,14 @@ template <typename T> bool binary_heap_priority_queue<T>::is_consistent() const
return true;
}
#endif
template <typename T> void binary_heap_priority_queue<T>::remove(unsigned o) {
T priority_of_o = m_priorities[o];
int o_in_heap = m_heap_inverse[o];
if (o_in_heap == -1) {
return; // nothing to do
}
lean_assert(static_cast<unsigned>(o_in_heap) <= m_heap_size);
SASSERT(static_cast<unsigned>(o_in_heap) <= m_heap_size);
if (static_cast<unsigned>(o_in_heap) < m_heap_size) {
put_at(o_in_heap, m_heap[m_heap_size--]);
if (m_priorities[m_heap[o_in_heap]] > priority_of_o) {
@ -72,11 +88,11 @@ template <typename T> void binary_heap_priority_queue<T>::remove(unsigned o) {
}
}
} else {
lean_assert(static_cast<unsigned>(o_in_heap) == m_heap_size);
SASSERT(static_cast<unsigned>(o_in_heap) == m_heap_size);
m_heap_size--;
}
m_heap_inverse[o] = -1;
// lean_assert(is_consistent());
// SASSERT(is_consistent());
}
// n is the initial queue capacity.
// The capacity will be enlarged two times automatically if needed
@ -102,7 +118,7 @@ template <typename T> void binary_heap_priority_queue<T>::put_to_heap(unsigned i
template <typename T> void binary_heap_priority_queue<T>::enqueue_new(unsigned o, const T& priority) {
m_heap_size++;
int i = m_heap_size;
lean_assert(o < m_priorities.size());
SASSERT(o < m_priorities.size());
m_priorities[o] = priority;
put_at(i, o);
while (i > 1 && m_priorities[m_heap[i >> 1]] > priority) {
@ -134,7 +150,7 @@ template <typename T> void binary_heap_priority_queue<T>::change_priority_for_ex
/// return the first element of the queue and removes it from the queue
template <typename T> unsigned binary_heap_priority_queue<T>::dequeue_and_get_priority(T & priority) {
lean_assert(m_heap_size != 0);
SASSERT(m_heap_size != 0);
int ret = m_heap[1];
priority = m_priorities[ret];
put_the_last_at_the_top_and_fix_the_heap();
@ -168,13 +184,13 @@ template <typename T> void binary_heap_priority_queue<T>::put_the_last_at_the_to
}
/// return the first element of the queue and removes it from the queue
template <typename T> unsigned binary_heap_priority_queue<T>::dequeue() {
lean_assert(m_heap_size > 0);
SASSERT(m_heap_size > 0);
int ret = m_heap[1];
put_the_last_at_the_top_and_fix_the_heap();
m_heap_inverse[ret] = -1;
return ret;
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T> void binary_heap_priority_queue<T>::print(std::ostream & out) {
vector<int> index;
vector<T> prs;

29
src/util/lp/binary_heap_priority_queue_instances.cpp
View file

@ -1,10 +1,25 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/numeric_pair.h"
#include "util/lp/binary_heap_priority_queue.hpp"
namespace lean {
namespace lp {
template binary_heap_priority_queue<int>::binary_heap_priority_queue(unsigned int);
template unsigned binary_heap_priority_queue<int>::dequeue();
template void binary_heap_priority_queue<int>::enqueue(unsigned int, int const&);
@ -16,11 +31,11 @@ template unsigned binary_heap_priority_queue<double>::dequeue();
template unsigned binary_heap_priority_queue<mpq>::dequeue();
template void binary_heap_priority_queue<numeric_pair<mpq> >::enqueue(unsigned int, numeric_pair<mpq> const&);
template void binary_heap_priority_queue<numeric_pair<mpq> >::resize(unsigned int);
template void lean::binary_heap_priority_queue<double>::resize(unsigned int);
template void lp::binary_heap_priority_queue<double>::resize(unsigned int);
template binary_heap_priority_queue<unsigned int>::binary_heap_priority_queue(unsigned int);
template void binary_heap_priority_queue<unsigned>::resize(unsigned int);
template unsigned binary_heap_priority_queue<unsigned int>::dequeue();
template void binary_heap_priority_queue<unsigned int>::enqueue(unsigned int, unsigned int const&);
template void binary_heap_priority_queue<unsigned int>::remove(unsigned int);
template void lean::binary_heap_priority_queue<mpq>::resize(unsigned int);
template void lp::binary_heap_priority_queue<mpq>::resize(unsigned int);
}

27
src/util/lp/binary_heap_upair_queue.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include <unordered_set>
@ -15,7 +30,7 @@
typedef std::pair<unsigned, unsigned> upair;
namespace lean {
namespace lp {
template <typename T>
class binary_heap_upair_queue {
binary_heap_priority_queue<T> m_q;
@ -38,7 +53,7 @@ public:
void enqueue(unsigned i, unsigned j, const T & priority);
void dequeue(unsigned & i, unsigned &j);
T get_priority(unsigned i, unsigned j) const;
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
bool pair_to_index_is_a_bijection() const;
bool available_spots_are_correct() const;
bool is_correct() const {

33
src/util/lp/binary_heap_upair_queue.hpp
View file

@ -1,12 +1,27 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <set>
#include "util/lp/lp_utils.h"
#include "util/lp/binary_heap_upair_queue.h"
namespace lean {
namespace lp {
template <typename T> binary_heap_upair_queue<T>::binary_heap_upair_queue(unsigned size) : m_q(size), m_pairs(size) {
for (unsigned i = 0; i < size; i++)
m_available_spots.push_back(i);
@ -14,7 +29,7 @@ template <typename T> binary_heap_upair_queue<T>::binary_heap_upair_queue(unsign
template <typename T> unsigned
binary_heap_upair_queue<T>::dequeue_available_spot() {
lean_assert(m_available_spots.empty() == false);
SASSERT(m_available_spots.empty() == false);
unsigned ret = m_available_spots.back();
m_available_spots.pop_back();
return ret;
@ -54,7 +69,7 @@ template <typename T> void binary_heap_upair_queue<T>::enqueue(unsigned i, unsig
m_pairs.resize(new_size);
}
ij_index = dequeue_available_spot();
// lean_assert(ij_index<m_pairs.size() && ij_index_is_new(ij_index));
// SASSERT(ij_index<m_pairs.size() && ij_index_is_new(ij_index));
m_pairs[ij_index] = p;
m_pairs_to_index[p] = ij_index;
} else {
@ -64,7 +79,7 @@ template <typename T> void binary_heap_upair_queue<T>::enqueue(unsigned i, unsig
}
template <typename T> void binary_heap_upair_queue<T>::dequeue(unsigned & i, unsigned &j) {
lean_assert(!m_q.is_empty());
SASSERT(!m_q.is_empty());
unsigned ij_index = m_q.dequeue();
upair & p = m_pairs[ij_index];
i = p.first;
@ -81,7 +96,7 @@ template <typename T> T binary_heap_upair_queue<T>::get_priority(unsigned i, uns
return m_q.get_priority(it->second);
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T> bool binary_heap_upair_queue<T>::pair_to_index_is_a_bijection() const {
std::set<int> tmp;
for (auto p : m_pairs_to_index) {

25
src/util/lp/binary_heap_upair_queue_instances.cpp
View file

@ -1,9 +1,24 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/binary_heap_upair_queue.hpp"
namespace lean {
namespace lp {
template binary_heap_upair_queue<int>::binary_heap_upair_queue(unsigned int);
template binary_heap_upair_queue<unsigned int>::binary_heap_upair_queue(unsigned int);
template unsigned binary_heap_upair_queue<int>::dequeue_available_spot();

35
src/util/lp/bound_analyzer_on_row.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
#include "util/lp/linear_combination_iterator.h"
@ -13,7 +28,7 @@
// We try to pin a var by pushing the total by using the variable bounds
// In a loop we drive the partial sum down, denoting the variables of this process by _u.
// In the same loop trying to pin variables by pushing the partial sum up, denoting the variable related to it by _l
namespace lean {
namespace lp {
class bound_analyzer_on_row {
@ -91,11 +106,11 @@ public :
}
const impq & ub(unsigned j) const {
lean_assert(upper_bound_is_available(j));
SASSERT(upper_bound_is_available(j));
return m_bp.get_upper_bound(j);
}
const impq & lb(unsigned j) const {
lean_assert(low_bound_is_available(j));
SASSERT(low_bound_is_available(j));
return m_bp.get_low_bound(j);
}
@ -153,7 +168,7 @@ public :
void limit_all_monoids_from_above() {
int strict = 0;
mpq total;
lean_assert(is_zero(total));
SASSERT(is_zero(total));
m_it.reset();
mpq a; unsigned j;
while (m_it.next(a, j)) {
@ -180,7 +195,7 @@ public :
void limit_all_monoids_from_below() {
int strict = 0;
mpq total;
lean_assert(is_zero(total));
SASSERT(is_zero(total));
m_it.reset();
mpq a; unsigned j;
while (m_it.next(a, j)) {
@ -272,7 +287,7 @@ public :
// mpq a; unsigned j;
// while (it->next(a, j)) {
// if (be.m_j == j) continue;
// lean_assert(bound_is_available(j, is_neg(a) ? low_bound : !low_bound));
// SASSERT(bound_is_available(j, is_neg(a) ? low_bound : !low_bound));
// be.m_vector_of_bound_signatures.emplace_back(a, j, numeric_traits<impq>::
// is_neg(a)? low_bound: !low_bound);
// }

25
src/util/lp/breakpoint.h
View file

@ -1,11 +1,26 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
namespace lean {
namespace lp {
enum breakpoint_type {
low_break, upper_break, fixed_break
};

31
src/util/lp/column_info.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
@ -9,7 +24,7 @@
#include <string>
#include <algorithm>
#include "util/lp/lp_settings.h"
namespace lean {
namespace lp {
inline bool is_valid(unsigned j) { return static_cast<int>(j) >= 0;}
template <typename T>
@ -100,11 +115,11 @@ public:
}
T get_low_bound() const {
lean_assert(m_low_bound_is_set);
SASSERT(m_low_bound_is_set);
return m_low_bound;
}
T get_upper_bound() const {
lean_assert(m_upper_bound_is_set);
SASSERT(m_upper_bound_is_set);
return m_upper_bound;
}
@ -156,7 +171,7 @@ public:
}
T get_fixed_value() const {
lean_assert(m_is_fixed);
SASSERT(m_is_fixed);
return m_fixed_value;
}

25
src/util/lp/column_namer.h
View file

@ -1,11 +1,26 @@
#pragma once
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <string>
#include "util/lp/linear_combination_iterator.h"
namespace lean {
namespace lp {
class column_namer {
public:
virtual std::string get_column_name(unsigned j) const = 0;

2
src/util/lp/conversion_helper.h
View file

@ -4,7 +4,7 @@
Author: Lev Nachmanson
*/
#pragma once
namespace lean {
namespace lp {
template <typename V>
struct conversion_helper {
static V get_low_bound(const column_info<mpq> & ci) {

25
src/util/lp/core_solver_pretty_printer.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include <limits>
#include <string>
@ -10,7 +25,7 @@
#include <ostream>
#include "util/lp/lp_settings.h"
#include "util/lp/indexed_vector.h"
namespace lean {
namespace lp {
template <typename T, typename X> class lp_core_solver_base; // forward definition
template <typename T, typename X>

31
src/util/lp/core_solver_pretty_printer.hpp
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <limits>
#include <string>
#include <algorithm>
@ -9,7 +24,7 @@
#include "util/lp/lp_core_solver_base.h"
#include "util/lp/core_solver_pretty_printer.h"
#include "util/lp/numeric_pair.h"
namespace lean {
namespace lp {
template <typename T, typename X>
@ -148,7 +163,7 @@ template <typename T, typename X> void core_solver_pretty_printer<T, X>::adjust_
case column_type::free_column:
break;
default:
lean_assert(false);
SASSERT(false);
break;
}
}
@ -357,7 +372,7 @@ template <typename T, typename X> void core_solver_pretty_printer<T, X>::print_g
unsigned width = m_column_widths[col];
string s = row[col];
int number_of_blanks = width - static_cast<unsigned>(s.size());
lean_assert(number_of_blanks >= 0);
SASSERT(number_of_blanks >= 0);
print_blanks(number_of_blanks, m_out);
m_out << s << ' ';
if (col < row.size() - 1) {
@ -368,7 +383,7 @@ template <typename T, typename X> void core_solver_pretty_printer<T, X>::print_g
string rs = T_to_string(rst);
int nb = m_rs_width - static_cast<int>(rs.size());
lean_assert(nb >= 0);
SASSERT(nb >= 0);
print_blanks(nb + 1, m_out);
m_out << rs << std::endl;
}

41
src/util/lp/core_solver_pretty_printer_instances.cpp
View file

@ -1,15 +1,30 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/numeric_pair.h"
#include "util/lp/core_solver_pretty_printer.hpp"
template lean::core_solver_pretty_printer<double, double>::core_solver_pretty_printer(lean::lp_core_solver_base<double, double> &, std::ostream & out);
template void lean::core_solver_pretty_printer<double, double>::print();
template lean::core_solver_pretty_printer<double, double>::~core_solver_pretty_printer();
template lean::core_solver_pretty_printer<lean::mpq, lean::mpq>::core_solver_pretty_printer(lean::lp_core_solver_base<lean::mpq, lean::mpq> &, std::ostream & out);
template void lean::core_solver_pretty_printer<lean::mpq, lean::mpq>::print();
template lean::core_solver_pretty_printer<lean::mpq, lean::mpq>::~core_solver_pretty_printer();
template lean::core_solver_pretty_printer<lean::mpq, lean::numeric_pair<lean::mpq> >::core_solver_pretty_printer(lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> > &, std::ostream & out);
template lean::core_solver_pretty_printer<lean::mpq, lean::numeric_pair<lean::mpq> >::~core_solver_pretty_printer();
template void lean::core_solver_pretty_printer<lean::mpq, lean::numeric_pair<lean::mpq> >::print();
template lp::core_solver_pretty_printer<double, double>::core_solver_pretty_printer(lp::lp_core_solver_base<double, double> &, std::ostream & out);
template void lp::core_solver_pretty_printer<double, double>::print();
template lp::core_solver_pretty_printer<double, double>::~core_solver_pretty_printer();
template lp::core_solver_pretty_printer<lp::mpq, lp::mpq>::core_solver_pretty_printer(lp::lp_core_solver_base<lp::mpq, lp::mpq> &, std::ostream & out);
template void lp::core_solver_pretty_printer<lp::mpq, lp::mpq>::print();
template lp::core_solver_pretty_printer<lp::mpq, lp::mpq>::~core_solver_pretty_printer();
template lp::core_solver_pretty_printer<lp::mpq, lp::numeric_pair<lp::mpq> >::core_solver_pretty_printer(lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> > &, std::ostream & out);
template lp::core_solver_pretty_printer<lp::mpq, lp::numeric_pair<lp::mpq> >::~core_solver_pretty_printer();
template void lp::core_solver_pretty_printer<lp::mpq, lp::numeric_pair<lp::mpq> >::print();

29
src/util/lp/dense_matrix.h
View file

@ -1,12 +1,27 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
#include "util/vector.h"
#include "util/lp/matrix.h"
namespace lean {
namespace lp {
// used for debugging purposes only
template <typename T, typename X>
class dense_matrix: public matrix<T, X> {
@ -31,7 +46,7 @@ public:
dense_matrix(unsigned m, unsigned n);
dense_matrix operator*=(matrix<T, X> const & a) {
lean_assert(column_count() == a.row_count());
SASSERT(column_count() == a.row_count());
dense_matrix c(row_count(), a.column_count());
for (unsigned i = 0; i < row_count(); i++) {
for (unsigned j = 0; j < a.column_count(); j++) {

29
src/util/lp/dense_matrix.hpp
View file

@ -1,13 +1,28 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/lp_settings.h"
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
#include "util/vector.h"
#include "util/lp/numeric_pair.h"
#include "util/lp/dense_matrix.h"
namespace lean {
namespace lp {
template <typename T> void print_vector(const vector<T> & t, std::ostream & out);
template <typename T, typename X> dense_matrix<T, X>::dense_matrix(unsigned m, unsigned n) : m_m(m), m_n(n), m_values(m * n, numeric_traits<T>::zero()) {
}
@ -170,7 +185,7 @@ template <typename T, typename X> void dense_matrix<T, X>::multiply_row_by_const
template <typename T, typename X>
dense_matrix<T, X> operator* (matrix<T, X> & a, matrix<T, X> & b){
lean_assert(a.column_count() == b.row_count());
SASSERT(a.column_count() == b.row_count());
dense_matrix<T, X> ret(a.row_count(), b.column_count());
for (unsigned i = 0; i < ret.m_m; i++)
for (unsigned j = 0; j< ret.m_n; j++) {

57
src/util/lp/dense_matrix_instances.cpp
View file

@ -1,25 +1,40 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/lp_settings.h"
#include "util/lp/dense_matrix.hpp"
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
#include "util/vector.h"
template lean::dense_matrix<double, double> lean::operator*<double, double>(lean::matrix<double, double>&, lean::matrix<double, double>&);
template void lean::dense_matrix<double, double>::apply_from_left(vector<double> &);
template lean::dense_matrix<double, double>::dense_matrix(lean::matrix<double, double> const*);
template lean::dense_matrix<double, double>::dense_matrix(unsigned int, unsigned int);
template lean::dense_matrix<double, double>& lean::dense_matrix<double, double>::operator=(lean::dense_matrix<double, double> const&);
template lean::dense_matrix<lean::mpq, lean::mpq>::dense_matrix(unsigned int, unsigned int);
template lean::dense_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::dense_matrix(lean::matrix<lean::mpq, lean::numeric_pair<lean::mpq> > const*);
template void lean::dense_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::apply_from_left(vector<lean::mpq>&);
template lean::dense_matrix<lean::mpq, lean::mpq> lean::operator*<lean::mpq, lean::mpq>(lean::matrix<lean::mpq, lean::mpq>&, lean::matrix<lean::mpq, lean::mpq>&);
template lean::dense_matrix<lean::mpq, lean::mpq> & lean::dense_matrix<lean::mpq, lean::mpq>::operator=(lean::dense_matrix<lean::mpq, lean::mpq> const&);
template lean::dense_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::dense_matrix(unsigned int, unsigned int);
template lean::dense_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >& lean::dense_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::operator=(lean::dense_matrix<lean::mpq, lean::numeric_pair<lean::mpq> > const&);
template lean::dense_matrix<lean::mpq, lean::numeric_pair<lean::mpq> > lean::operator*<lean::mpq, lean::numeric_pair<lean::mpq> >(lean::matrix<lean::mpq, lean::numeric_pair<lean::mpq> >&, lean::matrix<lean::mpq, lean::numeric_pair<lean::mpq> >&);
template void lean::dense_matrix<lean::mpq, lean::numeric_pair< lean::mpq> >::apply_from_right( vector< lean::mpq> &);
template void lean::dense_matrix<double,double>::apply_from_right(class vector<double> &);
template void lean::dense_matrix<lean::mpq, lean::mpq>::apply_from_left(vector<lean::mpq>&);
template lp::dense_matrix<double, double> lp::operator*<double, double>(lp::matrix<double, double>&, lp::matrix<double, double>&);
template void lp::dense_matrix<double, double>::apply_from_left(vector<double> &);
template lp::dense_matrix<double, double>::dense_matrix(lp::matrix<double, double> const*);
template lp::dense_matrix<double, double>::dense_matrix(unsigned int, unsigned int);
template lp::dense_matrix<double, double>& lp::dense_matrix<double, double>::operator=(lp::dense_matrix<double, double> const&);
template lp::dense_matrix<lp::mpq, lp::mpq>::dense_matrix(unsigned int, unsigned int);
template lp::dense_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::dense_matrix(lp::matrix<lp::mpq, lp::numeric_pair<lp::mpq> > const*);
template void lp::dense_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::apply_from_left(vector<lp::mpq>&);
template lp::dense_matrix<lp::mpq, lp::mpq> lp::operator*<lp::mpq, lp::mpq>(lp::matrix<lp::mpq, lp::mpq>&, lp::matrix<lp::mpq, lp::mpq>&);
template lp::dense_matrix<lp::mpq, lp::mpq> & lp::dense_matrix<lp::mpq, lp::mpq>::operator=(lp::dense_matrix<lp::mpq, lp::mpq> const&);
template lp::dense_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::dense_matrix(unsigned int, unsigned int);
template lp::dense_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >& lp::dense_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::operator=(lp::dense_matrix<lp::mpq, lp::numeric_pair<lp::mpq> > const&);
template lp::dense_matrix<lp::mpq, lp::numeric_pair<lp::mpq> > lp::operator*<lp::mpq, lp::numeric_pair<lp::mpq> >(lp::matrix<lp::mpq, lp::numeric_pair<lp::mpq> >&, lp::matrix<lp::mpq, lp::numeric_pair<lp::mpq> >&);
template void lp::dense_matrix<lp::mpq, lp::numeric_pair< lp::mpq> >::apply_from_right( vector< lp::mpq> &);
template void lp::dense_matrix<double,double>::apply_from_right(class vector<double> &);
template void lp::dense_matrix<lp::mpq, lp::mpq>::apply_from_left(vector<lp::mpq>&);
#endif

35
src/util/lp/eta_matrix.h
View file

@ -1,32 +1,47 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
#include "util/lp/tail_matrix.h"
#include "util/lp/permutation_matrix.h"
namespace lean {
namespace lp {
// This is the sum of a unit matrix and a one-column matrix
template <typename T, typename X>
class eta_matrix
: public tail_matrix<T, X> {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
unsigned m_length;
#endif
unsigned m_column_index;
public:
sparse_vector<T> m_column_vector;
T m_diagonal_element;
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
eta_matrix(unsigned column_index, unsigned length):
#else
eta_matrix(unsigned column_index):
#endif
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
m_length(length),
#endif
m_column_index(column_index) {}
@ -61,7 +76,7 @@ public:
void push_back(unsigned row_index, T val ) {
lean_assert(row_index != m_column_index);
SASSERT(row_index != m_column_index);
m_column_vector.push_back(row_index, val);
}
@ -69,7 +84,7 @@ public:
void apply_from_right(indexed_vector<T> & w);
T get_elem(unsigned i, unsigned j) const;
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
unsigned row_count() const { return m_length; }
unsigned column_count() const { return m_length; }
void set_number_of_rows(unsigned m) { m_length = m; }

47
src/util/lp/eta_matrix.hpp
View file

@ -1,12 +1,27 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
#include "util/lp/eta_matrix.h"
namespace lean {
namespace lp {
// This is the sum of a unit matrix and a one-column matrix
template <typename T, typename X>
@ -49,7 +64,7 @@ apply_from_left_local(indexed_vector<L> & w, lp_settings & settings) {
}
template <typename T, typename X>
void eta_matrix<T, X>::apply_from_right(vector<T> & w) {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// dense_matrix<T, X> deb(*this);
// auto clone_w = clone_vector<T>(w, get_number_of_rows());
// deb.apply_from_right(clone_w);
@ -59,8 +74,8 @@ void eta_matrix<T, X>::apply_from_right(vector<T> & w) {
t += w[it.first] * it.second;
}
w[m_column_index] = t;
#ifdef LEAN_DEBUG
// lean_assert(vectors_are_equal<T>(clone_w, w, get_number_of_rows()));
#ifdef Z3DEBUG
// SASSERT(vectors_are_equal<T>(clone_w, w, get_number_of_rows()));
// delete clone_w;
#endif
}
@ -68,7 +83,7 @@ template <typename T, typename X>
void eta_matrix<T, X>::apply_from_right(indexed_vector<T> & w) {
if (w.m_index.size() == 0)
return;
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// vector<T> wcopy(w.m_data);
// apply_from_right(wcopy);
#endif
@ -99,12 +114,12 @@ void eta_matrix<T, X>::apply_from_right(indexed_vector<T> & w) {
}
}
#ifdef LEAN_DEBUG
// lean_assert(w.is_OK());
// lean_assert(vectors_are_equal<T>(wcopy, w.m_data));
#ifdef Z3DEBUG
// SASSERT(w.is_OK());
// SASSERT(vectors_are_equal<T>(wcopy, w.m_data));
#endif
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X>
T eta_matrix<T, X>::get_elem(unsigned i, unsigned j) const {
if (j == m_column_index){
@ -120,7 +135,7 @@ T eta_matrix<T, X>::get_elem(unsigned i, unsigned j) const {
template <typename T, typename X>
void eta_matrix<T, X>::conjugate_by_permutation(permutation_matrix<T, X> & p) {
// this = p * this * p(-1)
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// auto rev = p.get_reverse();
// auto deb = ((*this) * rev);
// deb = p * deb;
@ -129,8 +144,8 @@ void eta_matrix<T, X>::conjugate_by_permutation(permutation_matrix<T, X> & p) {
for (auto & pair : m_column_vector.m_data) {
pair.first = p.get_rev(pair.first);
}
#ifdef LEAN_DEBUG
// lean_assert(deb == *this);
#ifdef Z3DEBUG
// SASSERT(deb == *this);
#endif
}
}

61
src/util/lp/eta_matrix_instances.cpp
View file

@ -1,28 +1,43 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <memory>
#include "util/vector.h"
#include "util/lp/numeric_pair.h"
#include "util/lp/eta_matrix.hpp"
#ifdef LEAN_DEBUG
template double lean::eta_matrix<double, double>::get_elem(unsigned int, unsigned int) const;
template lean::mpq lean::eta_matrix<lean::mpq, lean::mpq>::get_elem(unsigned int, unsigned int) const;
template lean::mpq lean::eta_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::get_elem(unsigned int, unsigned int) const;
#ifdef Z3DEBUG
template double lp::eta_matrix<double, double>::get_elem(unsigned int, unsigned int) const;
template lp::mpq lp::eta_matrix<lp::mpq, lp::mpq>::get_elem(unsigned int, unsigned int) const;
template lp::mpq lp::eta_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::get_elem(unsigned int, unsigned int) const;
#endif
template void lean::eta_matrix<double, double>::apply_from_left(vector<double>&, lean::lp_settings&);
template void lean::eta_matrix<double, double>::apply_from_right(vector<double>&);
template void lean::eta_matrix<double, double>::conjugate_by_permutation(lean::permutation_matrix<double, double>&);
template void lean::eta_matrix<lean::mpq, lean::mpq>::apply_from_left(vector<lean::mpq>&, lean::lp_settings&);
template void lean::eta_matrix<lean::mpq, lean::mpq>::apply_from_right(vector<lean::mpq>&);
template void lean::eta_matrix<lean::mpq, lean::mpq>::conjugate_by_permutation(lean::permutation_matrix<lean::mpq, lean::mpq>&);
template void lean::eta_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::apply_from_left(vector<lean::numeric_pair<lean::mpq> >&, lean::lp_settings&);
template void lean::eta_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::apply_from_right(vector<lean::mpq>&);
template void lean::eta_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::conjugate_by_permutation(lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >&);
template void lean::eta_matrix<double, double>::apply_from_left_local<double>(lean::indexed_vector<double>&, lean::lp_settings&);
template void lean::eta_matrix<lean::mpq, lean::mpq>::apply_from_left_local<lean::mpq>(lean::indexed_vector<lean::mpq>&, lean::lp_settings&);
template void lean::eta_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::apply_from_left_local<lean::mpq>(lean::indexed_vector<lean::mpq>&, lean::lp_settings&);
template void lean::eta_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::apply_from_right(lean::indexed_vector<lean::mpq>&);
template void lean::eta_matrix<lean::mpq, lean::mpq>::apply_from_right(lean::indexed_vector<lean::mpq>&);
template void lean::eta_matrix<double, double>::apply_from_right(lean::indexed_vector<double>&);
template void lp::eta_matrix<double, double>::apply_from_left(vector<double>&, lp::lp_settings&);
template void lp::eta_matrix<double, double>::apply_from_right(vector<double>&);
template void lp::eta_matrix<double, double>::conjugate_by_permutation(lp::permutation_matrix<double, double>&);
template void lp::eta_matrix<lp::mpq, lp::mpq>::apply_from_left(vector<lp::mpq>&, lp::lp_settings&);
template void lp::eta_matrix<lp::mpq, lp::mpq>::apply_from_right(vector<lp::mpq>&);
template void lp::eta_matrix<lp::mpq, lp::mpq>::conjugate_by_permutation(lp::permutation_matrix<lp::mpq, lp::mpq>&);
template void lp::eta_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::apply_from_left(vector<lp::numeric_pair<lp::mpq> >&, lp::lp_settings&);
template void lp::eta_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::apply_from_right(vector<lp::mpq>&);
template void lp::eta_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::conjugate_by_permutation(lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >&);
template void lp::eta_matrix<double, double>::apply_from_left_local<double>(lp::indexed_vector<double>&, lp::lp_settings&);
template void lp::eta_matrix<lp::mpq, lp::mpq>::apply_from_left_local<lp::mpq>(lp::indexed_vector<lp::mpq>&, lp::lp_settings&);
template void lp::eta_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::apply_from_left_local<lp::mpq>(lp::indexed_vector<lp::mpq>&, lp::lp_settings&);
template void lp::eta_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::apply_from_right(lp::indexed_vector<lp::mpq>&);
template void lp::eta_matrix<lp::mpq, lp::mpq>::apply_from_right(lp::indexed_vector<lp::mpq>&);
template void lp::eta_matrix<double, double>::apply_from_right(lp::indexed_vector<double>&);

27
src/util/lp/hash_helper.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include <utility>
#include <functional>
@ -12,8 +27,8 @@
#endif
namespace std {
template<>
struct hash<lean::mpq> {
inline size_t operator()(const lean::mpq & v) const {
struct hash<lp::mpq> {
inline size_t operator()(const lp::mpq & v) const {
return v.hash();
}
};

25
src/util/lp/implied_bound.h
View file

@ -1,11 +1,26 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/lp/lp_settings.h"
#include "util/lp/lar_constraints.h"
namespace lean {
namespace lp {
struct implied_bound {
mpq m_bound;
unsigned m_j; // the column for which the bound has been found

27
src/util/lp/indexed_value.h
View file

@ -1,10 +1,25 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
namespace lean {
namespace lp {
template <typename T>
class indexed_value {
public:
@ -41,7 +56,7 @@ public:
m_value = val;
}
};
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename X>
bool check_vector_for_small_values(indexed_vector<X> & w, lp_settings & settings) {
for (unsigned i : w.m_index) {

29
src/util/lp/indexed_vector.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
@ -11,7 +26,7 @@
#include "util/lp/lp_utils.h"
#include "util/lp/lp_settings.h"
#include <unordered_set>
namespace lean {
namespace lp {
template <typename T> void print_vector(const vector<T> & t, std::ostream & out);
template <typename T> void print_vector(const buffer<T> & t, std::ostream & out);
@ -76,7 +91,7 @@ public:
void set_value(const T& value, unsigned index);
void set_value_as_in_dictionary(unsigned index) {
lean_assert(index < m_data.size());
SASSERT(index < m_data.size());
T & loc = m_data[index];
if (is_zero(loc)) {
m_index.push_back(index);
@ -161,7 +176,7 @@ public:
}
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
bool is_OK() const;
void print(std::ostream & out);
#endif

31
src/util/lp/indexed_vector.hpp
View file

@ -1,11 +1,26 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/vector.h"
#include "util/lp/indexed_vector.h"
#include "util/lp/lp_settings.h"
namespace lean {
namespace lp {
template <typename T>
void print_vector(const vector<T> & t, std::ostream & out) {
@ -41,13 +56,13 @@ template <typename T>
void indexed_vector<T>::resize(unsigned data_size) {
clear();
m_data.resize(data_size, numeric_traits<T>::zero());
lean_assert(is_OK());
SASSERT(is_OK());
}
template <typename T>
void indexed_vector<T>::set_value(const T& value, unsigned index) {
m_data[index] = value;
lean_assert(std::find(m_index.begin(), m_index.end(), index) == m_index.end());
SASSERT(std::find(m_index.begin(), m_index.end(), index) == m_index.end());
m_index.push_back(index);
}
@ -70,7 +85,7 @@ void indexed_vector<T>::erase_from_index(unsigned j) {
m_index.erase(it);
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T>
bool indexed_vector<T>::is_OK() const {
return true;

51
src/util/lp/indexed_vector_instances.cpp
View file

@ -1,10 +1,25 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/vector.h"
#include "util/lp/indexed_vector.hpp"
namespace lean {
namespace lp {
template void indexed_vector<double>::clear();
template void indexed_vector<double>::clear_all();
template void indexed_vector<double>::erase_from_index(unsigned int);
@ -17,20 +32,20 @@ template void indexed_vector<mpq>::resize(unsigned int);
template void indexed_vector<unsigned>::resize(unsigned int);
template void indexed_vector<mpq>::set_value(const mpq&, unsigned int);
template void indexed_vector<unsigned>::set_value(const unsigned&, unsigned int);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template bool indexed_vector<double>::is_OK() const;
template bool indexed_vector<mpq>::is_OK() const;
template bool indexed_vector<lean::numeric_pair<mpq> >::is_OK() const;
template void lean::indexed_vector< lean::mpq>::print(std::basic_ostream<char,struct std::char_traits<char> > &);
template void lean::indexed_vector<double>::print(std::basic_ostream<char,struct std::char_traits<char> > &);
template void lean::indexed_vector<lean::numeric_pair<lean::mpq> >::print(std::ostream&);
template bool indexed_vector<lp::numeric_pair<mpq> >::is_OK() const;
template void lp::indexed_vector< lp::mpq>::print(std::basic_ostream<char,struct std::char_traits<char> > &);
template void lp::indexed_vector<double>::print(std::basic_ostream<char,struct std::char_traits<char> > &);
template void lp::indexed_vector<lp::numeric_pair<lp::mpq> >::print(std::ostream&);
#endif
}
template void lean::print_vector<double>(vector<double> const&, std::ostream&);
template void lean::print_vector<unsigned int>(vector<unsigned int> const&, std::ostream&);
template void lean::print_vector<std::string>(vector<std::string> const&, std::ostream&);
template void lean::print_vector<lean::numeric_pair<lean::mpq> >(vector<lean::numeric_pair<lean::mpq>> const&, std::ostream&);
template void lean::indexed_vector<double>::resize(unsigned int);
template void lean::print_vector< lean::mpq>(vector< lean::mpq> const &, std::basic_ostream<char, std::char_traits<char> > &);
template void lean::print_vector<std::pair<lean::mpq, unsigned int> >(vector<std::pair<lean::mpq, unsigned int>> const&, std::ostream&);
template void lean::indexed_vector<lean::numeric_pair<lean::mpq> >::erase_from_index(unsigned int);
template void lp::print_vector<double>(vector<double> const&, std::ostream&);
template void lp::print_vector<unsigned int>(vector<unsigned int> const&, std::ostream&);
template void lp::print_vector<std::string>(vector<std::string> const&, std::ostream&);
template void lp::print_vector<lp::numeric_pair<lp::mpq> >(vector<lp::numeric_pair<lp::mpq>> const&, std::ostream&);
template void lp::indexed_vector<double>::resize(unsigned int);
template void lp::print_vector< lp::mpq>(vector< lp::mpq> const &, std::basic_ostream<char, std::char_traits<char> > &);
template void lp::print_vector<std::pair<lp::mpq, unsigned int> >(vector<std::pair<lp::mpq, unsigned int>> const&, std::ostream&);
template void lp::indexed_vector<lp::numeric_pair<lp::mpq> >::erase_from_index(unsigned int);

109
src/util/lp/init_lar_solver.h
View file

@ -1,9 +1,24 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
// here we are inside lean::lar_solver class
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
// here we are inside lp::lar_solver class
bool strategy_is_undecided() const {
return m_settings.simplex_strategy() == simplex_strategy_enum::undecided;
@ -11,7 +26,7 @@ bool strategy_is_undecided() const {
var_index add_var(unsigned ext_j) {
var_index i;
lean_assert (ext_j < m_terms_start_index);
SASSERT (ext_j < m_terms_start_index);
if (ext_j >= m_terms_start_index)
throw 0; // todo : what is the right way to exit?
@ -19,19 +34,19 @@ var_index add_var(unsigned ext_j) {
if (try_get_val(m_ext_vars_to_columns, ext_j, i)) {
return i;
}
lean_assert(m_vars_to_ul_pairs.size() == A_r().column_count());
SASSERT(m_vars_to_ul_pairs.size() == A_r().column_count());
i = A_r().column_count();
m_vars_to_ul_pairs.push_back (ul_pair(static_cast<unsigned>(-1)));
add_non_basic_var_to_core_fields(ext_j);
lean_assert(sizes_are_correct());
SASSERT(sizes_are_correct());
return i;
}
void register_new_ext_var_index(unsigned ext_v) {
lean_assert(!contains(m_ext_vars_to_columns, ext_v));
SASSERT(!contains(m_ext_vars_to_columns, ext_v));
unsigned j = static_cast<unsigned>(m_ext_vars_to_columns.size());
m_ext_vars_to_columns[ext_v] = j;
lean_assert(m_columns_to_ext_vars_or_term_indices.size() == j);
SASSERT(m_columns_to_ext_vars_or_term_indices.size() == j);
m_columns_to_ext_vars_or_term_indices.push_back(ext_v);
}
@ -47,12 +62,12 @@ void add_non_basic_var_to_core_fields(unsigned ext_j) {
void add_new_var_to_core_fields_for_doubles(bool register_in_basis) {
unsigned j = A_d().column_count();
A_d().add_column();
lean_assert(m_mpq_lar_core_solver.m_d_x.size() == j);
// lean_assert(m_mpq_lar_core_solver.m_d_low_bounds.size() == j && m_mpq_lar_core_solver.m_d_upper_bounds.size() == j); // restore later
SASSERT(m_mpq_lar_core_solver.m_d_x.size() == j);
// SASSERT(m_mpq_lar_core_solver.m_d_low_bounds.size() == j && m_mpq_lar_core_solver.m_d_upper_bounds.size() == j); // restore later
m_mpq_lar_core_solver.m_d_x.resize(j + 1 );
m_mpq_lar_core_solver.m_d_low_bounds.resize(j + 1);
m_mpq_lar_core_solver.m_d_upper_bounds.resize(j + 1);
lean_assert(m_mpq_lar_core_solver.m_d_heading.size() == j); // as A().column_count() on the entry to the method
SASSERT(m_mpq_lar_core_solver.m_d_heading.size() == j); // as A().column_count() on the entry to the method
if (register_in_basis) {
A_d().add_row();
m_mpq_lar_core_solver.m_d_heading.push_back(m_mpq_lar_core_solver.m_d_basis.size());
@ -66,15 +81,15 @@ void add_new_var_to_core_fields_for_doubles(bool register_in_basis) {
void add_new_var_to_core_fields_for_mpq(bool register_in_basis) {
unsigned j = A_r().column_count();
A_r().add_column();
lean_assert(m_mpq_lar_core_solver.m_r_x.size() == j);
// lean_assert(m_mpq_lar_core_solver.m_r_low_bounds.size() == j && m_mpq_lar_core_solver.m_r_upper_bounds.size() == j); // restore later
SASSERT(m_mpq_lar_core_solver.m_r_x.size() == j);
// SASSERT(m_mpq_lar_core_solver.m_r_low_bounds.size() == j && m_mpq_lar_core_solver.m_r_upper_bounds.size() == j); // restore later
m_mpq_lar_core_solver.m_r_x.resize(j + 1);
m_mpq_lar_core_solver.m_r_low_bounds.increase_size_by_one();
m_mpq_lar_core_solver.m_r_upper_bounds.increase_size_by_one();
m_mpq_lar_core_solver.m_r_solver.m_inf_set.increase_size_by_one();
m_mpq_lar_core_solver.m_r_solver.m_costs.resize(j + 1);
m_mpq_lar_core_solver.m_r_solver.m_d.resize(j + 1);
lean_assert(m_mpq_lar_core_solver.m_r_heading.size() == j); // as A().column_count() on the entry to the method
SASSERT(m_mpq_lar_core_solver.m_r_heading.size() == j); // as A().column_count() on the entry to the method
if (register_in_basis) {
A_r().add_row();
m_mpq_lar_core_solver.m_r_heading.push_back(m_mpq_lar_core_solver.m_r_basis.size());
@ -110,14 +125,14 @@ var_index add_term(const vector<std::pair<mpq, var_index>> & coeffs,
if (m_settings.bound_propagation())
m_rows_with_changed_bounds.insert(A_r().row_count() - 1);
}
lean_assert(m_ext_vars_to_columns.size() == A_r().column_count());
SASSERT(m_ext_vars_to_columns.size() == A_r().column_count());
return ret;
}
void add_row_for_term(const lar_term * term, unsigned term_ext_index) {
lean_assert(sizes_are_correct());
SASSERT(sizes_are_correct());
add_row_from_term_no_constraint(term, term_ext_index);
lean_assert(sizes_are_correct());
SASSERT(sizes_are_correct());
}
void add_row_from_term_no_constraint(const lar_term * term, unsigned term_ext_index) {
@ -142,7 +157,7 @@ void add_row_from_term_no_constraint(const lar_term * term, unsigned term_ext_in
void add_basic_var_to_core_fields() {
bool use_lu = m_mpq_lar_core_solver.need_to_presolve_with_double_solver();
lean_assert(!use_lu || A_r().column_count() == A_d().column_count());
SASSERT(!use_lu || A_r().column_count() == A_d().column_count());
m_mpq_lar_core_solver.m_column_types.push_back(column_type::free_column);
m_columns_with_changed_bound.increase_size_by_one();
m_rows_with_changed_bounds.increase_size_by_one();
@ -160,7 +175,7 @@ constraint_index add_var_bound(var_index j, lconstraint_kind kind, const mpq & r
} else {
add_var_bound_on_constraint_for_term(j, kind, right_side, ci);
}
lean_assert(sizes_are_correct());
SASSERT(sizes_are_correct());
return ci;
}
@ -182,12 +197,12 @@ void update_column_type_and_bound(var_index j, lconstraint_kind kind, const mpq
update_fixed_column_type_and_bound(j, kind, right_side, constr_index);
break;
default:
lean_assert(false); // cannot be here
SASSERT(false); // cannot be here
}
}
void add_var_bound_on_constraint_for_term(var_index j, lconstraint_kind kind, const mpq & right_side, constraint_index ci) {
lean_assert(is_term(j));
SASSERT(is_term(j));
unsigned adjusted_term_index = adjust_term_index(j);
unsigned term_j;
if (try_get_val(m_ext_vars_to_columns, j, term_j)) {
@ -208,11 +223,11 @@ void add_constraint_from_term_and_create_new_column_row(unsigned term_j, const l
unsigned j = A_r().column_count() - 1;
update_column_type_and_bound(j, kind, right_side - term->m_v, m_constraints.size());
m_constraints.push_back(new lar_term_constraint(term, kind, right_side));
lean_assert(A_r().column_count() == m_mpq_lar_core_solver.m_r_solver.m_costs.size());
SASSERT(A_r().column_count() == m_mpq_lar_core_solver.m_r_solver.m_costs.size());
}
void decide_on_strategy_and_adjust_initial_state() {
lean_assert(strategy_is_undecided());
SASSERT(strategy_is_undecided());
if (m_vars_to_ul_pairs.size() > m_settings.column_number_threshold_for_using_lu_in_lar_solver) {
m_settings.simplex_strategy() = simplex_strategy_enum::lu;
} else {
@ -230,7 +245,7 @@ void adjust_initial_state() {
adjust_initial_state_for_tableau_rows();
break;
case simplex_strategy_enum::tableau_costs:
lean_assert(false); // not implemented
SASSERT(false); // not implemented
case simplex_strategy_enum::undecided:
adjust_initial_state_for_tableau_rows();
break;
@ -249,12 +264,12 @@ void adjust_initial_state_for_lu() {
/*
unsigned j = A_d().column_count();
A_d().add_column();
lean_assert(m_mpq_lar_core_solver.m_d_x.size() == j);
// lean_assert(m_mpq_lar_core_solver.m_d_low_bounds.size() == j && m_mpq_lar_core_solver.m_d_upper_bounds.size() == j); // restore later
SASSERT(m_mpq_lar_core_solver.m_d_x.size() == j);
// SASSERT(m_mpq_lar_core_solver.m_d_low_bounds.size() == j && m_mpq_lar_core_solver.m_d_upper_bounds.size() == j); // restore later
m_mpq_lar_core_solver.m_d_x.resize(j + 1 );
m_mpq_lar_core_solver.m_d_low_bounds.resize(j + 1);
m_mpq_lar_core_solver.m_d_upper_bounds.resize(j + 1);
lean_assert(m_mpq_lar_core_solver.m_d_heading.size() == j); // as A().column_count() on the entry to the method
SASSERT(m_mpq_lar_core_solver.m_d_heading.size() == j); // as A().column_count() on the entry to the method
if (register_in_basis) {
A_d().add_row();
m_mpq_lar_core_solver.m_d_heading.push_back(m_mpq_lar_core_solver.m_d_basis.size());
@ -275,13 +290,13 @@ void adjust_initial_state_for_tableau_rows() {
// this fills the last row of A_d and sets the basis column: -1 in the last column of the row
void fill_last_row_of_A_d(static_matrix<double, double> & A, const lar_term* ls) {
lean_assert(A.row_count() > 0);
lean_assert(A.column_count() > 0);
SASSERT(A.row_count() > 0);
SASSERT(A.column_count() > 0);
unsigned last_row = A.row_count() - 1;
lean_assert(A.m_rows[last_row].empty());
SASSERT(A.m_rows[last_row].empty());
for (auto & t : ls->m_coeffs) {
lean_assert(!is_zero(t.second));
SASSERT(!is_zero(t.second));
var_index j = t.first;
A.set(last_row, j, - t.second.get_double());
}
@ -297,8 +312,8 @@ void update_free_column_type_and_bound(var_index j, lconstraint_kind kind, const
y_of_bound = -1;
case LE:
m_mpq_lar_core_solver.m_column_types[j] = column_type::upper_bound;
lean_assert(m_mpq_lar_core_solver.m_column_types()[j] == column_type::upper_bound);
lean_assert(m_mpq_lar_core_solver.m_r_upper_bounds.size() > j);
SASSERT(m_mpq_lar_core_solver.m_column_types()[j] == column_type::upper_bound);
SASSERT(m_mpq_lar_core_solver.m_r_upper_bounds.size() > j);
{
auto up = numeric_pair<mpq>(right_side, y_of_bound);
m_mpq_lar_core_solver.m_r_upper_bounds[j] = up;
@ -309,7 +324,7 @@ void update_free_column_type_and_bound(var_index j, lconstraint_kind kind, const
y_of_bound = 1;
case GE:
m_mpq_lar_core_solver.m_column_types[j] = column_type::low_bound;
lean_assert(m_mpq_lar_core_solver.m_r_upper_bounds.size() > j);
SASSERT(m_mpq_lar_core_solver.m_r_upper_bounds.size() > j);
{
auto low = numeric_pair<mpq>(right_side, y_of_bound);
m_mpq_lar_core_solver.m_r_low_bounds[j] = low;
@ -324,14 +339,14 @@ void update_free_column_type_and_bound(var_index j, lconstraint_kind kind, const
break;
default:
lean_unreachable();
SASSERT(false);
}
m_columns_with_changed_bound.insert(j);
}
void update_upper_bound_column_type_and_bound(var_index j, lconstraint_kind kind, const mpq & right_side, constraint_index ci) {
lean_assert(m_mpq_lar_core_solver.m_column_types()[j] == column_type::upper_bound);
SASSERT(m_mpq_lar_core_solver.m_column_types()[j] == column_type::upper_bound);
mpq y_of_bound(0);
switch (kind) {
case LT:
@ -382,13 +397,13 @@ void update_upper_bound_column_type_and_bound(var_index j, lconstraint_kind kind
break;
default:
lean_unreachable();
SASSERT(false);
}
}
void update_boxed_column_type_and_bound(var_index j, lconstraint_kind kind, const mpq & right_side, constraint_index ci) {
lean_assert(m_status == INFEASIBLE || (m_mpq_lar_core_solver.m_column_types()[j] == column_type::boxed && m_mpq_lar_core_solver.m_r_low_bounds()[j] < m_mpq_lar_core_solver.m_r_upper_bounds()[j]));
SASSERT(m_status == INFEASIBLE || (m_mpq_lar_core_solver.m_column_types()[j] == column_type::boxed && m_mpq_lar_core_solver.m_r_low_bounds()[j] < m_mpq_lar_core_solver.m_r_upper_bounds()[j]));
mpq y_of_bound(0);
switch (kind) {
case LT:
@ -404,7 +419,7 @@ void update_boxed_column_type_and_bound(var_index j, lconstraint_kind kind, cons
if (up < m_mpq_lar_core_solver.m_r_low_bounds[j]) {
m_status = INFEASIBLE;
lean_assert(false);
SASSERT(false);
m_infeasible_column_index = j;
} else {
if (m_mpq_lar_core_solver.m_r_low_bounds()[j] == m_mpq_lar_core_solver.m_r_upper_bounds()[j])
@ -453,12 +468,12 @@ void update_boxed_column_type_and_bound(var_index j, lconstraint_kind kind, cons
}
default:
lean_unreachable();
SASSERT(false);
}
}
void update_low_bound_column_type_and_bound(var_index j, lconstraint_kind kind, const mpq & right_side, constraint_index ci) {
lean_assert(m_mpq_lar_core_solver.m_column_types()[j] == column_type::low_bound);
SASSERT(m_mpq_lar_core_solver.m_column_types()[j] == column_type::low_bound);
mpq y_of_bound(0);
switch (kind) {
case LT:
@ -508,14 +523,14 @@ void update_low_bound_column_type_and_bound(var_index j, lconstraint_kind kind,
}
default:
lean_unreachable();
SASSERT(false);
}
}
void update_fixed_column_type_and_bound(var_index j, lconstraint_kind kind, const mpq & right_side, constraint_index ci) {
lean_assert(m_status == INFEASIBLE || (m_mpq_lar_core_solver.m_column_types()[j] == column_type::fixed && m_mpq_lar_core_solver.m_r_low_bounds()[j] == m_mpq_lar_core_solver.m_r_upper_bounds()[j]));
lean_assert(m_status == INFEASIBLE || (m_mpq_lar_core_solver.m_r_low_bounds()[j].y.is_zero() && m_mpq_lar_core_solver.m_r_upper_bounds()[j].y.is_zero()));
SASSERT(m_status == INFEASIBLE || (m_mpq_lar_core_solver.m_column_types()[j] == column_type::fixed && m_mpq_lar_core_solver.m_r_low_bounds()[j] == m_mpq_lar_core_solver.m_r_upper_bounds()[j]));
SASSERT(m_status == INFEASIBLE || (m_mpq_lar_core_solver.m_r_low_bounds()[j].y.is_zero() && m_mpq_lar_core_solver.m_r_upper_bounds()[j].y.is_zero()));
auto v = numeric_pair<mpq>(right_side, mpq(0));
mpq y_of_bound(0);
@ -569,7 +584,7 @@ void update_fixed_column_type_and_bound(var_index j, lconstraint_kind kind, cons
}
default:
lean_unreachable();
SASSERT(false);
}
}

27
src/util/lp/int_set.h
View file

@ -1,12 +1,27 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
#include "util/lp/indexed_vector.h"
#include <ostream>
namespace lean {
namespace lp {
// serves at a set of non-negative integers smaller than the set size
class int_set {
vector<int> m_data;
@ -20,7 +35,7 @@ public:
return m_data[j] >= 0;
}
void insert(unsigned j) {
lean_assert(j < m_data.size());
SASSERT(j < m_data.size());
if (contains(j)) return;
m_data[j] = m_index.size();
m_index.push_back(j);

25
src/util/lp/iterator_on_column.h
View file

@ -1,12 +1,27 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/lp/linear_combination_iterator.h"
#include "util/lp/static_matrix.h"
#include "util/lp/lar_term.h"
namespace lean {
namespace lp {
template <typename T, typename X>
struct iterator_on_column:linear_combination_iterator<T> {
const vector<column_cell>& m_column; // the offset in term coeffs

25
src/util/lp/iterator_on_indexed_vector.h
View file

@ -1,10 +1,25 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/lp/linear_combination_iterator.h"
namespace lean {
namespace lp {
template <typename T>
struct iterator_on_indexed_vector:linear_combination_iterator<T> {
const indexed_vector<T> & m_v;

25
src/util/lp/iterator_on_pivot_row.h
View file

@ -1,10 +1,25 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/lp/iterator_on_indexed_vector.h"
namespace lean {
namespace lp {
template <typename T>
struct iterator_on_pivot_row:linear_combination_iterator<T> {
bool m_basis_returned;

25
src/util/lp/iterator_on_row.h
View file

@ -1,10 +1,25 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/lp/linear_combination_iterator.h"
namespace lean {
namespace lp {
template <typename T>
struct iterator_on_row:linear_combination_iterator<T> {
const vector<row_cell<T>> & m_row;

25
src/util/lp/iterator_on_term_with_basis_var.h
View file

@ -1,12 +1,27 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/lp/linear_combination_iterator.h"
#include "util/lp/numeric_pair.h"
#include "util/lp/lar_term.h"
namespace lean {
namespace lp {
struct iterator_on_term_with_basis_var:linear_combination_iterator<mpq> {
const lar_term & m_term;
std::unordered_map<unsigned, mpq>::const_iterator m_i; // the offset in term coeffs

29
src/util/lp/lar_constraints.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
@ -12,7 +27,7 @@
#include "util/lp/lp_utils.h"
#include "util/lp/ul_pair.h"
#include "util/lp/lar_term.h"
namespace lean {
namespace lp {
inline lconstraint_kind flip_kind(lconstraint_kind t) {
return static_cast<lconstraint_kind>( - static_cast<int>(t));
}
@ -25,7 +40,7 @@ inline std::string lconstraint_kind_string(lconstraint_kind t) {
case GT: return std::string(">");
case EQ: return std::string("=");
}
lean_unreachable();
SASSERT(false);
return std::string(); // it is unreachable
}
@ -74,7 +89,7 @@ public:
: lar_base_constraint(kind, right_side), m_coeffs(left_side) {}
lar_constraint(const lar_base_constraint & c) {
lean_assert(false); // should not be called : todo!
SASSERT(false); // should not be called : todo!
}
unsigned size() const {

113
src/util/lp/lar_core_solver.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
#include <string>
@ -18,7 +33,7 @@
#include "util/lp/iterator_on_column.h"
#include "util/lp/iterator_on_indexed_vector.h"
#include "util/lp/stacked_value.h"
namespace lean {
namespace lp {
class lar_core_solver {
// m_sign_of_entering is set to 1 if the entering variable needs
@ -168,9 +183,9 @@ public:
}
void push() {
lean_assert(m_r_solver.basis_heading_is_correct());
lean_assert(!need_to_presolve_with_double_solver() || m_d_solver.basis_heading_is_correct());
lean_assert(m_column_types.size() == m_r_A.column_count());
SASSERT(m_r_solver.basis_heading_is_correct());
SASSERT(!need_to_presolve_with_double_solver() || m_d_solver.basis_heading_is_correct());
SASSERT(m_column_types.size() == m_r_A.column_count());
m_stacked_simplex_strategy = settings().simplex_strategy();
m_stacked_simplex_strategy.push();
m_column_types.push();
@ -192,7 +207,7 @@ public:
template <typename K>
void push_vector(stacked_vector<K> & pushed_vector, const vector<K> & vector) {
lean_assert(pushed_vector.size() <= vector.size());
SASSERT(pushed_vector.size() <= vector.size());
for (unsigned i = 0; i < vector.size();i++) {
if (i == pushed_vector.size()) {
pushed_vector.push_back(vector[i]);
@ -242,8 +257,8 @@ public:
pop_basis(k);
m_stacked_simplex_strategy.pop(k);
settings().simplex_strategy() = m_stacked_simplex_strategy;
lean_assert(m_r_solver.basis_heading_is_correct());
lean_assert(!need_to_presolve_with_double_solver() || m_d_solver.basis_heading_is_correct());
SASSERT(m_r_solver.basis_heading_is_correct());
SASSERT(!need_to_presolve_with_double_solver() || m_d_solver.basis_heading_is_correct());
}
bool need_to_presolve_with_double_solver() const {
@ -304,11 +319,11 @@ public:
break;
default:
lean_assert(false);
SASSERT(false);
}
break;
default:
lean_unreachable();
SASSERT(false);
}
m_r_solver.remove_column_from_inf_set(j);
return true;
@ -317,7 +332,7 @@ public:
void prepare_solver_x_with_signature_tableau(const lar_solution_signature & signature) {
lean_assert(m_r_solver.inf_set_is_correct());
SASSERT(m_r_solver.inf_set_is_correct());
for (auto &t : signature) {
unsigned j = t.first;
if (m_r_heading[j] >= 0)
@ -332,9 +347,9 @@ public:
m_r_solver.m_x[jb] -= delta * m_r_solver.m_A.get_val(cc);
m_r_solver.update_column_in_inf_set(jb);
}
lean_assert(m_r_solver.A_mult_x_is_off() == false);
SASSERT(m_r_solver.A_mult_x_is_off() == false);
}
lean_assert(m_r_solver.inf_set_is_correct());
SASSERT(m_r_solver.inf_set_is_correct());
}
@ -342,7 +357,7 @@ public:
void prepare_solver_x_with_signature(const lar_solution_signature & signature, lp_primal_core_solver<L,K> & s) {
for (auto &t : signature) {
unsigned j = t.first;
lean_assert(m_r_heading[j] < 0);
SASSERT(m_r_heading[j] < 0);
auto pos_type = t.second;
switch (pos_type) {
case at_low_bound:
@ -359,7 +374,7 @@ public:
case not_at_bound:
switch (m_column_types[j]) {
case column_type::free_column:
lean_assert(false); // unreachable
SASSERT(false); // unreachable
case column_type::upper_bound:
s.m_x[j] = s.m_upper_bounds[j];
break;
@ -377,15 +392,15 @@ public:
s.m_x[j] = s.m_low_bounds[j];
break;
default:
lean_assert(false);
SASSERT(false);
}
break;
default:
lean_unreachable();
SASSERT(false);
}
}
lean_assert(is_zero_vector(s.m_b));
SASSERT(is_zero_vector(s.m_b));
s.solve_Ax_eq_b();
}
@ -418,7 +433,7 @@ public:
// the queues of delayed indices
std::queue<unsigned> entr_q, leav_q;
auto * l = cs.m_factorization;
lean_assert(l->get_status() == LU_status::OK);
SASSERT(l->get_status() == LU_status::OK);
for (unsigned i = 0; i < trace_of_basis_change.size(); i+= 2) {
unsigned entering = trace_of_basis_change[i];
unsigned leaving = trace_of_basis_change[i+1];
@ -446,8 +461,8 @@ public:
continue;
}
}
lean_assert(cs.m_basis_heading[entering] < 0);
lean_assert(cs.m_basis_heading[leaving] >= 0);
SASSERT(cs.m_basis_heading[entering] < 0);
SASSERT(cs.m_basis_heading[leaving] >= 0);
if (l->get_status() == LU_status::OK) {
l->prepare_entering(entering, w); // to init vector w
l->replace_column(zero_of_type<L>(), w, cs.m_basis_heading[leaving]);
@ -471,7 +486,7 @@ public:
void solve_on_signature_tableau(const lar_solution_signature & signature, const vector<unsigned> & changes_of_basis) {
r_basis_is_OK();
lean_assert(settings().use_tableau());
SASSERT(settings().use_tableau());
bool r = catch_up_in_lu_tableau(changes_of_basis, m_d_solver.m_basis_heading);
if (!r) { // it is the case where m_d_solver gives a degenerated basis
@ -490,10 +505,10 @@ public:
return;
m_r_solver.stop_tracing_basis_changes();
// and now catch up in the double solver
lean_assert(m_r_solver.total_iterations() >= m_r_solver.m_trace_of_basis_change_vector.size() /2);
SASSERT(m_r_solver.total_iterations() >= m_r_solver.m_trace_of_basis_change_vector.size() /2);
catch_up_in_lu(m_r_solver.m_trace_of_basis_change_vector, m_r_solver.m_basis_heading, m_d_solver);
}
lean_assert(r_basis_is_OK());
SASSERT(r_basis_is_OK());
}
bool adjust_x_of_column(unsigned j) {
@ -507,16 +522,16 @@ public:
}
m_r_solver.snap_column_to_bound_tableau(j);
lean_assert(m_r_solver.column_is_feasible(j));
SASSERT(m_r_solver.column_is_feasible(j));
m_r_solver.m_inf_set.erase(j);
*/
lean_assert(false);
SASSERT(false);
return true;
}
bool catch_up_in_lu_tableau(const vector<unsigned> & trace_of_basis_change, const vector<int> & basis_heading) {
lean_assert(r_basis_is_OK());
SASSERT(r_basis_is_OK());
// the queues of delayed indices
std::queue<unsigned> entr_q, leav_q;
for (unsigned i = 0; i < trace_of_basis_change.size(); i+= 2) {
@ -546,47 +561,47 @@ public:
continue;
}
}
lean_assert(m_r_solver.m_basis_heading[entering] < 0);
lean_assert(m_r_solver.m_basis_heading[leaving] >= 0);
SASSERT(m_r_solver.m_basis_heading[entering] < 0);
SASSERT(m_r_solver.m_basis_heading[leaving] >= 0);
m_r_solver.change_basis_unconditionally(entering, leaving);
if(!m_r_solver.pivot_column_tableau(entering, m_r_solver.m_basis_heading[entering])) {
// unroll the last step
m_r_solver.change_basis_unconditionally(leaving, entering);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
bool t =
#endif
m_r_solver.pivot_column_tableau(leaving, m_r_solver.m_basis_heading[leaving]);
#ifdef LEAN_DEBUG
lean_assert(t);
#ifdef Z3DEBUG
SASSERT(t);
#endif
return false;
}
}
lean_assert(r_basis_is_OK());
SASSERT(r_basis_is_OK());
return true;
}
bool r_basis_is_OK() const {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
if (!m_r_solver.m_settings.use_tableau())
return true;
for (unsigned j : m_r_solver.m_basis) {
lean_assert(m_r_solver.m_A.m_columns[j].size() == 1);
lean_assert(m_r_solver.m_A.get_val(m_r_solver.m_A.m_columns[j][0]) == one_of_type<mpq>());
SASSERT(m_r_solver.m_A.m_columns[j].size() == 1);
SASSERT(m_r_solver.m_A.get_val(m_r_solver.m_A.m_columns[j][0]) == one_of_type<mpq>());
}
for (unsigned j =0; j < m_r_solver.m_basis_heading.size(); j++) {
if (m_r_solver.m_basis_heading[j] >= 0) continue;
if (m_r_solver.m_column_types[j] == column_type::fixed) continue;
lean_assert(static_cast<unsigned>(- m_r_solver.m_basis_heading[j] - 1) < m_r_solver.m_column_types.size());
lean_assert( m_r_solver.m_basis_heading[j] <= -1);
SASSERT(static_cast<unsigned>(- m_r_solver.m_basis_heading[j] - 1) < m_r_solver.m_column_types.size());
SASSERT( m_r_solver.m_basis_heading[j] <= -1);
}
#endif
return true;
}
void solve_on_signature(const lar_solution_signature & signature, const vector<unsigned> & changes_of_basis) {
lean_assert(!settings().use_tableau());
SASSERT(!settings().use_tableau());
if (m_r_solver.m_factorization == nullptr) {
for (unsigned j = 0; j < changes_of_basis.size(); j+=2) {
unsigned entering = changes_of_basis[j];
@ -615,7 +630,7 @@ public:
return;
m_r_solver.stop_tracing_basis_changes();
// and now catch up in the double solver
lean_assert(m_r_solver.total_iterations() >= m_r_solver.m_trace_of_basis_change_vector.size() /2);
SASSERT(m_r_solver.total_iterations() >= m_r_solver.m_trace_of_basis_change_vector.size() /2);
catch_up_in_lu(m_r_solver.m_trace_of_basis_change_vector, m_r_solver.m_basis_heading, m_d_solver);
}
}
@ -641,7 +656,7 @@ public:
template <typename L, typename K>
void extract_signature_from_lp_core_solver(const lp_primal_core_solver<L, K> & solver, lar_solution_signature & signature) {
signature.clear();
lean_assert(signature.size() == 0);
SASSERT(signature.size() == 0);
for (unsigned j = 0; j < solver.m_basis_heading.size(); j++) {
if (solver.m_basis_heading[j] < 0) {
signature[j] = solver.get_non_basic_column_value_position(j);
@ -664,7 +679,7 @@ public:
if (upper_bound_is_set(j)) {
const auto & ub = m_r_solver.m_upper_bounds[j];
m_d_upper_bounds[j] = ub.x.get_double() + delta * ub.y.get_double();
lean_assert(!low_bound_is_set(j) || (m_d_upper_bounds[j] >= m_d_low_bounds[j]));
SASSERT(!low_bound_is_set(j) || (m_d_upper_bounds[j] >= m_d_low_bounds[j]));
}
}
}
@ -729,7 +744,7 @@ public:
case column_type::fixed:
return true;
default:
lean_assert(false);
SASSERT(false);
}
return false;
}
@ -744,20 +759,20 @@ public:
case column_type::fixed:
return true;
default:
lean_assert(false);
SASSERT(false);
}
return false;
}
void update_delta(mpq& delta, numeric_pair<mpq> const& l, numeric_pair<mpq> const& u) const {
lean_assert(l <= u);
SASSERT(l <= u);
if (l.x < u.x && l.y > u.y) {
mpq delta1 = (u.x - l.x) / (l.y - u.y);
if (delta1 < delta) {
delta = delta1;
}
}
lean_assert(l.x + delta * l.y <= u.x + delta * u.y);
SASSERT(l.x + delta * l.y <= u.x + delta * u.y);
}

86
src/util/lp/lar_core_solver.hpp
View file

@ -1,16 +1,46 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <string>
#include "util/vector.h"
#include "util/lp/lar_core_solver.h"
#include "util/lp/lar_solution_signature.h"
namespace lean {
namespace lp {
lar_core_solver::lar_core_solver(
lp_settings & settings,
const column_namer & column_names
@ -42,9 +72,9 @@ lar_core_solver::lar_core_solver(
column_names){}
void lar_core_solver::init_costs(bool first_time) {
lean_assert(false); // should not be called
// lean_assert(this->m_x.size() >= this->m_n());
// lean_assert(this->m_column_types.size() >= this->m_n());
SASSERT(false); // should not be called
// SASSERT(this->m_x.size() >= this->m_n());
// SASSERT(this->m_column_types.size() >= this->m_n());
// if (first_time)
// this->m_costs.resize(this->m_n());
// X inf = this->m_infeasibility;
@ -54,7 +84,7 @@ void lar_core_solver::init_costs(bool first_time) {
// if (!(first_time || inf >= this->m_infeasibility)) {
// LP_OUT(this->m_settings, "iter = " << this->total_iterations() << std::endl);
// LP_OUT(this->m_settings, "inf was " << T_to_string(inf) << " and now " << T_to_string(this->m_infeasibility) << std::endl);
// lean_assert(false);
// SASSERT(false);
// }
// if (inf == this->m_infeasibility)
// this->m_iters_with_no_cost_growing++;
@ -105,7 +135,7 @@ void lar_core_solver::init_cost_for_column(unsigned j) {
this->m_costs[j] = numeric_traits<T>::zero();
break;
default:
lean_assert(false);
SASSERT(false);
break;
}*/
}
@ -138,15 +168,15 @@ int lar_core_solver::column_is_out_of_bounds(unsigned j) {
return 0;
break;
}*/
lean_assert(false);
SASSERT(false);
return true;
}
void lar_core_solver::calculate_pivot_row(unsigned i) {
lean_assert(!m_r_solver.use_tableau());
lean_assert(m_r_solver.m_pivot_row.is_OK());
SASSERT(!m_r_solver.use_tableau());
SASSERT(m_r_solver.m_pivot_row.is_OK());
m_r_solver.m_pivot_row_of_B_1.clear();
m_r_solver.m_pivot_row_of_B_1.resize(m_r_solver.m_m());
m_r_solver.m_pivot_row.clear();
@ -208,7 +238,7 @@ void lar_core_solver::calculate_pivot_row(unsigned i) {
}
void lar_core_solver::fill_not_improvable_zero_sum_from_inf_row() {
lean_assert(m_r_solver.A_mult_x_is_off() == false);
SASSERT(m_r_solver.A_mult_x_is_off() == false);
unsigned bj = m_r_basis[m_r_solver.m_inf_row_index_for_tableau];
m_infeasible_sum_sign = m_r_solver.inf_sign_of_column(bj);
m_infeasible_linear_combination.clear();
@ -243,15 +273,15 @@ void lar_core_solver::fill_not_improvable_zero_sum() {
void lar_core_solver::solve() {
lean_assert(m_r_solver.non_basic_columns_are_set_correctly());
lean_assert(m_r_solver.inf_set_is_correct());
SASSERT(m_r_solver.non_basic_columns_are_set_correctly());
SASSERT(m_r_solver.inf_set_is_correct());
if (m_r_solver.current_x_is_feasible() && m_r_solver.m_look_for_feasible_solution_only) {
m_r_solver.set_status(OPTIMAL);
return;
}
++settings().st().m_need_to_solve_inf;
lean_assert(!m_r_solver.A_mult_x_is_off());
lean_assert((!settings().use_tableau()) || r_basis_is_OK());
SASSERT(!m_r_solver.A_mult_x_is_off());
SASSERT((!settings().use_tableau()) || r_basis_is_OK());
if (need_to_presolve_with_double_solver()) {
prefix_d();
lar_solution_signature solution_signature;
@ -264,11 +294,11 @@ void lar_core_solver::solve() {
solve_on_signature_tableau(solution_signature, changes_of_basis);
else
solve_on_signature(solution_signature, changes_of_basis);
lean_assert(!settings().use_tableau() || r_basis_is_OK());
SASSERT(!settings().use_tableau() || r_basis_is_OK());
} else {
if (!settings().use_tableau()) {
bool snapped = m_r_solver.snap_non_basic_x_to_bound();
lean_assert(m_r_solver.non_basic_columns_are_set_correctly());
SASSERT(m_r_solver.non_basic_columns_are_set_correctly());
if (snapped)
m_r_solver.solve_Ax_eq_b();
}
@ -276,16 +306,16 @@ void lar_core_solver::solve() {
m_r_solver.find_feasible_solution();
else
m_r_solver.solve();
lean_assert(!settings().use_tableau() || r_basis_is_OK());
SASSERT(!settings().use_tableau() || r_basis_is_OK());
}
if (m_r_solver.get_status() == INFEASIBLE) {
fill_not_improvable_zero_sum();
} else if (m_r_solver.get_status() != UNBOUNDED) {
m_r_solver.set_status(OPTIMAL);
}
lean_assert(r_basis_is_OK());
lean_assert(m_r_solver.non_basic_columns_are_set_correctly());
lean_assert(m_r_solver.inf_set_is_correct());
SASSERT(r_basis_is_OK());
SASSERT(m_r_solver.non_basic_columns_are_set_correctly());
SASSERT(m_r_solver.inf_set_is_correct());
}

23
src/util/lp/lar_core_solver_instances.cpp
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <utility>
#include <memory>
#include <string>

25
src/util/lp/lar_solution_signature.h
View file

@ -1,13 +1,28 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
#include "util/debug.h"
#include "util/lp/lp_settings.h"
#include <unordered_map>
namespace lean {
namespace lp {
typedef std::unordered_map<unsigned, non_basic_column_value_position> lar_solution_signature;
}

213
src/util/lp/lar_solver.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
#include <utility>
@ -30,7 +45,7 @@
#include "util/lp/iterator_on_row.h"
#include "util/lp/quick_xplain.h"
#include "util/lp/conversion_helper.h"
namespace lean {
namespace lp {
class lar_solver : public column_namer {
//////////////////// fields //////////////////////////
@ -75,7 +90,7 @@ public:
lp_settings const & settings() const { return m_settings;}
void clear() {lean_assert(false); // not implemented
void clear() {SASSERT(false); // not implemented
}
@ -107,7 +122,7 @@ public:
}
unsigned adjust_term_index(unsigned j) const {
lean_assert(is_term(j));
SASSERT(is_term(j));
return j - m_terms_start_index;
}
@ -115,10 +130,10 @@ public:
bool use_lu() const { return m_settings.simplex_strategy() == simplex_strategy_enum::lu; }
bool sizes_are_correct() const {
lean_assert(strategy_is_undecided() || !m_mpq_lar_core_solver.need_to_presolve_with_double_solver() || A_r().column_count() == A_d().column_count());
lean_assert(A_r().column_count() == m_mpq_lar_core_solver.m_r_solver.m_column_types.size());
lean_assert(A_r().column_count() == m_mpq_lar_core_solver.m_r_solver.m_costs.size());
lean_assert(A_r().column_count() == m_mpq_lar_core_solver.m_r_x.size());
SASSERT(strategy_is_undecided() || !m_mpq_lar_core_solver.need_to_presolve_with_double_solver() || A_r().column_count() == A_d().column_count());
SASSERT(A_r().column_count() == m_mpq_lar_core_solver.m_r_solver.m_column_types.size());
SASSERT(A_r().column_count() == m_mpq_lar_core_solver.m_r_solver.m_costs.size());
SASSERT(A_r().column_count() == m_mpq_lar_core_solver.m_r_x.size());
return true;
}
@ -160,7 +175,7 @@ public:
else if (kind == LE || kind == LT) n_of_L++;
rs_of_evidence += coeff*constr.m_right_side;
}
lean_assert(n_of_G == 0 || n_of_L == 0);
SASSERT(n_of_G == 0 || n_of_L == 0);
lconstraint_kind kind = n_of_G ? GE : (n_of_L ? LE : EQ);
if (strict)
kind = static_cast<lconstraint_kind>((static_cast<int>(kind) / 2));
@ -204,7 +219,7 @@ public:
void analyze_new_bounds_on_row(
unsigned row_index,
lp_bound_propagator & bp) {
lean_assert(!use_tableau());
SASSERT(!use_tableau());
iterator_on_pivot_row<mpq> it(m_mpq_lar_core_solver.get_pivot_row(), m_mpq_lar_core_solver.m_r_basis[row_index]);
bound_analyzer_on_row ra_pos(it,
@ -223,7 +238,7 @@ public:
if (A_r().m_rows[row_index].size() > settings().max_row_length_for_bound_propagation)
return;
iterator_on_row<mpq> it(A_r().m_rows[row_index]);
lean_assert(use_tableau());
SASSERT(use_tableau());
bound_analyzer_on_row::analyze_row(it,
zero_of_type<numeric_pair<mpq>>(),
row_index,
@ -271,7 +286,7 @@ public:
}
void fill_bound_evidence_on_term(implied_bound & ie, implied_bound& be) {
lean_assert(false);
SASSERT(false);
}
void fill_implied_bound_on_row(implied_bound & ie, implied_bound& be) {
iterator_on_row<mpq> it(A_r().m_rows[ie.m_row_or_term_index]);
@ -285,7 +300,7 @@ public:
if (is_neg(a)) { // so the monoid has a positive coeff on the right side
constraint_index witness = toggle ? ul.m_low_bound_witness : ul.m_upper_bound_witness;
lean_assert(is_valid(witness));
SASSERT(is_valid(witness));
be.m_explanation.emplace_back(a, witness);
}
}
@ -304,7 +319,7 @@ public:
}
implied_bound fill_implied_bound_for_upper_bound(implied_bound& implied_evidence) {
lean_assert(false);
SASSERT(false);
be.m_j = implied_evidence.m_j;
be.m_bound = implied_evidence.m_bound.x;
@ -312,7 +327,7 @@ public:
for (auto t : implied_evidence.m_vector_of_bound_signatures) {
const ul_pair & ul = m_vars_to_ul_pairs[t.m_column_index];
constraint_index witness = t.m_low_bound ? ul.m_low_bound_witness : ul.m_upper_bound_witness;
lean_assert(is_valid(witness));
SASSERT(is_valid(witness));
be.m_explanation.emplace_back(t.m_coeff, witness);
}
@ -338,7 +353,7 @@ public:
// implied_bound * get_existing_
linear_combination_iterator<mpq> * create_new_iter_from_term(unsigned term_index) const {
lean_assert(false); // not implemented
SASSERT(false); // not implemented
return nullptr;
// new linear_combination_iterator_on_vector<mpq>(m_terms[adjust_term_index(term_index)]->coeffs_as_vector());
}
@ -349,7 +364,7 @@ public:
}
void propagate_bounds_on_a_term(const lar_term& t, lp_bound_propagator & bp, unsigned term_offset) {
lean_assert(false); // not implemented
SASSERT(false); // not implemented
}
@ -372,15 +387,15 @@ public:
int sign = j_sign * a_sign;
const ul_pair & ul = m_vars_to_ul_pairs[j];
auto witness = sign > 0? ul.upper_bound_witness(): ul.low_bound_witness();
lean_assert(is_valid(witness));
SASSERT(is_valid(witness));
bp.consume(a, witness);
}
// lean_assert(implied_bound_is_correctly_explained(ib, explanation));
// SASSERT(implied_bound_is_correctly_explained(ib, explanation));
}
bool term_is_used_as_row(unsigned term) const {
lean_assert(is_term(term));
SASSERT(is_term(term));
return contains(m_ext_vars_to_columns, term);
}
@ -500,12 +515,12 @@ public:
unsigned m = A_r().row_count();
clean_large_elements_after_pop(m, m_rows_with_changed_bounds);
clean_inf_set_of_r_solver_after_pop();
lean_assert(m_settings.simplex_strategy() == simplex_strategy_enum::undecided ||
SASSERT(m_settings.simplex_strategy() == simplex_strategy_enum::undecided ||
(!use_tableau()) || m_mpq_lar_core_solver.m_r_solver.reduced_costs_are_correct_tableau());
lean_assert(ax_is_correct());
lean_assert(m_mpq_lar_core_solver.m_r_solver.inf_set_is_correct());
SASSERT(ax_is_correct());
SASSERT(m_mpq_lar_core_solver.m_r_solver.inf_set_is_correct());
m_constraint_count.pop(k);
for (unsigned i = m_constraint_count; i < m_constraints.size(); i++)
delete m_constraints[i];
@ -520,8 +535,8 @@ public:
m_orig_terms.resize(m_term_count);
m_simplex_strategy.pop(k);
m_settings.simplex_strategy() = m_simplex_strategy;
lean_assert(sizes_are_correct());
lean_assert((!m_settings.use_tableau()) || m_mpq_lar_core_solver.m_r_solver.reduced_costs_are_correct_tableau());
SASSERT(sizes_are_correct());
SASSERT((!m_settings.use_tableau()) || m_mpq_lar_core_solver.m_r_solver.reduced_costs_are_correct_tableau());
}
vector<constraint_index> get_all_constraint_indices() const {
@ -550,13 +565,13 @@ public:
bool costs_are_zeros_for_r_solver() const {
for (unsigned j = 0; j < m_mpq_lar_core_solver.m_r_solver.m_costs.size(); j++) {
lean_assert(is_zero(m_mpq_lar_core_solver.m_r_solver.m_costs[j]));
SASSERT(is_zero(m_mpq_lar_core_solver.m_r_solver.m_costs[j]));
}
return true;
}
bool reduced_costs_are_zeroes_for_r_solver() const {
for (unsigned j = 0; j < m_mpq_lar_core_solver.m_r_solver.m_d.size(); j++) {
lean_assert(is_zero(m_mpq_lar_core_solver.m_r_solver.m_d[j]));
SASSERT(is_zero(m_mpq_lar_core_solver.m_r_solver.m_d[j]));
}
return true;
}
@ -564,7 +579,7 @@ public:
void set_costs_to_zero(const vector<std::pair<mpq, var_index>> & term) {
auto & rslv = m_mpq_lar_core_solver.m_r_solver;
auto & jset = m_mpq_lar_core_solver.m_r_solver.m_inf_set; // hijack this set that should be empty right now
lean_assert(jset.m_index.size()==0);
SASSERT(jset.m_index.size()==0);
for (auto & p : term) {
unsigned j = p.second;
@ -583,16 +598,16 @@ public:
jset.clear();
lean_assert(reduced_costs_are_zeroes_for_r_solver());
lean_assert(costs_are_zeros_for_r_solver());
SASSERT(reduced_costs_are_zeroes_for_r_solver());
SASSERT(costs_are_zeros_for_r_solver());
}
void prepare_costs_for_r_solver(const vector<std::pair<mpq, var_index>> & term) {
auto & rslv = m_mpq_lar_core_solver.m_r_solver;
rslv.m_using_infeas_costs = false;
lean_assert(costs_are_zeros_for_r_solver());
lean_assert(reduced_costs_are_zeroes_for_r_solver());
SASSERT(costs_are_zeros_for_r_solver());
SASSERT(reduced_costs_are_zeroes_for_r_solver());
rslv.m_costs.resize(A_r().column_count(), zero_of_type<mpq>());
for (auto & p : term) {
unsigned j = p.second;
@ -602,7 +617,7 @@ public:
else
rslv.update_reduced_cost_for_basic_column_cost_change(- p.first, j);
}
lean_assert(rslv.reduced_costs_are_correct_tableau());
SASSERT(rslv.reduced_costs_are_correct_tableau());
}
bool maximize_term_on_corrected_r_solver(const vector<std::pair<mpq, var_index>> & term,
@ -629,10 +644,10 @@ public:
}
case simplex_strategy_enum::lu:
lean_assert(false); // not implemented
SASSERT(false); // not implemented
return false;
default:
lean_unreachable(); // wrong mode
SASSERT(false); // wrong mode
}
return false;
}
@ -640,7 +655,7 @@ public:
// return true if found and false if unbounded
bool maximize_term(const vector<std::pair<mpq, var_index>> & term,
impq &term_max) {
lean_assert(m_mpq_lar_core_solver.m_r_solver.current_x_is_feasible());
SASSERT(m_mpq_lar_core_solver.m_r_solver.current_x_is_feasible());
m_mpq_lar_core_solver.m_r_solver.m_look_for_feasible_solution_only = false;
return maximize_term_on_corrected_r_solver(term, term_max);
}
@ -648,7 +663,7 @@ public:
const lar_term & get_term(unsigned j) const {
lean_assert(j >= m_terms_start_index);
SASSERT(j >= m_terms_start_index);
return *m_terms[j - m_terms_start_index];
}
@ -680,7 +695,7 @@ public:
vector<std::pair<mpq, var_index>> &left_side, mpq & right_side) const {
for (auto & t : left_side_with_terms) {
if (t.second < m_terms_start_index) {
lean_assert(t.second < A_r().column_count());
SASSERT(t.second < A_r().column_count());
left_side.push_back(std::pair<mpq, var_index>(mult * t.first, t.second));
} else {
const lar_term & term = * m_terms[adjust_term_index(t.second)];
@ -696,7 +711,7 @@ public:
m_column_buffer.resize(A_r().row_count());
else
m_column_buffer.clear();
lean_assert(m_column_buffer.size() == 0 && m_column_buffer.is_OK());
SASSERT(m_column_buffer.size() == 0 && m_column_buffer.is_OK());
m_mpq_lar_core_solver.m_r_solver.solve_Bd(j, m_column_buffer);
for (unsigned i : m_column_buffer.m_index)
@ -730,7 +745,7 @@ public:
}
void adjust_x_of_column(unsigned j) {
lean_assert(false);
SASSERT(false);
}
bool row_is_correct(unsigned i) const {
@ -819,14 +834,14 @@ public:
}
void update_x_and_inf_costs_for_columns_with_changed_bounds_tableau() {
lean_assert(ax_is_correct());
SASSERT(ax_is_correct());
for (auto j : m_columns_with_changed_bound.m_index)
update_x_and_inf_costs_for_column_with_changed_bounds(j);
if (tableau_with_costs()) {
for (unsigned j : m_basic_columns_with_changed_cost.m_index)
m_mpq_lar_core_solver.m_r_solver.update_inf_cost_for_column_tableau(j);
lean_assert(m_mpq_lar_core_solver.m_r_solver.reduced_costs_are_correct_tableau());
SASSERT(m_mpq_lar_core_solver.m_r_solver.reduced_costs_are_correct_tableau());
}
}
@ -848,7 +863,7 @@ public:
update_x_and_inf_costs_for_columns_with_changed_bounds();
m_mpq_lar_core_solver.solve();
set_status(m_mpq_lar_core_solver.m_r_solver.get_status());
lean_assert(m_status != OPTIMAL || all_constraints_hold());
SASSERT(m_status != OPTIMAL || all_constraints_hold());
}
@ -875,7 +890,7 @@ public:
numeric_pair<mpq> r = zero_of_type<numeric_pair<mpq>>();
m_mpq_lar_core_solver.calculate_pivot_row(i);
for (unsigned j : m_mpq_lar_core_solver.m_r_solver.m_pivot_row.m_index) {
lean_assert(m_mpq_lar_core_solver.m_r_solver.m_basis_heading[j] < 0);
SASSERT(m_mpq_lar_core_solver.m_r_solver.m_basis_heading[j] < 0);
r -= m_mpq_lar_core_solver.m_r_solver.m_pivot_row.m_data[j] * m_mpq_lar_core_solver.m_r_x[j];
}
return r;
@ -939,12 +954,12 @@ public:
}
void fill_last_row_of_A_r(static_matrix<mpq, numeric_pair<mpq>> & A, const lar_term * ls) {
lean_assert(A.row_count() > 0);
lean_assert(A.column_count() > 0);
SASSERT(A.row_count() > 0);
SASSERT(A.column_count() > 0);
unsigned last_row = A.row_count() - 1;
lean_assert(A.m_rows[last_row].size() == 0);
SASSERT(A.m_rows[last_row].size() == 0);
for (auto & t : ls->m_coeffs) {
lean_assert(!is_zero(t.second));
SASSERT(!is_zero(t.second));
var_index j = t.first;
A.set(last_row, j, - t.second);
}
@ -954,7 +969,7 @@ public:
template <typename U, typename V>
void create_matrix_A(static_matrix<U, V> & matr) {
lean_assert(false); // not implemented
SASSERT(false); // not implemented
/*
unsigned m = number_or_nontrivial_left_sides();
unsigned n = m_vec_of_canonic_left_sides.size();
@ -1016,8 +1031,8 @@ public:
mpq rs = right_side_parm;
vector<std::pair<mpq, var_index>> left_side;
substitute_terms(one_of_type<mpq>(), left_side_with_terms, left_side, rs);
lean_assert(left_side.size() > 0);
lean_assert(all_constrained_variables_are_registered(left_side));
SASSERT(left_side.size() > 0);
SASSERT(all_constrained_variables_are_registered(left_side));
lar_constraint original_constr(left_side, kind_par, rs);
unsigned j; // j is the index of the basic variables corresponding to the left side
canonic_left_side ls = create_or_fetch_canonic_left_side(left_side, j);
@ -1030,7 +1045,7 @@ public:
update_column_type_and_bound(j, kind, rs, constr_ind);
return constr_ind;
*/
lean_assert(false); // not implemented
SASSERT(false); // not implemented
return 0;
}
@ -1058,7 +1073,7 @@ public:
case GT: return left_side_val > constr.m_right_side;
case EQ: return left_side_val == constr.m_right_side;
default:
lean_unreachable();
SASSERT(false);
}
return false; // it is unreachable
}
@ -1108,7 +1123,7 @@ public:
for (auto & it : evidence) {
mpq coeff = it.first;
constraint_index con_ind = it.second;
lean_assert(con_ind < m_constraints.size());
SASSERT(con_ind < m_constraints.size());
register_in_map(coeff_map, *m_constraints[con_ind], coeff);
}
@ -1131,7 +1146,7 @@ public:
for (auto & it : evidence) {
mpq coeff = it.first;
constraint_index con_ind = it.second;
lean_assert(con_ind < m_constraints.size());
SASSERT(con_ind < m_constraints.size());
const lar_constraint & constr = *m_constraints[con_ind];
ret += constr.m_right_side * coeff;
}
@ -1139,24 +1154,24 @@ public:
}
bool explanation_is_correct(const vector<std::pair<mpq, unsigned>>& explanation) const {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
lconstraint_kind kind;
lean_assert(the_relations_are_of_same_type(explanation, kind));
lean_assert(the_left_sides_sum_to_zero(explanation));
SASSERT(the_relations_are_of_same_type(explanation, kind));
SASSERT(the_left_sides_sum_to_zero(explanation));
mpq rs = sum_of_right_sides_of_explanation(explanation);
switch (kind) {
case LE: lean_assert(rs < zero_of_type<mpq>());
case LE: SASSERT(rs < zero_of_type<mpq>());
break;
case LT: lean_assert(rs <= zero_of_type<mpq>());
case LT: SASSERT(rs <= zero_of_type<mpq>());
break;
case GE: lean_assert(rs > zero_of_type<mpq>());
case GE: SASSERT(rs > zero_of_type<mpq>());
break;
case GT: lean_assert(rs >= zero_of_type<mpq>());
case GT: SASSERT(rs >= zero_of_type<mpq>());
break;
case EQ: lean_assert(rs != zero_of_type<mpq>());
case EQ: SASSERT(rs != zero_of_type<mpq>());
break;
default:
lean_assert(false);
SASSERT(false);
return false;
}
#endif
@ -1164,7 +1179,7 @@ public:
}
bool inf_explanation_is_correct() const {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
vector<std::pair<mpq, unsigned>> explanation;
get_infeasibility_explanation(explanation);
return explanation_is_correct(explanation);
@ -1177,7 +1192,7 @@ public:
for (auto & it : explanation) {
mpq coeff = it.first;
constraint_index con_ind = it.second;
lean_assert(con_ind < m_constraints.size());
SASSERT(con_ind < m_constraints.size());
ret += (m_constraints[con_ind]->m_right_side - m_constraints[con_ind]->get_free_coeff_of_left_side()) * coeff;
}
return ret;
@ -1235,7 +1250,7 @@ public:
int inf_sign;
auto inf_row = m_mpq_lar_core_solver.get_infeasibility_info(inf_sign);
get_infeasibility_explanation_for_inf_sign(explanation, inf_row, inf_sign);
lean_assert(explanation_is_correct(explanation));
SASSERT(explanation_is_correct(explanation));
}
void get_infeasibility_explanation_for_inf_sign(
@ -1251,7 +1266,7 @@ public:
const ul_pair & ul = m_vars_to_ul_pairs[j];
constraint_index bound_constr_i = adj_sign < 0 ? ul.upper_bound_witness() : ul.low_bound_witness();
lean_assert(bound_constr_i < m_constraints.size());
SASSERT(bound_constr_i < m_constraints.size());
explanation.push_back(std::make_pair(coeff, bound_constr_i));
}
}
@ -1260,7 +1275,7 @@ public:
void get_model(std::unordered_map<var_index, mpq> & variable_values) const {
mpq delta = m_mpq_lar_core_solver.find_delta_for_strict_bounds(mpq(1, 2)); // start from 0.5 to have less clashes
lean_assert(m_status == OPTIMAL);
SASSERT(m_status == OPTIMAL);
unsigned i;
do {
@ -1332,7 +1347,7 @@ public:
for (auto & it : cns.get_left_side_coefficients()) {
var_index j = it.second;
auto vi = var_map.find(j);
lean_assert(vi != var_map.end());
SASSERT(vi != var_map.end());
ret += it.first * vi->second;
}
return ret;
@ -1379,7 +1394,7 @@ public:
void make_sure_that_the_bottom_right_elem_not_zero_in_tableau(unsigned i, unsigned j) {
// i, j - is the indices of the bottom-right element of the tableau
lean_assert(A_r().row_count() == i + 1 && A_r().column_count() == j + 1);
SASSERT(A_r().row_count() == i + 1 && A_r().column_count() == j + 1);
auto & last_column = A_r().m_columns[j];
int non_zero_column_cell_index = -1;
for (unsigned k = last_column.size(); k-- > 0;){
@ -1389,13 +1404,13 @@ public:
non_zero_column_cell_index = k;
}
lean_assert(non_zero_column_cell_index != -1);
lean_assert(static_cast<unsigned>(non_zero_column_cell_index) != i);
SASSERT(non_zero_column_cell_index != -1);
SASSERT(static_cast<unsigned>(non_zero_column_cell_index) != i);
m_mpq_lar_core_solver.m_r_solver.transpose_rows_tableau(last_column[non_zero_column_cell_index].m_i, i);
}
void remove_last_row_and_column_from_tableau(unsigned j) {
lean_assert(A_r().column_count() == m_mpq_lar_core_solver.m_r_solver.m_costs.size());
SASSERT(A_r().column_count() == m_mpq_lar_core_solver.m_r_solver.m_costs.size());
auto & slv = m_mpq_lar_core_solver.m_r_solver;
unsigned i = A_r().row_count() - 1; //last row index
make_sure_that_the_bottom_right_elem_not_zero_in_tableau(i, j);
@ -1414,17 +1429,17 @@ public:
A_r().remove_element(last_row, rc);
}
lean_assert(last_row.size() == 0);
lean_assert(A_r().m_columns[j].size() == 0);
SASSERT(last_row.size() == 0);
SASSERT(A_r().m_columns[j].size() == 0);
A_r().m_rows.pop_back();
A_r().m_columns.pop_back();
slv.m_b.pop_back();
}
void remove_last_column_from_tableau(unsigned j) {
lean_assert(j == A_r().column_count() - 1);
SASSERT(j == A_r().column_count() - 1);
// the last column has to be empty
lean_assert(A_r().m_columns[j].size() == 0);
SASSERT(A_r().m_columns[j].size() == 0);
A_r().m_columns.pop_back();
}
@ -1433,7 +1448,7 @@ public:
int i = rslv.m_basis_heading[j];
if (i >= 0) { // j is a basic var
int last_pos = static_cast<int>(rslv.m_basis.size()) - 1;
lean_assert(last_pos >= 0);
SASSERT(last_pos >= 0);
if (i != last_pos) {
unsigned j_at_last_pos = rslv.m_basis[last_pos];
rslv.m_basis[i] = j_at_last_pos;
@ -1442,7 +1457,7 @@ public:
rslv.m_basis.pop_back(); // remove j from the basis
} else {
int last_pos = static_cast<int>(rslv.m_nbasis.size()) - 1;
lean_assert(last_pos >= 0);
SASSERT(last_pos >= 0);
i = - 1 - i;
if (i != last_pos) {
unsigned j_at_last_pos = rslv.m_nbasis[last_pos];
@ -1452,14 +1467,14 @@ public:
rslv.m_nbasis.pop_back(); // remove j from the basis
}
rslv.m_basis_heading.pop_back();
lean_assert(rslv.m_basis.size() == A_r().row_count());
lean_assert(rslv.basis_heading_is_correct());
SASSERT(rslv.m_basis.size() == A_r().row_count());
SASSERT(rslv.basis_heading_is_correct());
}
void remove_column_from_tableau(unsigned j) {
auto& rslv = m_mpq_lar_core_solver.m_r_solver;
lean_assert(j == A_r().column_count() - 1);
lean_assert(A_r().column_count() == m_mpq_lar_core_solver.m_r_solver.m_costs.size());
SASSERT(j == A_r().column_count() - 1);
SASSERT(A_r().column_count() == m_mpq_lar_core_solver.m_r_solver.m_costs.size());
if (column_represents_row_in_tableau(j)) {
remove_last_row_and_column_from_tableau(j);
if (rslv.m_basis_heading[j] < 0)
@ -1473,23 +1488,23 @@ public:
rslv.m_costs.pop_back();
remove_last_column_from_basis_tableau(j);
lean_assert(m_mpq_lar_core_solver.r_basis_is_OK());
lean_assert(A_r().column_count() == m_mpq_lar_core_solver.m_r_solver.m_costs.size());
SASSERT(m_mpq_lar_core_solver.r_basis_is_OK());
SASSERT(A_r().column_count() == m_mpq_lar_core_solver.m_r_solver.m_costs.size());
}
void pop_tableau() {
lean_assert(m_mpq_lar_core_solver.m_r_solver.m_costs.size() == A_r().column_count());
SASSERT(m_mpq_lar_core_solver.m_r_solver.m_costs.size() == A_r().column_count());
lean_assert(m_mpq_lar_core_solver.m_r_solver.m_basis.size() == A_r().row_count());
lean_assert(m_mpq_lar_core_solver.m_r_solver.basis_heading_is_correct());
SASSERT(m_mpq_lar_core_solver.m_r_solver.m_basis.size() == A_r().row_count());
SASSERT(m_mpq_lar_core_solver.m_r_solver.basis_heading_is_correct());
// We remove last variables starting from m_column_names.size() to m_vec_of_canonic_left_sides.size().
// At this moment m_column_names is already popped
for (unsigned j = A_r().column_count(); j-- > m_columns_to_ext_vars_or_term_indices.size();)
remove_column_from_tableau(j);
lean_assert(m_mpq_lar_core_solver.m_r_solver.m_costs.size() == A_r().column_count());
lean_assert(m_mpq_lar_core_solver.m_r_solver.m_basis.size() == A_r().row_count());
lean_assert(m_mpq_lar_core_solver.m_r_solver.basis_heading_is_correct());
SASSERT(m_mpq_lar_core_solver.m_r_solver.m_costs.size() == A_r().column_count());
SASSERT(m_mpq_lar_core_solver.m_r_solver.m_basis.size() == A_r().row_count());
SASSERT(m_mpq_lar_core_solver.m_r_solver.basis_heading_is_correct());
}
@ -1512,14 +1527,14 @@ public:
}
for (unsigned j : became_feas) {
lean_assert(m_mpq_lar_core_solver.m_r_solver.m_basis_heading[j] < 0);
SASSERT(m_mpq_lar_core_solver.m_r_solver.m_basis_heading[j] < 0);
m_mpq_lar_core_solver.m_r_solver.m_d[j] -= m_mpq_lar_core_solver.m_r_solver.m_costs[j];
m_mpq_lar_core_solver.m_r_solver.m_costs[j] = zero_of_type<mpq>();
m_mpq_lar_core_solver.m_r_solver.m_inf_set.erase(j);
}
became_feas.clear();
for (unsigned j : m_mpq_lar_core_solver.m_r_solver.m_inf_set.m_index) {
lean_assert(m_mpq_lar_core_solver.m_r_heading[j] >= 0);
SASSERT(m_mpq_lar_core_solver.m_r_heading[j] >= 0);
if (m_mpq_lar_core_solver.m_r_solver.column_is_feasible(j))
became_feas.push_back(j);
}
@ -1532,7 +1547,7 @@ public:
m_mpq_lar_core_solver.m_r_solver.update_inf_cost_for_column_tableau(j);
for (unsigned j : basic_columns_with_changed_cost)
m_mpq_lar_core_solver.m_r_solver.update_inf_cost_for_column_tableau(j);
lean_assert(m_mpq_lar_core_solver.m_r_solver.reduced_costs_are_correct_tableau());
SASSERT(m_mpq_lar_core_solver.m_r_solver.reduced_costs_are_correct_tableau());
}
}
@ -1540,7 +1555,7 @@ public:
void shrink_explanation_to_minimum(vector<std::pair<mpq, constraint_index>> & explanation) const {
// implementing quickXplain
quick_xplain::run(explanation, *this);
lean_assert(this->explanation_is_correct(explanation));
SASSERT(this->explanation_is_correct(explanation));
}
};
}

25
src/util/lp/lar_term.h
View file

@ -1,10 +1,25 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/lp/indexed_vector.h"
namespace lean {
namespace lp {
struct lar_term {
// the term evaluates to sum of m_coeffs + m_v
std::unordered_map<unsigned, mpq> m_coeffs;

25
src/util/lp/linear_combination_iterator.h
View file

@ -1,9 +1,24 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
namespace lean {
namespace lp {
template <typename T>
struct linear_combination_iterator {
virtual bool next(T & a, unsigned & i) = 0;

25
src/util/lp/lp_bound_propagator.cpp
View file

@ -1,9 +1,24 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/lar_solver.h"
namespace lean {
namespace lp {
lp_bound_propagator::lp_bound_propagator(lar_solver & ls):
m_lar_solver(ls) {}
column_type lp_bound_propagator::get_column_type(unsigned j) const {

27
src/util/lp/lp_bound_propagator.h
View file

@ -1,10 +1,25 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/lp/lp_settings.h"
namespace lean {
namespace lp {
class lar_solver;
class lp_bound_propagator {
std::unordered_map<unsigned, unsigned> m_improved_low_bounds; // these maps map a column index to the corresponding index in ibounds
@ -19,7 +34,7 @@ public:
const impq & get_upper_bound(unsigned) const;
void try_add_bound(const mpq & v, unsigned j, bool is_low, bool coeff_before_j_is_pos, unsigned row_or_term_index, bool strict);
virtual bool bound_is_interesting(unsigned vi,
lean::lconstraint_kind kind,
lp::lconstraint_kind kind,
const rational & bval) {return true;}
unsigned number_of_found_bounds() const { return m_ibounds.size(); }
virtual void consume(mpq const& v, unsigned j) { std::cout << "doh\n"; }

53
src/util/lp/lp_core_solver_base.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include <set>
#include "util/vector.h"
@ -13,7 +28,7 @@
#include "util/lp/lu.h"
#include "util/lp/permutation_matrix.h"
#include "util/lp/column_namer.h"
namespace lean {
namespace lp {
template <typename T, typename X> // X represents the type of the x variable and the bounds
class lp_core_solver_base {
@ -182,11 +197,11 @@ public:
bool need_to_pivot_to_basis_tableau() const {
lean_assert(m_A.is_correct());
SASSERT(m_A.is_correct());
unsigned m = m_A.row_count();
for (unsigned i = 0; i < m; i++) {
unsigned bj = m_basis[i];
lean_assert(m_A.m_columns[bj].size() > 0);
SASSERT(m_A.m_columns[bj].size() > 0);
if (m_A.m_columns[bj].size() > 1 || m_A.get_val(m_A.m_columns[bj][0]) != one_of_type<mpq>()) return true;
}
return false;
@ -195,7 +210,7 @@ public:
bool reduced_costs_are_correct_tableau() const {
if (m_settings.simplex_strategy() == simplex_strategy_enum::tableau_rows)
return true;
lean_assert(m_A.is_correct());
SASSERT(m_A.is_correct());
if (m_using_infeas_costs) {
if (infeasibility_costs_are_correct() == false) {
std::cout << "infeasibility_costs_are_correct() does not hold" << std::endl;
@ -370,11 +385,11 @@ public:
}
bool make_column_feasible(unsigned j, numeric_pair<mpq> & delta) {
lean_assert(m_basis_heading[j] < 0);
SASSERT(m_basis_heading[j] < 0);
auto & x = m_x[j];
switch (m_column_types[j]) {
case column_type::fixed:
lean_assert(m_low_bounds[j] == m_upper_bounds[j]);
SASSERT(m_low_bounds[j] == m_upper_bounds[j]);
if (x != m_low_bounds[j]) {
delta = m_low_bounds[j] - x;
x = m_low_bounds[j];
@ -410,7 +425,7 @@ public:
case column_type::free_column:
break;
default:
lean_assert(false);
SASSERT(false);
break;
}
return false;
@ -458,7 +473,7 @@ public:
}
void change_basis_unconditionally(unsigned entering, unsigned leaving) {
lean_assert(m_basis_heading[entering] < 0);
SASSERT(m_basis_heading[entering] < 0);
int place_in_non_basis = -1 - m_basis_heading[entering];
if (static_cast<unsigned>(place_in_non_basis) >= m_nbasis.size()) {
// entering variable in not in m_nbasis, we need to put it back;
@ -477,7 +492,7 @@ public:
}
void change_basis(unsigned entering, unsigned leaving) {
lean_assert(m_basis_heading[entering] < 0);
SASSERT(m_basis_heading[entering] < 0);
int place_in_basis = m_basis_heading[leaving];
int place_in_non_basis = - m_basis_heading[entering] - 1;
@ -518,7 +533,7 @@ public:
case column_type::free_column:
break;
default:
lean_assert(false);
SASSERT(false);
break;
}
return true;
@ -566,7 +581,7 @@ public:
case column_type::free_column:
break;
default:
lean_assert(false);
SASSERT(false);
}
std::cout << "basis heading = " << m_basis_heading[j] << std::endl;
std::cout << "x = " << m_x[j] << std::endl;
@ -665,17 +680,17 @@ public:
}
void insert_column_into_inf_set(unsigned j) {
m_inf_set.insert(j);
lean_assert(!column_is_feasible(j));
SASSERT(!column_is_feasible(j));
}
void remove_column_from_inf_set(unsigned j) {
m_inf_set.erase(j);
lean_assert(column_is_feasible(j));
SASSERT(column_is_feasible(j));
}
bool costs_on_nbasis_are_zeros() const {
lean_assert(this->basis_heading_is_correct());
SASSERT(this->basis_heading_is_correct());
for (unsigned j = 0; j < this->m_n(); j++) {
if (this->m_basis_heading[j] < 0)
lean_assert(is_zero(this->m_costs[j]));
SASSERT(is_zero(this->m_costs[j]));
}
return true;
}

93
src/util/lp/lp_core_solver_base.hpp
View file

@ -1,13 +1,28 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <set>
#include <string>
#include "util/vector.h"
#include "util/lp/lp_utils.h"
#include "util/lp/lp_core_solver_base.h"
namespace lean {
namespace lp {
template <typename T, typename X> lp_core_solver_base<T, X>::
lp_core_solver_base(static_matrix<T, X> & A,
@ -53,7 +68,7 @@ lp_core_solver_base(static_matrix<T, X> & A,
m_tracing_basis_changes(false),
m_pivoted_rows(nullptr),
m_look_for_feasible_solution_only(false) {
lean_assert(bounds_for_boxed_are_set_correctly());
SASSERT(bounds_for_boxed_are_set_correctly());
init();
init_basis_heading_and_non_basic_columns_vector();
}
@ -61,7 +76,7 @@ lp_core_solver_base(static_matrix<T, X> & A,
template <typename T, typename X> void lp_core_solver_base<T, X>::
allocate_basis_heading() { // the rest of initilization will be handled by the factorization class
init_basis_heading_and_non_basic_columns_vector();
lean_assert(basis_heading_is_correct());
SASSERT(basis_heading_is_correct());
}
template <typename T, typename X> void lp_core_solver_base<T, X>::
init() {
@ -127,7 +142,7 @@ solve_yB(vector<T> & y) {
// }
// }
template <typename T, typename X> void lp_core_solver_base<T, X>::solve_Bd(unsigned entering, indexed_vector<T> & column) {
lean_assert(!m_settings.use_tableau());
SASSERT(!m_settings.use_tableau());
if (m_factorization == nullptr) {
init_factorization(m_factorization, m_A, m_basis, m_settings);
}
@ -137,19 +152,19 @@ template <typename T, typename X> void lp_core_solver_base<T, X>::solve_Bd(unsig
template <typename T, typename X> void lp_core_solver_base<T, X>::
solve_Bd(unsigned entering) {
lean_assert(m_ed.is_OK());
SASSERT(m_ed.is_OK());
m_factorization->solve_Bd(entering, m_ed, m_w);
if (this->precise())
m_columns_nz[entering] = m_ed.m_index.size();
lean_assert(m_ed.is_OK());
lean_assert(m_w.is_OK());
#ifdef LEAN_DEBUG
SASSERT(m_ed.is_OK());
SASSERT(m_w.is_OK());
#ifdef Z3DEBUG
// auto B = get_B(*m_factorization, m_basis);
// vector<T> a(m_m());
// m_A.copy_column_to_vector(entering, a);
// vector<T> cd(m_ed.m_data);
// B.apply_from_left(cd, m_settings);
// lean_assert(vectors_are_equal(cd , a));
// SASSERT(vectors_are_equal(cd , a));
#endif
}
@ -208,7 +223,7 @@ restore_m_ed(T * buffer) {
template <typename T, typename X> bool lp_core_solver_base<T, X>::
A_mult_x_is_off() const {
lean_assert(m_x.size() == m_A.column_count());
SASSERT(m_x.size() == m_A.column_count());
if (numeric_traits<T>::precise()) {
for (unsigned i = 0; i < m_m(); i++) {
X delta = m_b[i] - m_A.dot_product_with_row(i, m_x);
@ -244,7 +259,7 @@ A_mult_x_is_off() const {
}
template <typename T, typename X> bool lp_core_solver_base<T, X>::
A_mult_x_is_off_on_index(const vector<unsigned> & index) const {
lean_assert(m_x.size() == m_A.column_count());
SASSERT(m_x.size() == m_A.column_count());
if (numeric_traits<T>::precise()) return false;
#if RUN_A_MULT_X_IS_OFF_FOR_PRECESE
for (unsigned i : index) {
@ -284,13 +299,13 @@ A_mult_x_is_off_on_index(const vector<unsigned> & index) const {
// from page 182 of Istvan Maros's book
template <typename T, typename X> void lp_core_solver_base<T, X>::
calculate_pivot_row_of_B_1(unsigned pivot_row) {
lean_assert(! use_tableau());
lean_assert(m_pivot_row_of_B_1.is_OK());
SASSERT(! use_tableau());
SASSERT(m_pivot_row_of_B_1.is_OK());
m_pivot_row_of_B_1.clear();
m_pivot_row_of_B_1.set_value(numeric_traits<T>::one(), pivot_row);
lean_assert(m_pivot_row_of_B_1.is_OK());
SASSERT(m_pivot_row_of_B_1.is_OK());
m_factorization->solve_yB_with_error_check_indexed(m_pivot_row_of_B_1, m_basis_heading, m_basis, m_settings);
lean_assert(m_pivot_row_of_B_1.is_OK());
SASSERT(m_pivot_row_of_B_1.is_OK());
}
@ -380,11 +395,11 @@ set_non_basic_x_to_correct_bounds() {
break;
case column_type::low_bound:
m_x[j] = m_low_bounds[j];
lean_assert(column_is_dual_feasible(j));
SASSERT(column_is_dual_feasible(j));
break;
case column_type::upper_bound:
m_x[j] = m_upper_bounds[j];
lean_assert(column_is_dual_feasible(j));
SASSERT(column_is_dual_feasible(j));
break;
default:
break;
@ -402,15 +417,15 @@ column_is_dual_feasible(unsigned j) const {
return x_is_at_low_bound(j) && d_is_not_negative(j);
case column_type::upper_bound:
LP_OUT(m_settings, "upper_bound type should be switched to low_bound" << std::endl);
lean_assert(false); // impossible case
SASSERT(false); // impossible case
case column_type::free_column:
return numeric_traits<X>::is_zero(m_d[j]);
default:
LP_OUT(m_settings, "column = " << j << std::endl);
LP_OUT(m_settings, "unexpected column type = " << column_type_to_string(m_column_types[j]) << std::endl);
lean_unreachable();
SASSERT(false);
}
lean_unreachable();
SASSERT(false);
return false;
}
template <typename T, typename X> bool lp_core_solver_base<T, X>::
@ -493,7 +508,7 @@ template <typename T, typename X> bool lp_core_solver_base<T, X>::column_is_feas
return true;
break;
default:
lean_unreachable();
SASSERT(false);
}
return false; // it is unreachable
}
@ -575,7 +590,7 @@ update_basis_and_x(int entering, int leaving, X const & tt) {
restore_x_and_refactor(entering, leaving, tt);
if (m_status == FLOATING_POINT_ERROR)
return false;
lean_assert(!A_mult_x_is_off());
SASSERT(!A_mult_x_is_off());
m_iters_with_no_cost_growing++;
// LP_OUT(m_settings, "rolled back after failing of init_factorization()" << std::endl);
m_status = UNSTABLE;
@ -587,7 +602,7 @@ update_basis_and_x(int entering, int leaving, X const & tt) {
template <typename T, typename X> bool lp_core_solver_base<T, X>::
divide_row_by_pivot(unsigned pivot_row, unsigned pivot_col) {
lean_assert(numeric_traits<T>::precise());
SASSERT(numeric_traits<T>::precise());
int pivot_index = -1;
auto & row = m_A.m_rows[pivot_row];
unsigned size = row.size();
@ -628,7 +643,7 @@ pivot_column_tableau(unsigned j, unsigned piv_row_index) {
return false;
if (pivot_col_cell_index != 0) {
lean_assert(column.size() > 1);
SASSERT(column.size() > 1);
// swap the pivot column cell with the head cell
auto c = column[0];
column[0] = column[pivot_col_cell_index];
@ -639,7 +654,7 @@ pivot_column_tableau(unsigned j, unsigned piv_row_index) {
}
while (column.size() > 1) {
auto & c = column.back();
lean_assert(c.m_i != piv_row_index);
SASSERT(c.m_i != piv_row_index);
if(! m_A.pivot_row_to_row_given_cell(piv_row_index, c, j)) {
return false;
}
@ -687,7 +702,7 @@ non_basis_is_correctly_represented_in_heading() const {
}
for (unsigned j = 0; j < m_A.column_count(); j++) {
if (m_basis_heading[j] >= 0) {
lean_assert(static_cast<unsigned>(m_basis_heading[j]) < m_A.row_count() && m_basis[m_basis_heading[j]] == j);
SASSERT(static_cast<unsigned>(m_basis_heading[j]) < m_A.row_count() && m_basis[m_basis_heading[j]] == j);
}
}
return true;
@ -695,9 +710,9 @@ non_basis_is_correctly_represented_in_heading() const {
template <typename T, typename X> bool lp_core_solver_base<T, X>::
basis_heading_is_correct() const {
lean_assert(m_basis_heading.size() == m_A.column_count());
lean_assert(m_basis.size() == m_A.row_count());
lean_assert(m_nbasis.size() <= m_A.column_count() - m_A.row_count()); // for the dual the size of non basis can be smaller
SASSERT(m_basis_heading.size() == m_A.column_count());
SASSERT(m_basis.size() == m_A.row_count());
SASSERT(m_nbasis.size() <= m_A.column_count() - m_A.row_count()); // for the dual the size of non basis can be smaller
if (!basis_has_no_doubles()) {
// std::cout << "basis_has_no_doubles" << std::endl;
return false;
@ -841,7 +856,7 @@ solve_Ax_eq_b() {
template <typename T, typename X> void lp_core_solver_base<T, X>::
snap_non_basic_x_to_bound_and_free_to_zeroes() {
for (unsigned j : non_basis()) {
lean_assert(j < m_x.size());
SASSERT(j < m_x.size());
switch (m_column_types[j]) {
case column_type::fixed:
case column_type::boxed:
@ -892,9 +907,9 @@ get_non_basic_column_value_position(unsigned j) const {
case column_type::upper_bound:
return x_is_at_upper_bound(j)? at_upper_bound : not_at_bound;
default:
lean_unreachable();
SASSERT(false);
}
lean_unreachable();
SASSERT(false);
return at_low_bound;
}
@ -958,7 +973,7 @@ template <typename T, typename X> void lp_core_solver_base<T, X>::pivot_fixed_v
break;
}
}
lean_assert(m_factorization->get_status()== LU_status::OK);
SASSERT(m_factorization->get_status()== LU_status::OK);
}
}
@ -966,7 +981,7 @@ template <typename T, typename X> bool
lp_core_solver_base<T, X>::infeasibility_costs_are_correct() const {
if (! this->m_using_infeas_costs)
return true;
lean_assert(costs_on_nbasis_are_zeros());
SASSERT(costs_on_nbasis_are_zeros());
for (unsigned j :this->m_basis) {
if (!infeasibility_cost_is_correct_for_column(j)) {
std::cout << "infeasibility_cost_is_correct_for_column does not hold\n";
@ -1011,7 +1026,7 @@ lp_core_solver_base<T, X>::infeasibility_cost_is_correct_for_column(unsigned j)
case column_type::free_column:
return is_zero(this->m_costs[j]);
default:
lean_assert(false);
SASSERT(false);
return true;
}
}

245
src/util/lp/lp_core_solver_base_instances.cpp
View file

@ -1,131 +1,146 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <utility>
#include <memory>
#include <string>
#include "util/vector.h"
#include <functional>
#include "util/lp/lp_core_solver_base.hpp"
template bool lean::lp_core_solver_base<double, double>::A_mult_x_is_off() const;
template bool lean::lp_core_solver_base<double, double>::A_mult_x_is_off_on_index(const vector<unsigned> &) const;
template bool lean::lp_core_solver_base<double, double>::basis_heading_is_correct() const;
template void lean::lp_core_solver_base<double, double>::calculate_pivot_row_of_B_1(unsigned int);
template void lean::lp_core_solver_base<double, double>::calculate_pivot_row_when_pivot_row_of_B1_is_ready(unsigned);
template bool lean::lp_core_solver_base<double, double>::column_is_dual_feasible(unsigned int) const;
template void lean::lp_core_solver_base<double, double>::fill_reduced_costs_from_m_y_by_rows();
template bool lean::lp_core_solver_base<double, double>::find_x_by_solving();
template lean::non_basic_column_value_position lean::lp_core_solver_base<double, double>::get_non_basic_column_value_position(unsigned int) const;
template lean::non_basic_column_value_position lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::get_non_basic_column_value_position(unsigned int) const;
template lean::non_basic_column_value_position lean::lp_core_solver_base<lean::mpq, lean::mpq>::get_non_basic_column_value_position(unsigned int) const;
template void lean::lp_core_solver_base<double, double>::init_reduced_costs_for_one_iteration();
template lean::lp_core_solver_base<double, double>::lp_core_solver_base(
lean::static_matrix<double, double>&, vector<double>&,
template bool lp::lp_core_solver_base<double, double>::A_mult_x_is_off() const;
template bool lp::lp_core_solver_base<double, double>::A_mult_x_is_off_on_index(const vector<unsigned> &) const;
template bool lp::lp_core_solver_base<double, double>::basis_heading_is_correct() const;
template void lp::lp_core_solver_base<double, double>::calculate_pivot_row_of_B_1(unsigned int);
template void lp::lp_core_solver_base<double, double>::calculate_pivot_row_when_pivot_row_of_B1_is_ready(unsigned);
template bool lp::lp_core_solver_base<double, double>::column_is_dual_feasible(unsigned int) const;
template void lp::lp_core_solver_base<double, double>::fill_reduced_costs_from_m_y_by_rows();
template bool lp::lp_core_solver_base<double, double>::find_x_by_solving();
template lp::non_basic_column_value_position lp::lp_core_solver_base<double, double>::get_non_basic_column_value_position(unsigned int) const;
template lp::non_basic_column_value_position lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::get_non_basic_column_value_position(unsigned int) const;
template lp::non_basic_column_value_position lp::lp_core_solver_base<lp::mpq, lp::mpq>::get_non_basic_column_value_position(unsigned int) const;
template void lp::lp_core_solver_base<double, double>::init_reduced_costs_for_one_iteration();
template lp::lp_core_solver_base<double, double>::lp_core_solver_base(
lp::static_matrix<double, double>&, vector<double>&,
vector<unsigned int >&,
vector<unsigned> &, vector<int> &,
vector<double >&,
vector<double >&,
lean::lp_settings&, const column_namer&, const vector<lean::column_type >&,
lp::lp_settings&, const column_namer&, const vector<lp::column_type >&,
const vector<double >&,
const vector<double >&);
template bool lean::lp_core_solver_base<double, double>::print_statistics_with_iterations_and_nonzeroes_and_cost_and_check_that_the_time_is_over(char const*, std::ostream &);
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::print_statistics_with_iterations_and_nonzeroes_and_cost_and_check_that_the_time_is_over(char const*, std::ostream &);
template void lean::lp_core_solver_base<double, double>::restore_x(unsigned int, double const&);
template void lean::lp_core_solver_base<double, double>::set_non_basic_x_to_correct_bounds();
template void lean::lp_core_solver_base<double, double>::snap_xN_to_bounds_and_free_columns_to_zeroes();
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::snap_xN_to_bounds_and_free_columns_to_zeroes();
template void lean::lp_core_solver_base<double, double>::solve_Ax_eq_b();
template void lean::lp_core_solver_base<double, double>::solve_Bd(unsigned int);
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq>>::solve_Bd(unsigned int, indexed_vector<lean::mpq>&);
template void lean::lp_core_solver_base<double, double>::solve_yB(vector<double >&);
template bool lean::lp_core_solver_base<double, double>::update_basis_and_x(int, int, double const&);
template void lean::lp_core_solver_base<double, double>::update_x(unsigned int, const double&);
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq>::A_mult_x_is_off() const;
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq>::A_mult_x_is_off_on_index(const vector<unsigned> &) const;
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq>::basis_heading_is_correct() const ;
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::calculate_pivot_row_of_B_1(unsigned int);
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::calculate_pivot_row_when_pivot_row_of_B1_is_ready(unsigned);
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq>::column_is_dual_feasible(unsigned int) const;
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::fill_reduced_costs_from_m_y_by_rows();
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq>::find_x_by_solving();
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::init_reduced_costs_for_one_iteration();
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq>::print_statistics_with_iterations_and_nonzeroes_and_cost_and_check_that_the_time_is_over(char const*, std::ostream &);
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::restore_x(unsigned int, lean::mpq const&);
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::set_non_basic_x_to_correct_bounds();
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::solve_Ax_eq_b();
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::solve_Bd(unsigned int);
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::solve_yB(vector<lean::mpq>&);
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq>::update_basis_and_x(int, int, lean::mpq const&);
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::update_x(unsigned int, const lean::mpq&);
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::calculate_pivot_row_of_B_1(unsigned int);
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::calculate_pivot_row_when_pivot_row_of_B1_is_ready(unsigned);
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::init();
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::init_basis_heading_and_non_basic_columns_vector();
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::init_reduced_costs_for_one_iteration();
template lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::lp_core_solver_base(lean::static_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >&, vector<lean::numeric_pair<lean::mpq> >&, vector<unsigned int >&, vector<unsigned> &, vector<int> &, vector<lean::numeric_pair<lean::mpq> >&, vector<lean::mpq>&, lean::lp_settings&, const column_namer&, const vector<lean::column_type >&,
const vector<lean::numeric_pair<lean::mpq> >&,
const vector<lean::numeric_pair<lean::mpq> >&);
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::print_statistics_with_cost_and_check_that_the_time_is_over(lean::numeric_pair<lean::mpq>, std::ostream&);
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::snap_xN_to_bounds_and_fill_xB();
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_Bd(unsigned int);
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::update_basis_and_x(int, int, lean::numeric_pair<lean::mpq> const&);
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::update_x(unsigned int, const lean::numeric_pair<lean::mpq>&);
template lean::lp_core_solver_base<lean::mpq, lean::mpq>::lp_core_solver_base(
lean::static_matrix<lean::mpq, lean::mpq>&,
vector<lean::mpq>&,
template bool lp::lp_core_solver_base<double, double>::print_statistics_with_iterations_and_nonzeroes_and_cost_and_check_that_the_time_is_over(char const*, std::ostream &);
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::print_statistics_with_iterations_and_nonzeroes_and_cost_and_check_that_the_time_is_over(char const*, std::ostream &);
template void lp::lp_core_solver_base<double, double>::restore_x(unsigned int, double const&);
template void lp::lp_core_solver_base<double, double>::set_non_basic_x_to_correct_bounds();
template void lp::lp_core_solver_base<double, double>::snap_xN_to_bounds_and_free_columns_to_zeroes();
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::snap_xN_to_bounds_and_free_columns_to_zeroes();
template void lp::lp_core_solver_base<double, double>::solve_Ax_eq_b();
template void lp::lp_core_solver_base<double, double>::solve_Bd(unsigned int);
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq>>::solve_Bd(unsigned int, indexed_vector<lp::mpq>&);
template void lp::lp_core_solver_base<double, double>::solve_yB(vector<double >&);
template bool lp::lp_core_solver_base<double, double>::update_basis_and_x(int, int, double const&);
template void lp::lp_core_solver_base<double, double>::update_x(unsigned int, const double&);
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq>::A_mult_x_is_off() const;
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq>::A_mult_x_is_off_on_index(const vector<unsigned> &) const;
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq>::basis_heading_is_correct() const ;
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::calculate_pivot_row_of_B_1(unsigned int);
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::calculate_pivot_row_when_pivot_row_of_B1_is_ready(unsigned);
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq>::column_is_dual_feasible(unsigned int) const;
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::fill_reduced_costs_from_m_y_by_rows();
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq>::find_x_by_solving();
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::init_reduced_costs_for_one_iteration();
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq>::print_statistics_with_iterations_and_nonzeroes_and_cost_and_check_that_the_time_is_over(char const*, std::ostream &);
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::restore_x(unsigned int, lp::mpq const&);
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::set_non_basic_x_to_correct_bounds();
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::solve_Ax_eq_b();
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::solve_Bd(unsigned int);
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::solve_yB(vector<lp::mpq>&);
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq>::update_basis_and_x(int, int, lp::mpq const&);
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::update_x(unsigned int, const lp::mpq&);
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::calculate_pivot_row_of_B_1(unsigned int);
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::calculate_pivot_row_when_pivot_row_of_B1_is_ready(unsigned);
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::init();
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::init_basis_heading_and_non_basic_columns_vector();
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::init_reduced_costs_for_one_iteration();
template lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::lp_core_solver_base(lp::static_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >&, vector<lp::numeric_pair<lp::mpq> >&, vector<unsigned int >&, vector<unsigned> &, vector<int> &, vector<lp::numeric_pair<lp::mpq> >&, vector<lp::mpq>&, lp::lp_settings&, const column_namer&, const vector<lp::column_type >&,
const vector<lp::numeric_pair<lp::mpq> >&,
const vector<lp::numeric_pair<lp::mpq> >&);
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::print_statistics_with_cost_and_check_that_the_time_is_over(lp::numeric_pair<lp::mpq>, std::ostream&);
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::snap_xN_to_bounds_and_fill_xB();
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_Bd(unsigned int);
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::update_basis_and_x(int, int, lp::numeric_pair<lp::mpq> const&);
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::update_x(unsigned int, const lp::numeric_pair<lp::mpq>&);
template lp::lp_core_solver_base<lp::mpq, lp::mpq>::lp_core_solver_base(
lp::static_matrix<lp::mpq, lp::mpq>&,
vector<lp::mpq>&,
vector<unsigned int >&,
vector<unsigned> &, vector<int> &,
vector<lean::mpq>&,
vector<lean::mpq>&,
lean::lp_settings&,
vector<lp::mpq>&,
vector<lp::mpq>&,
lp::lp_settings&,
const column_namer&,
const vector<lean::column_type >&,
const vector<lean::mpq>&,
const vector<lean::mpq>&);
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::print_statistics_with_iterations_and_check_that_the_time_is_over(std::ostream &);
template std::string lean::lp_core_solver_base<double, double>::column_name(unsigned int) const;
template void lean::lp_core_solver_base<double, double>::pretty_print(std::ostream & out);
template void lean::lp_core_solver_base<double, double>::restore_state(double*, double*);
template void lean::lp_core_solver_base<double, double>::save_state(double*, double*);
template std::string lean::lp_core_solver_base<lean::mpq, lean::mpq>::column_name(unsigned int) const;
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::pretty_print(std::ostream & out);
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::restore_state(lean::mpq*, lean::mpq*);
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::save_state(lean::mpq*, lean::mpq*);
template std::string lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::column_name(unsigned int) const;
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::pretty_print(std::ostream & out);
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::restore_state(lean::mpq*, lean::mpq*);
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::save_state(lean::mpq*, lean::mpq*);
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_yB(vector<lean::mpq>&);
template void lean::lp_core_solver_base<double, double>::init_lu();
template void lean::lp_core_solver_base<lean::mpq, lean::mpq>::init_lu();
template int lean::lp_core_solver_base<double, double>::pivots_in_column_and_row_are_different(int, int) const;
template int lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::pivots_in_column_and_row_are_different(int, int) const;
template int lean::lp_core_solver_base<lean::mpq, lean::mpq>::pivots_in_column_and_row_are_different(int, int) const;
template bool lean::lp_core_solver_base<double, double>::calc_current_x_is_feasible_include_non_basis(void)const;
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq>::calc_current_x_is_feasible_include_non_basis(void)const;
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::calc_current_x_is_feasible_include_non_basis() const;
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::pivot_fixed_vars_from_basis();
template bool lean::lp_core_solver_base<double, double>::column_is_feasible(unsigned int) const;
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq>::column_is_feasible(unsigned int) const;
// template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::print_linear_combination_of_column_indices(vector<std::pair<lean::mpq, unsigned int>, std::allocator<std::pair<lean::mpq, unsigned int> > > const&, std::ostream&) const;
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::column_is_feasible(unsigned int) const;
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::snap_non_basic_x_to_bound();
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::init_lu();
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::A_mult_x_is_off_on_index(vector<unsigned int> const&) const;
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::find_x_by_solving();
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::restore_x(unsigned int, lean::numeric_pair<lean::mpq> const&);
template bool lean::lp_core_solver_base<double, double>::pivot_for_tableau_on_basis();
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq>::pivot_for_tableau_on_basis();
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq>>::pivot_for_tableau_on_basis();
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq>>::pivot_column_tableau(unsigned int, unsigned int);
template bool lean::lp_core_solver_base<double, double>::pivot_column_tableau(unsigned int, unsigned int);
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq>::pivot_column_tableau(unsigned int, unsigned int);
template void lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::transpose_rows_tableau(unsigned int, unsigned int);
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::inf_set_is_correct() const;
template bool lean::lp_core_solver_base<double, double>::inf_set_is_correct() const;
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq>::inf_set_is_correct() const;
template bool lean::lp_core_solver_base<lean::mpq, lean::numeric_pair<lean::mpq> >::infeasibility_costs_are_correct() const;
template bool lean::lp_core_solver_base<lean::mpq, lean::mpq >::infeasibility_costs_are_correct() const;
template bool lean::lp_core_solver_base<double, double >::infeasibility_costs_are_correct() const;
const vector<lp::column_type >&,
const vector<lp::mpq>&,
const vector<lp::mpq>&);
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::print_statistics_with_iterations_and_check_that_the_time_is_over(std::ostream &);
template std::string lp::lp_core_solver_base<double, double>::column_name(unsigned int) const;
template void lp::lp_core_solver_base<double, double>::pretty_print(std::ostream & out);
template void lp::lp_core_solver_base<double, double>::restore_state(double*, double*);
template void lp::lp_core_solver_base<double, double>::save_state(double*, double*);
template std::string lp::lp_core_solver_base<lp::mpq, lp::mpq>::column_name(unsigned int) const;
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::pretty_print(std::ostream & out);
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::restore_state(lp::mpq*, lp::mpq*);
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::save_state(lp::mpq*, lp::mpq*);
template std::string lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::column_name(unsigned int) const;
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::pretty_print(std::ostream & out);
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::restore_state(lp::mpq*, lp::mpq*);
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::save_state(lp::mpq*, lp::mpq*);
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_yB(vector<lp::mpq>&);
template void lp::lp_core_solver_base<double, double>::init_lu();
template void lp::lp_core_solver_base<lp::mpq, lp::mpq>::init_lu();
template int lp::lp_core_solver_base<double, double>::pivots_in_column_and_row_are_different(int, int) const;
template int lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::pivots_in_column_and_row_are_different(int, int) const;
template int lp::lp_core_solver_base<lp::mpq, lp::mpq>::pivots_in_column_and_row_are_different(int, int) const;
template bool lp::lp_core_solver_base<double, double>::calc_current_x_is_feasible_include_non_basis(void)const;
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq>::calc_current_x_is_feasible_include_non_basis(void)const;
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::calc_current_x_is_feasible_include_non_basis() const;
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::pivot_fixed_vars_from_basis();
template bool lp::lp_core_solver_base<double, double>::column_is_feasible(unsigned int) const;
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq>::column_is_feasible(unsigned int) const;
// template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::print_linear_combination_of_column_indices(vector<std::pair<lp::mpq, unsigned int>, std::allocator<std::pair<lp::mpq, unsigned int> > > const&, std::ostream&) const;
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::column_is_feasible(unsigned int) const;
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::snap_non_basic_x_to_bound();
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::init_lu();
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::A_mult_x_is_off_on_index(vector<unsigned int> const&) const;
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::find_x_by_solving();
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::restore_x(unsigned int, lp::numeric_pair<lp::mpq> const&);
template bool lp::lp_core_solver_base<double, double>::pivot_for_tableau_on_basis();
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq>::pivot_for_tableau_on_basis();
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq>>::pivot_for_tableau_on_basis();
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq>>::pivot_column_tableau(unsigned int, unsigned int);
template bool lp::lp_core_solver_base<double, double>::pivot_column_tableau(unsigned int, unsigned int);
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq>::pivot_column_tableau(unsigned int, unsigned int);
template void lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::transpose_rows_tableau(unsigned int, unsigned int);
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::inf_set_is_correct() const;
template bool lp::lp_core_solver_base<double, double>::inf_set_is_correct() const;
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq>::inf_set_is_correct() const;
template bool lp::lp_core_solver_base<lp::mpq, lp::numeric_pair<lp::mpq> >::infeasibility_costs_are_correct() const;
template bool lp::lp_core_solver_base<lp::mpq, lp::mpq >::infeasibility_costs_are_correct() const;
template bool lp::lp_core_solver_base<double, double >::infeasibility_costs_are_correct() const;

25
src/util/lp/lp_dual_core_solver.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/lp/static_matrix.h"
#include "util/lp/lp_core_solver_base.h"
@ -11,7 +26,7 @@
#include <algorithm>
#include "util/vector.h"
namespace lean {
namespace lp {
template <typename T, typename X>
class lp_dual_core_solver:public lp_core_solver_base<T, X> {
public:

85
src/util/lp/lp_dual_core_solver.hpp
View file

@ -1,13 +1,28 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <algorithm>
#include <string>
#include "util/vector.h"
#include "util/lp/lp_dual_core_solver.h"
namespace lean {
namespace lp {
template <typename T, typename X> void lp_dual_core_solver<T, X>::init_a_wave_by_zeros() {
unsigned j = this->m_m();
@ -23,7 +38,7 @@ template <typename T, typename X> void lp_dual_core_solver<T, X>::restore_non_ba
while (j--) {
if (this->m_basis_heading[j] >= 0 ) continue;
if (m_can_enter_basis[j]) {
lean_assert(std::find(nb.begin(), nb.end(), j) == nb.end());
SASSERT(std::find(nb.begin(), nb.end(), j) == nb.end());
nb.push_back(j);
this->m_basis_heading[j] = - static_cast<int>(nb.size());
}
@ -93,14 +108,14 @@ template <typename T, typename X> bool lp_dual_core_solver<T, X>::done() {
}
template <typename T, typename X> T lp_dual_core_solver<T, X>::get_edge_steepness_for_low_bound(unsigned p) {
lean_assert(this->m_basis_heading[p] >= 0 && static_cast<unsigned>(this->m_basis_heading[p]) < this->m_m());
SASSERT(this->m_basis_heading[p] >= 0 && static_cast<unsigned>(this->m_basis_heading[p]) < this->m_m());
T del = this->m_x[p] - this->m_low_bounds[p];
del *= del;
return del / this->m_betas[this->m_basis_heading[p]];
}
template <typename T, typename X> T lp_dual_core_solver<T, X>::get_edge_steepness_for_upper_bound(unsigned p) {
lean_assert(this->m_basis_heading[p] >= 0 && static_cast<unsigned>(this->m_basis_heading[p]) < this->m_m());
SASSERT(this->m_basis_heading[p] >= 0 && static_cast<unsigned>(this->m_basis_heading[p]) < this->m_m());
T del = this->m_x[p] - this->m_upper_bounds[p];
del *= del;
return del / this->m_betas[this->m_basis_heading[p]];
@ -135,12 +150,12 @@ template <typename T, typename X> T lp_dual_core_solver<T, X>::pricing_for_row(u
return numeric_traits<T>::zero();
break;
case column_type::free_column:
lean_assert(numeric_traits<T>::is_zero(this->m_d[p]));
SASSERT(numeric_traits<T>::is_zero(this->m_d[p]));
return numeric_traits<T>::zero();
default:
lean_unreachable();
SASSERT(false);
}
lean_unreachable();
SASSERT(false);
return numeric_traits<T>::zero();
}
@ -209,9 +224,9 @@ template <typename T, typename X> bool lp_dual_core_solver<T, X>::advance_on_kno
int pivot_compare_result = this->pivots_in_column_and_row_are_different(m_q, m_p);
if (!pivot_compare_result){;}
else if (pivot_compare_result == 2) { // the sign is changed, cannot continue
lean_unreachable(); // not implemented yet
SASSERT(false); // not implemented yet
} else {
lean_assert(pivot_compare_result == 1);
SASSERT(pivot_compare_result == 1);
this->init_lu();
}
DSE_FTran();
@ -228,21 +243,21 @@ template <typename T, typename X> int lp_dual_core_solver<T, X>::define_sign_of_
if (this->x_above_upper_bound(m_p)) {
return 1;
}
lean_unreachable();
SASSERT(false);
case column_type::low_bound:
if (this->x_below_low_bound(m_p)) {
return -1;
}
lean_unreachable();
SASSERT(false);
case column_type::upper_bound:
if (this->x_above_upper_bound(m_p)) {
return 1;
}
lean_unreachable();
SASSERT(false);
default:
lean_unreachable();
SASSERT(false);
}
lean_unreachable();
SASSERT(false);
return 0;
}
@ -250,10 +265,10 @@ template <typename T, typename X> bool lp_dual_core_solver<T, X>::can_be_breakpo
if (this->pivot_row_element_is_too_small_for_ratio_test(j)) return false;
switch (this->m_column_types[j]) {
case column_type::low_bound:
lean_assert(this->m_settings.abs_val_is_smaller_than_harris_tolerance(this->m_x[j] - this->m_low_bounds[j]));
SASSERT(this->m_settings.abs_val_is_smaller_than_harris_tolerance(this->m_x[j] - this->m_low_bounds[j]));
return m_sign_of_alpha_r * this->m_pivot_row[j] > 0;
case column_type::upper_bound:
lean_assert(this->m_settings.abs_val_is_smaller_than_harris_tolerance(this->m_x[j] - this->m_upper_bounds[j]));
SASSERT(this->m_settings.abs_val_is_smaller_than_harris_tolerance(this->m_x[j] - this->m_upper_bounds[j]));
return m_sign_of_alpha_r * this->m_pivot_row[j] < 0;
case column_type::boxed:
{
@ -292,23 +307,23 @@ template <typename T, typename X> T lp_dual_core_solver<T, X>::get_delta() {
if (this->x_above_upper_bound(m_p)) {
return this->m_x[m_p] - this->m_upper_bounds[m_p];
}
lean_unreachable();
SASSERT(false);
case column_type::low_bound:
if (this->x_below_low_bound(m_p)) {
return this->m_x[m_p] - this->m_low_bounds[m_p];
}
lean_unreachable();
SASSERT(false);
case column_type::upper_bound:
if (this->x_above_upper_bound(m_p)) {
return get_edge_steepness_for_upper_bound(m_p);
}
lean_unreachable();
SASSERT(false);
case column_type::fixed:
return this->m_x[m_p] - this->m_upper_bounds[m_p];
default:
lean_unreachable();
SASSERT(false);
}
lean_unreachable();
SASSERT(false);
return zero_of_type<T>();
}
@ -355,7 +370,7 @@ template <typename T, typename X> void lp_dual_core_solver<T, X>::update_betas()
template <typename T, typename X> void lp_dual_core_solver<T, X>::apply_flips() {
for (unsigned j : m_flipped_boxed) {
lean_assert(this->x_is_at_bound(j));
SASSERT(this->x_is_at_bound(j));
if (this->x_is_at_low_bound(j)) {
this->m_x[j] = this->m_upper_bounds[j];
} else {
@ -385,7 +400,7 @@ template <typename T, typename X> void lp_dual_core_solver<T, X>::snap_xN_column
case column_type::free_column:
break;
default:
lean_unreachable();
SASSERT(false);
}
}
@ -441,7 +456,7 @@ template <typename T, typename X> bool lp_dual_core_solver<T, X>::basis_change_a
return false;
}
lean_assert(d_is_correct());
SASSERT(d_is_correct());
return true;
}
@ -457,7 +472,7 @@ template <typename T, typename X> void lp_dual_core_solver<T, X>::recover_leavin
case free_of_bounds:
this->m_x[m_q] = zero_of_type<X>();
default:
lean_unreachable();
SASSERT(false);
}
}
@ -584,7 +599,7 @@ template <typename T, typename X> bool lp_dual_core_solver<T, X>::tight_breakpoi
template <typename T, typename X> T lp_dual_core_solver<T, X>::calculate_harris_delta_on_breakpoint_set() {
bool first_time = true;
T ret = zero_of_type<T>();
lean_assert(m_breakpoint_set.size() > 0);
SASSERT(m_breakpoint_set.size() > 0);
for (auto j : m_breakpoint_set) {
T t;
if (this->x_is_at_low_bound(j)) {
@ -633,7 +648,7 @@ template <typename T, typename X> void lp_dual_core_solver<T, X>::find_q_on_tigh
}
}
m_tight_set.erase(m_q);
lean_assert(m_q != -1);
SASSERT(m_q != -1);
}
template <typename T, typename X> void lp_dual_core_solver<T, X>::find_q_and_tight_set() {
@ -722,13 +737,13 @@ template <typename T, typename X> void lp_dual_core_solver<T, X>::one_iteration(
this->set_status(FEASIBLE);
}
pricing_loop(number_of_rows_to_try, offset_in_rows);
lean_assert(problem_is_dual_feasible());
SASSERT(problem_is_dual_feasible());
}
template <typename T, typename X> void lp_dual_core_solver<T, X>::solve() { // see the page 35
lean_assert(d_is_correct());
lean_assert(problem_is_dual_feasible());
lean_assert(this->basis_heading_is_correct());
SASSERT(d_is_correct());
SASSERT(problem_is_dual_feasible());
SASSERT(this->basis_heading_is_correct());
this->set_total_iterations(0);
this->iters_with_no_cost_growing() = 0;
do {

45
src/util/lp/lp_dual_core_solver_instances.cpp
View file

@ -1,29 +1,44 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <utility>
#include <memory>
#include <string>
#include "util/vector.h"
#include <functional>
#include "util/lp/lp_dual_core_solver.hpp"
template void lean::lp_dual_core_solver<lean::mpq, lean::mpq>::start_with_initial_basis_and_make_it_dual_feasible();
template void lean::lp_dual_core_solver<lean::mpq, lean::mpq>::solve();
template lean::lp_dual_core_solver<double, double>::lp_dual_core_solver(lean::static_matrix<double, double>&, vector<bool>&,
template void lp::lp_dual_core_solver<lp::mpq, lp::mpq>::start_with_initial_basis_and_make_it_dual_feasible();
template void lp::lp_dual_core_solver<lp::mpq, lp::mpq>::solve();
template lp::lp_dual_core_solver<double, double>::lp_dual_core_solver(lp::static_matrix<double, double>&, vector<bool>&,
vector<double>&,
vector<double>&,
vector<unsigned int>&,
vector<unsigned> &,
vector<int> &,
vector<double>&,
vector<lean::column_type>&,
vector<lp::column_type>&,
vector<double>&,
vector<double>&,
lean::lp_settings&, const lean::column_namer&);
template void lean::lp_dual_core_solver<double, double>::start_with_initial_basis_and_make_it_dual_feasible();
template void lean::lp_dual_core_solver<double, double>::solve();
template void lean::lp_dual_core_solver<lean::mpq, lean::mpq>::restore_non_basis();
template void lean::lp_dual_core_solver<double, double>::restore_non_basis();
template void lean::lp_dual_core_solver<double, double>::revert_to_previous_basis();
template void lean::lp_dual_core_solver<lean::mpq, lean::mpq>::revert_to_previous_basis();
lp::lp_settings&, const lp::column_namer&);
template void lp::lp_dual_core_solver<double, double>::start_with_initial_basis_and_make_it_dual_feasible();
template void lp::lp_dual_core_solver<double, double>::solve();
template void lp::lp_dual_core_solver<lp::mpq, lp::mpq>::restore_non_basis();
template void lp::lp_dual_core_solver<double, double>::restore_non_basis();
template void lp::lp_dual_core_solver<double, double>::revert_to_previous_basis();
template void lp::lp_dual_core_solver<lp::mpq, lp::mpq>::revert_to_previous_basis();

25
src/util/lp/lp_dual_simplex.h
View file

@ -1,13 +1,28 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
#include "util/lp/lp_utils.h"
#include "util/lp/lp_solver.h"
#include "util/lp/lp_dual_core_solver.h"
namespace lean {
namespace lp {
template <typename T, typename X>
class lp_dual_simplex: public lp_solver<T, X> {

53
src/util/lp/lp_dual_simplex.hpp
View file

@ -1,9 +1,24 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/lp_dual_simplex.h"
namespace lean{
namespace lp{
template <typename T, typename X> void lp_dual_simplex<T, X>::decide_on_status_after_stage1() {
switch (m_core_solver->get_status()) {
@ -15,7 +30,7 @@ template <typename T, typename X> void lp_dual_simplex<T, X>::decide_on_status_a
}
break;
case DUAL_UNBOUNDED:
lean_unreachable();
SASSERT(false);
case ITERATIONS_EXHAUSTED:
this->m_status = ITERATIONS_EXHAUSTED;
break;
@ -26,12 +41,12 @@ template <typename T, typename X> void lp_dual_simplex<T, X>::decide_on_status_a
this->m_status = FLOATING_POINT_ERROR;
break;
default:
lean_unreachable();
SASSERT(false);
}
}
template <typename T, typename X> void lp_dual_simplex<T, X>::fix_logical_for_stage2(unsigned j) {
lean_assert(j >= this->number_of_core_structurals());
SASSERT(j >= this->number_of_core_structurals());
switch (m_column_types_of_logicals[j - this->number_of_core_structurals()]) {
case column_type::low_bound:
m_low_bounds[j] = numeric_traits<T>::zero();
@ -44,7 +59,7 @@ template <typename T, typename X> void lp_dual_simplex<T, X>::fix_logical_for_st
m_can_enter_basis[j] = false;
break;
default:
lean_unreachable();
SASSERT(false);
}
}
@ -58,7 +73,7 @@ template <typename T, typename X> void lp_dual_simplex<T, X>::fix_structural_for
break;
case column_type::fixed:
case column_type::upper_bound:
lean_unreachable();
SASSERT(false);
case column_type::boxed:
this->m_upper_bounds[j] = ci->get_adjusted_upper_bound() / this->m_column_scale[j];
m_low_bounds[j] = numeric_traits<T>::zero();
@ -70,7 +85,7 @@ template <typename T, typename X> void lp_dual_simplex<T, X>::fix_structural_for
m_column_types_of_core_solver[j] = column_type::free_column;
break;
default:
lean_unreachable();
SASSERT(false);
}
// T cost_was = this->m_costs[j];
this->set_scaled_cost(j);
@ -115,7 +130,7 @@ template <typename T, typename X> void lp_dual_simplex<T, X>::solve_for_stage2()
this->m_status = FLOATING_POINT_ERROR;
break;
default:
lean_unreachable();
SASSERT(false);
}
this->m_second_stage_iterations = m_core_solver->total_iterations();
this->m_total_iterations = (this->m_first_stage_iterations + this->m_second_stage_iterations);
@ -129,7 +144,7 @@ template <typename T, typename X> void lp_dual_simplex<T, X>::fill_x_with_zeros(
}
template <typename T, typename X> void lp_dual_simplex<T, X>::stage1() {
lean_assert(m_core_solver == nullptr);
SASSERT(m_core_solver == nullptr);
this->m_x.resize(this->m_A->column_count(), numeric_traits<T>::zero());
if (this->m_settings.get_message_ostream() != nullptr)
this->print_statistics_on_A(*this->m_settings.get_message_ostream());
@ -177,7 +192,7 @@ template <typename T, typename X> void lp_dual_simplex<T, X>::fill_first_stage_s
}
template <typename T, typename X> column_type lp_dual_simplex<T, X>::get_column_type(unsigned j) {
lean_assert(j < this->m_A->column_count());
SASSERT(j < this->m_A->column_count());
if (j >= this->number_of_core_structurals()) {
return m_column_types_of_logicals[j - this->number_of_core_structurals()];
}
@ -186,12 +201,12 @@ template <typename T, typename X> column_type lp_dual_simplex<T, X>::get_column_
template <typename T, typename X> void lp_dual_simplex<T, X>::fill_costs_bounds_types_and_can_enter_basis_for_the_first_stage_solver_structural_column(unsigned j) {
// see 4.7 in the dissertation of Achim Koberstein
lean_assert(this->m_core_solver_columns_to_external_columns.find(j) !=
SASSERT(this->m_core_solver_columns_to_external_columns.find(j) !=
this->m_core_solver_columns_to_external_columns.end());
T free_bound = T(1e4); // see 4.8
unsigned jj = this->m_core_solver_columns_to_external_columns[j];
lean_assert(this->m_map_from_var_index_to_column_info.find(jj) != this->m_map_from_var_index_to_column_info.end());
SASSERT(this->m_map_from_var_index_to_column_info.find(jj) != this->m_map_from_var_index_to_column_info.end());
column_info<T> * ci = this->m_map_from_var_index_to_column_info[jj];
switch (ci->get_column_type()) {
case column_type::upper_bound: {
@ -221,14 +236,14 @@ template <typename T, typename X> void lp_dual_simplex<T, X>::fill_costs_bounds_
this->m_upper_bounds[j] = this->m_low_bounds[j] = numeric_traits<T>::zero(); // is it needed?
break;
default:
lean_unreachable();
SASSERT(false);
}
m_column_types_of_core_solver[j] = column_type::boxed;
}
template <typename T, typename X> void lp_dual_simplex<T, X>::fill_costs_bounds_types_and_can_enter_basis_for_the_first_stage_solver_logical_column(unsigned j) {
this->m_costs[j] = 0;
lean_assert(get_column_type(j) != column_type::upper_bound);
SASSERT(get_column_type(j) != column_type::upper_bound);
if ((m_can_enter_basis[j] = (get_column_type(j) == column_type::low_bound))) {
m_column_types_of_core_solver[j] = column_type::boxed;
this->m_low_bounds[j] = numeric_traits<T>::zero();
@ -254,7 +269,7 @@ template <typename T, typename X> void lp_dual_simplex<T, X>::fill_costs_and_bou
template <typename T, typename X> void lp_dual_simplex<T, X>::fill_first_stage_solver_fields_for_row_slack_and_artificial(unsigned row,
unsigned & slack_var,
unsigned & artificial) {
lean_assert(row < this->row_count());
SASSERT(row < this->row_count());
auto & constraint = this->m_constraints[this->m_core_solver_rows_to_external_rows[row]];
// we need to bring the program to the form Ax = b
T rs = this->m_b[row];

31
src/util/lp/lp_dual_simplex_instances.cpp
View file

@ -1,9 +1,24 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/lp_dual_simplex.hpp"
template lean::mpq lean::lp_dual_simplex<lean::mpq, lean::mpq>::get_current_cost() const;
template void lean::lp_dual_simplex<lean::mpq, lean::mpq>::find_maximal_solution();
template double lean::lp_dual_simplex<double, double>::get_current_cost() const;
template void lean::lp_dual_simplex<double, double>::find_maximal_solution();
template lp::mpq lp::lp_dual_simplex<lp::mpq, lp::mpq>::get_current_cost() const;
template void lp::lp_dual_simplex<lp::mpq, lp::mpq>::find_maximal_solution();
template double lp::lp_dual_simplex<double, double>::get_current_cost() const;
template void lp::lp_dual_simplex<double, double>::find_maximal_solution();

105
src/util/lp/lp_primal_core_solver.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include <list>
@ -23,7 +38,7 @@
#include "util/lp/binary_heap_priority_queue.h"
#include "util/lp/int_set.h"
#include "util/lp/iterator_on_row.h"
namespace lean {
namespace lp {
// This core solver solves (Ax=b, low_bound_values \leq x \leq upper_bound_values, maximize costs*x )
// The right side b is given implicitly by x and the basis
@ -70,7 +85,7 @@ public:
// unsigned len = 100000000;
// for (unsigned j : this->m_inf_set.m_index) {
// int i = this->m_basis_heading[j];
// lean_assert(i >= 0);
// SASSERT(i >= 0);
// unsigned row_len = this->m_A.m_rows[i].size();
// if (row_len < len) {
// choices.clear();
@ -98,8 +113,8 @@ public:
bool column_is_benefitial_for_entering_basis_on_sign_row_strategy(unsigned j, int sign) const {
// sign = 1 means the x of the basis column of the row has to grow to become feasible, when the coeff before j is neg, or x - has to diminish when the coeff is pos
// we have xbj = -aj * xj
lean_assert(this->m_basis_heading[j] < 0);
lean_assert(this->column_is_feasible(j));
SASSERT(this->m_basis_heading[j] < 0);
SASSERT(this->column_is_feasible(j));
switch (this->m_column_types[j]) {
case column_type::free_column: return true;
case column_type::fixed: return false;
@ -117,13 +132,13 @@ public:
return !this->x_is_at_upper_bound(j);
}
lean_assert(false); // cannot be here
SASSERT(false); // cannot be here
return false;
}
bool needs_to_grow(unsigned bj) const {
lean_assert(!this->column_is_feasible(bj));
SASSERT(!this->column_is_feasible(bj));
switch(this->m_column_types[bj]) {
case column_type::free_column:
return false;
@ -134,12 +149,12 @@ public:
default:
return false;
}
lean_assert(false); // unreachable
SASSERT(false); // unreachable
return false;
}
int inf_sign_of_column(unsigned bj) const {
lean_assert(!this->column_is_feasible(bj));
SASSERT(!this->column_is_feasible(bj));
switch(this->m_column_types[bj]) {
case column_type::free_column:
return 0;
@ -151,7 +166,7 @@ public:
default:
return -1;
}
lean_assert(false); // unreachable
SASSERT(false); // unreachable
return 0;
}
@ -159,7 +174,7 @@ public:
bool monoid_can_decrease(const row_cell<T> & rc) const {
unsigned j = rc.m_j;
lean_assert(this->column_is_feasible(j));
SASSERT(this->column_is_feasible(j));
switch (this->m_column_types[j]) {
case column_type::free_column:
return true;
@ -186,13 +201,13 @@ public:
default:
return false;
}
lean_assert(false); // unreachable
SASSERT(false); // unreachable
return false;
}
bool monoid_can_increase(const row_cell<T> & rc) const {
unsigned j = rc.m_j;
lean_assert(this->column_is_feasible(j));
SASSERT(this->column_is_feasible(j));
switch (this->m_column_types[j]) {
case column_type::free_column:
return true;
@ -219,7 +234,7 @@ public:
default:
return false;
}
lean_assert(false); // unreachable
SASSERT(false); // unreachable
return false;
}
@ -329,24 +344,24 @@ public:
}
void limit_theta_on_basis_column_for_inf_case_m_neg_upper_bound(unsigned j, const T & m, X & theta, bool & unlimited) {
lean_assert(m < 0 && this->m_column_types[j] == column_type::upper_bound);
SASSERT(m < 0 && this->m_column_types[j] == column_type::upper_bound);
limit_inf_on_upper_bound_m_neg(m, this->m_x[j], this->m_upper_bounds[j], theta, unlimited);
}
void limit_theta_on_basis_column_for_inf_case_m_neg_low_bound(unsigned j, const T & m, X & theta, bool & unlimited) {
lean_assert(m < 0 && this->m_column_types[j] == column_type::low_bound);
SASSERT(m < 0 && this->m_column_types[j] == column_type::low_bound);
limit_inf_on_bound_m_neg(m, this->m_x[j], this->m_low_bounds[j], theta, unlimited);
}
void limit_theta_on_basis_column_for_inf_case_m_pos_low_bound(unsigned j, const T & m, X & theta, bool & unlimited) {
lean_assert(m > 0 && this->m_column_types[j] == column_type::low_bound);
SASSERT(m > 0 && this->m_column_types[j] == column_type::low_bound);
limit_inf_on_low_bound_m_pos(m, this->m_x[j], this->m_low_bounds[j], theta, unlimited);
}
void limit_theta_on_basis_column_for_inf_case_m_pos_upper_bound(unsigned j, const T & m, X & theta, bool & unlimited) {
lean_assert(m > 0 && this->m_column_types[j] == column_type::upper_bound);
SASSERT(m > 0 && this->m_column_types[j] == column_type::upper_bound);
limit_inf_on_bound_m_pos(m, this->m_x[j], this->m_upper_bounds[j], theta, unlimited);
};
@ -359,7 +374,7 @@ public:
X get_max_bound(vector<X> & b);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
void check_Ax_equal_b();
void check_the_bounds();
void check_bound(unsigned i);
@ -388,7 +403,7 @@ public:
bool need_to_switch_costs() const {
if (this->m_settings.simplex_strategy() == simplex_strategy_enum::tableau_rows)
return false;
// lean_assert(calc_current_x_is_feasible() == current_x_is_feasible());
// SASSERT(calc_current_x_is_feasible() == current_x_is_feasible());
return this->current_x_is_feasible() == this->m_using_infeas_costs;
}
@ -443,7 +458,7 @@ public:
if (j == -1)
return -1;
lean_assert(!this->column_is_feasible(j));
SASSERT(!this->column_is_feasible(j));
switch (this->m_column_types[j]) {
case column_type::fixed:
case column_type::upper_bound:
@ -459,7 +474,7 @@ public:
new_val_for_leaving = this->m_low_bounds[j];
break;
default:
lean_assert(false);
SASSERT(false);
new_val_for_leaving = numeric_traits<T>::zero(); // does not matter
}
return j;
@ -490,7 +505,7 @@ public:
}
X theta = (this->m_x[leaving] - new_val_for_leaving) / a_ent;
advance_on_entering_and_leaving_tableau_rows(entering, leaving, theta );
lean_assert(this->m_x[leaving] == new_val_for_leaving);
SASSERT(this->m_x[leaving] == new_val_for_leaving);
if (this->current_x_is_feasible())
this->set_status(OPTIMAL);
}
@ -507,13 +522,13 @@ public:
void update_basis_and_x_with_comparison(unsigned entering, unsigned leaving, X delta);
void decide_on_status_when_cannot_find_entering() {
lean_assert(!need_to_switch_costs());
SASSERT(!need_to_switch_costs());
this->set_status(this->current_x_is_feasible()? OPTIMAL: INFEASIBLE);
}
// void limit_theta_on_basis_column_for_feas_case_m_neg(unsigned j, const T & m, X & theta) {
// lean_assert(m < 0);
// lean_assert(this->m_column_type[j] == low_bound || this->m_column_type[j] == boxed);
// SASSERT(m < 0);
// SASSERT(this->m_column_type[j] == low_bound || this->m_column_type[j] == boxed);
// const X & eps = harris_eps_for_bound(this->m_low_bounds[j]);
// if (this->above_bound(this->m_x[j], this->m_low_bounds[j])) {
// theta = std::min((this->m_low_bounds[j] -this->m_x[j] - eps) / m, theta);
@ -522,7 +537,7 @@ public:
// }
void limit_theta_on_basis_column_for_feas_case_m_neg_no_check(unsigned j, const T & m, X & theta, bool & unlimited) {
lean_assert(m < 0);
SASSERT(m < 0);
const X& eps = harris_eps_for_bound(this->m_low_bounds[j]);
limit_theta((this->m_low_bounds[j] - this->m_x[j] - eps) / m, theta, unlimited);
if (theta < zero_of_type<X>()) theta = zero_of_type<X>();
@ -530,7 +545,7 @@ public:
bool limit_inf_on_bound_m_neg(const T & m, const X & x, const X & bound, X & theta, bool & unlimited) {
// x gets smaller
lean_assert(m < 0);
SASSERT(m < 0);
if (numeric_traits<T>::precise()) {
if (this->below_bound(x, bound)) return false;
if (this->above_bound(x, bound)) {
@ -554,7 +569,7 @@ public:
bool limit_inf_on_bound_m_pos(const T & m, const X & x, const X & bound, X & theta, bool & unlimited) {
// x gets larger
lean_assert(m > 0);
SASSERT(m > 0);
if (numeric_traits<T>::precise()) {
if (this->above_bound(x, bound)) return false;
if (this->below_bound(x, bound)) {
@ -579,14 +594,14 @@ public:
void limit_inf_on_low_bound_m_pos(const T & m, const X & x, const X & bound, X & theta, bool & unlimited) {
if (numeric_traits<T>::precise()) {
// x gets larger
lean_assert(m > 0);
SASSERT(m > 0);
if (this->below_bound(x, bound)) {
limit_theta((bound - x) / m, theta, unlimited);
}
}
else {
// x gets larger
lean_assert(m > 0);
SASSERT(m > 0);
const X& eps = harris_eps_for_bound(bound);
if (this->below_bound(x, bound)) {
limit_theta((bound - x + eps) / m, theta, unlimited);
@ -596,7 +611,7 @@ public:
void limit_inf_on_upper_bound_m_neg(const T & m, const X & x, const X & bound, X & theta, bool & unlimited) {
// x gets smaller
lean_assert(m < 0);
SASSERT(m < 0);
const X& eps = harris_eps_for_bound(bound);
if (this->above_bound(x, bound)) {
limit_theta((bound - x - eps) / m, theta, unlimited);
@ -604,7 +619,7 @@ public:
}
void limit_theta_on_basis_column_for_inf_case_m_pos_boxed(unsigned j, const T & m, X & theta, bool & unlimited) {
// lean_assert(m > 0 && this->m_column_type[j] == column_type::boxed);
// SASSERT(m > 0 && this->m_column_type[j] == column_type::boxed);
const X & x = this->m_x[j];
const X & lbound = this->m_low_bounds[j];
@ -624,7 +639,7 @@ public:
}
void limit_theta_on_basis_column_for_inf_case_m_neg_boxed(unsigned j, const T & m, X & theta, bool & unlimited) {
// lean_assert(m < 0 && this->m_column_type[j] == column_type::boxed);
// SASSERT(m < 0 && this->m_column_type[j] == column_type::boxed);
const X & x = this->m_x[j];
const X & ubound = this->m_upper_bounds[j];
if (this->above_bound(x, ubound)) {
@ -642,7 +657,7 @@ public:
}
}
void limit_theta_on_basis_column_for_feas_case_m_pos(unsigned j, const T & m, X & theta, bool & unlimited) {
lean_assert(m > 0);
SASSERT(m > 0);
const T& eps = harris_eps_for_bound(this->m_upper_bounds[j]);
if (this->below_bound(this->m_x[j], this->m_upper_bounds[j])) {
limit_theta((this->m_upper_bounds[j] - this->m_x[j] + eps) / m, theta, unlimited);
@ -654,7 +669,7 @@ public:
}
void limit_theta_on_basis_column_for_feas_case_m_pos_no_check(unsigned j, const T & m, X & theta, bool & unlimited ) {
lean_assert(m > 0);
SASSERT(m > 0);
const X& eps = harris_eps_for_bound(this->m_upper_bounds[j]);
limit_theta( (this->m_upper_bounds[j] - this->m_x[j] + eps) / m, theta, unlimited);
if (theta < zero_of_type<X>()) {
@ -720,7 +735,7 @@ public:
break;
default:
lean_unreachable();
SASSERT(false);
}
if (!unlimited && theta < zero_of_type<X>()) {
theta = zero_of_type<X>();
@ -803,7 +818,7 @@ public:
case column_type::free_column:
return 0;
default:
lean_assert(false);
SASSERT(false);
}
return 0;
}
@ -838,7 +853,7 @@ public:
return -1;
break;
default:
lean_assert(false);
SASSERT(false);
}
return 0;
@ -864,7 +879,7 @@ public:
// the delta is between the old and the new cost (old - new)
void update_reduced_cost_for_basic_column_cost_change(const T & delta, unsigned j) {
lean_assert(this->m_basis_heading[j] >= 0);
SASSERT(this->m_basis_heading[j] >= 0);
unsigned i = static_cast<unsigned>(this->m_basis_heading[j]);
for (const row_cell<T> & rc : this->m_A.m_rows[i]) {
unsigned k = rc.m_j;
@ -943,10 +958,10 @@ public:
upper_bound_values),
m_beta(A.row_count()),
m_converted_harris_eps(convert_struct<T, double>::convert(this->m_settings.harris_feasibility_tolerance)) {
lean_assert(initial_x_is_correct());
SASSERT(initial_x_is_correct());
m_low_bounds_dummy.resize(A.column_count(), zero_of_type<T>());
m_enter_price_eps = numeric_traits<T>::precise() ? numeric_traits<T>::zero() : T(1e-5);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// check_correctness();
#endif
}

157
src/util/lp/lp_primal_core_solver.hpp
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <list>
#include "util/vector.h"
#include <fstream>
@ -9,13 +24,13 @@
#include <set>
#include <string>
#include "util/lp/lp_primal_core_solver.h"
namespace lean {
namespace lp {
// This core solver solves (Ax=b, low_bound_values \leq x \leq upper_bound_values, maximize costs*x )
// The right side b is given implicitly by x and the basis
template <typename T, typename X>
void lp_primal_core_solver<T, X>::sort_non_basis_rational() {
lean_assert(numeric_traits<T>::precise());
SASSERT(numeric_traits<T>::precise());
if (this->m_settings.use_tableau()) {
std::sort(this->m_nbasis.begin(), this->m_nbasis.end(), [this](unsigned a, unsigned b) {
unsigned ca = this->m_A.number_of_non_zeroes_in_column(a);
@ -70,11 +85,11 @@ bool lp_primal_core_solver<T, X>::column_is_benefitial_for_entering_on_breakpoin
const T & d = this->m_d[j];
switch (this->m_column_types[j]) {
case column_type::low_bound:
lean_assert(this->x_is_at_low_bound(j));
SASSERT(this->x_is_at_low_bound(j));
ret = d < -m_epsilon_of_reduced_cost;
break;
case column_type::upper_bound:
lean_assert(this->x_is_at_upper_bound(j));
SASSERT(this->x_is_at_upper_bound(j));
ret = d > m_epsilon_of_reduced_cost;
break;
case column_type::fixed:
@ -83,7 +98,7 @@ bool lp_primal_core_solver<T, X>::column_is_benefitial_for_entering_on_breakpoin
case column_type::boxed:
{
bool low_bound = this->x_is_at_low_bound(j);
lean_assert(low_bound || this->x_is_at_upper_bound(j));
SASSERT(low_bound || this->x_is_at_upper_bound(j));
ret = (low_bound && d < -m_epsilon_of_reduced_cost) || ((!low_bound) && d > m_epsilon_of_reduced_cost);
}
break;
@ -91,7 +106,7 @@ bool lp_primal_core_solver<T, X>::column_is_benefitial_for_entering_on_breakpoin
ret = d > m_epsilon_of_reduced_cost || d < - m_epsilon_of_reduced_cost;
break;
default:
lean_unreachable();
SASSERT(false);
ret = false;
break;
}
@ -127,14 +142,14 @@ bool lp_primal_core_solver<T, X>::column_is_benefitial_for_entering_basis(unsign
}
break;
default:
lean_unreachable();
SASSERT(false);
break;
}
return false;
}
template <typename T, typename X>
bool lp_primal_core_solver<T, X>::column_is_benefitial_for_entering_basis_precise(unsigned j) const {
lean_assert (numeric_traits<T>::precise());
SASSERT (numeric_traits<T>::precise());
if (this->m_using_infeas_costs && this->m_settings.use_breakpoints_in_feasibility_search)
return column_is_benefitial_for_entering_on_breakpoints(j);
const T& dj = this->m_d[j];
@ -167,7 +182,7 @@ bool lp_primal_core_solver<T, X>::column_is_benefitial_for_entering_basis_precis
}
break;
default:
lean_unreachable();
SASSERT(false);
break;
}
return false;
@ -175,7 +190,7 @@ bool lp_primal_core_solver<T, X>::column_is_benefitial_for_entering_basis_precis
template <typename T, typename X>
int lp_primal_core_solver<T, X>::choose_entering_column_presize(unsigned number_of_benefitial_columns_to_go_over) { // at this moment m_y = cB * B(-1)
lean_assert(numeric_traits<T>::precise());
SASSERT(numeric_traits<T>::precise());
if (number_of_benefitial_columns_to_go_over == 0)
return -1;
if (this->m_basis_sort_counter == 0) {
@ -259,7 +274,7 @@ int lp_primal_core_solver<T, X>::choose_entering_column(unsigned number_of_benef
template <typename T, typename X> int lp_primal_core_solver<T, X>::advance_on_sorted_breakpoints(unsigned entering, X &t) {
T slope_at_entering = this->m_d[entering];
breakpoint<X> * last_bp = nullptr;
lean_assert(m_breakpoint_indices_queue.is_empty()==false);
SASSERT(m_breakpoint_indices_queue.is_empty()==false);
while (m_breakpoint_indices_queue.is_empty() == false) {
unsigned bi = m_breakpoint_indices_queue.dequeue();
breakpoint<X> *b = &m_breakpoints[bi];
@ -274,7 +289,7 @@ template <typename T, typename X> int lp_primal_core_solver<T, X>::advance_on_so
}
}
}
lean_assert (last_bp != nullptr);
SASSERT (last_bp != nullptr);
t = last_bp->m_delta;
return last_bp->m_j;
}
@ -282,13 +297,13 @@ template <typename T, typename X> int lp_primal_core_solver<T, X>::advance_on_so
template <typename T, typename X> int
lp_primal_core_solver<T, X>::find_leaving_and_t_with_breakpoints(unsigned entering, X & t){
lean_assert(this->precise() == false);
SASSERT(this->precise() == false);
fill_breakpoints_array(entering);
return advance_on_sorted_breakpoints(entering, t);
}
template <typename T, typename X> bool lp_primal_core_solver<T, X>::get_harris_theta(X & theta) {
lean_assert(this->m_ed.is_OK());
SASSERT(this->m_ed.is_OK());
bool unlimited = true;
for (unsigned i : this->m_ed.m_index) {
if (this->m_settings.abs_val_is_smaller_than_pivot_tolerance(this->m_ed[i])) continue;
@ -345,13 +360,13 @@ template <typename T, typename X> bool lp_primal_core_solver<T, X>::try_jump_to_
if (m_sign_of_entering_delta > 0) {
t = this->m_upper_bounds[entering] - this->m_x[entering];
if (unlimited || t <= theta){
lean_assert(t >= zero_of_type<X>());
SASSERT(t >= zero_of_type<X>());
return true;
}
} else { // m_sign_of_entering_delta == -1
t = this->m_x[entering] - this->m_low_bounds[entering];
if (unlimited || t <= theta) {
lean_assert(t >= zero_of_type<X>());
SASSERT(t >= zero_of_type<X>());
return true;
}
}
@ -360,7 +375,7 @@ template <typename T, typename X> bool lp_primal_core_solver<T, X>::try_jump_to_
if (m_sign_of_entering_delta > 0) {
t = this->m_upper_bounds[entering] - this->m_x[entering];
if (unlimited || t <= theta){
lean_assert(t >= zero_of_type<X>());
SASSERT(t >= zero_of_type<X>());
return true;
}
}
@ -369,7 +384,7 @@ template <typename T, typename X> bool lp_primal_core_solver<T, X>::try_jump_to_
if (m_sign_of_entering_delta < 0) {
t = this->m_x[entering] - this->m_low_bounds[entering];
if (unlimited || t <= theta) {
lean_assert(t >= zero_of_type<X>());
SASSERT(t >= zero_of_type<X>());
return true;
}
}
@ -405,7 +420,7 @@ template <typename T, typename X> int lp_primal_core_solver<T, X>::find_leaving_
do {
unsigned i = this->m_ed.m_index[k];
const T & ed = this->m_ed[i];
lean_assert(!numeric_traits<T>::is_zero(ed));
SASSERT(!numeric_traits<T>::is_zero(ed));
unsigned j = this->m_basis[i];
limit_theta_on_basis_column(j, - ed * m_sign_of_entering_delta, t, unlimited);
if (!unlimited) {
@ -424,7 +439,7 @@ template <typename T, typename X> int lp_primal_core_solver<T, X>::find_leaving_
while (k != initial_k) {
unsigned i = this->m_ed.m_index[k];
const T & ed = this->m_ed[i];
lean_assert(!numeric_traits<T>::is_zero(ed));
SASSERT(!numeric_traits<T>::is_zero(ed));
unsigned j = this->m_basis[i];
unlimited = true;
limit_theta_on_basis_column(j, -ed * m_sign_of_entering_delta, ratio, unlimited);
@ -464,7 +479,7 @@ template <typename T, typename X> int lp_primal_core_solver<T, X>::find_leavi
return find_leaving_and_t_with_breakpoints(entering, t);
X theta;
bool unlimited = get_harris_theta(theta);
lean_assert(unlimited || theta >= zero_of_type<X>());
SASSERT(unlimited || theta >= zero_of_type<X>());
if (try_jump_to_another_bound_on_entering(entering, theta, t, unlimited)) return entering;
if (unlimited)
return -1;
@ -529,11 +544,11 @@ template <typename T, typename X> X lp_primal_core_solver<T, X>::get_max_boun
return ret;
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X> void lp_primal_core_solver<T, X>::check_Ax_equal_b() {
dense_matrix<T, X> d(this->m_A);
T * ls = d.apply_from_left_with_different_dims(this->m_x);
lean_assert(vectors_are_equal<T>(ls, this->m_b, this->m_m()));
SASSERT(vectors_are_equal<T>(ls, this->m_b, this->m_m()));
delete [] ls;
}
template <typename T, typename X> void lp_primal_core_solver<T, X>::check_the_bounds() {
@ -543,8 +558,8 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::check_the
}
template <typename T, typename X> void lp_primal_core_solver<T, X>::check_bound(unsigned i) {
lean_assert (!(this->column_has_low_bound(i) && (numeric_traits<T>::zero() > this->m_x[i])));
lean_assert (!(this->column_has_upper_bound(i) && (this->m_upper_bounds[i] < this->m_x[i])));
SASSERT (!(this->column_has_low_bound(i) && (numeric_traits<T>::zero() > this->m_x[i])));
SASSERT (!(this->column_has_upper_bound(i) && (this->m_upper_bounds[i] < this->m_x[i])));
}
template <typename T, typename X> void lp_primal_core_solver<T, X>::check_correctness() {
@ -558,10 +573,10 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::check_cor
template <typename T, typename X>
void lp_primal_core_solver<T, X>::update_reduced_costs_from_pivot_row(unsigned entering, unsigned leaving) {
// the basis heading has changed already
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
auto & basis_heading = this->m_basis_heading;
lean_assert(basis_heading[entering] >= 0 && static_cast<unsigned>(basis_heading[entering]) < this->m_m());
lean_assert(basis_heading[leaving] < 0);
SASSERT(basis_heading[entering] >= 0 && static_cast<unsigned>(basis_heading[entering]) < this->m_m());
SASSERT(basis_heading[leaving] < 0);
#endif
T pivot = this->m_pivot_row[entering];
T dq = this->m_d[entering]/pivot;
@ -584,7 +599,7 @@ void lp_primal_core_solver<T, X>::update_reduced_costs_from_pivot_row(unsigned e
template <typename T, typename X> int lp_primal_core_solver<T, X>::refresh_reduced_cost_at_entering_and_check_that_it_is_off(unsigned entering) {
if (numeric_traits<T>::precise()) return 0;
T reduced_at_entering_was = this->m_d[entering]; // can benefit from going over non-zeros of m_ed
lean_assert(abs(reduced_at_entering_was) > m_epsilon_of_reduced_cost);
SASSERT(abs(reduced_at_entering_was) > m_epsilon_of_reduced_cost);
T refreshed_cost = this->m_costs[entering];
unsigned i = this->m_m();
while (i--) refreshed_cost -= this->m_costs[this->m_basis[i]] * this->m_ed[i];
@ -619,7 +634,7 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::backup_an
m_costs_backup = this->m_costs;
} else {
T cost_max = std::max(max_abs_in_vector(this->m_costs), T(1));
lean_assert(m_costs_backup.size() == 0);
SASSERT(m_costs_backup.size() == 0);
for (unsigned j = 0; j < this->m_costs.size(); j++)
m_costs_backup.push_back(this->m_costs[j] /= cost_max);
}
@ -649,16 +664,16 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::init_run(
template <typename T, typename X> void lp_primal_core_solver<T, X>::calc_working_vector_beta_for_column_norms(){
lean_assert(numeric_traits<T>::precise() == false);
lean_assert(this->m_ed.is_OK());
lean_assert(m_beta.is_OK());
SASSERT(numeric_traits<T>::precise() == false);
SASSERT(this->m_ed.is_OK());
SASSERT(m_beta.is_OK());
m_beta = this->m_ed;
this->m_factorization->solve_yB_with_error_check_indexed(m_beta, this->m_basis_heading, this->m_basis, this->m_settings);
}
template <typename T, typename X>
void lp_primal_core_solver<T, X>::advance_on_entering_equal_leaving(int entering, X & t) {
lean_assert(!this->A_mult_x_is_off() );
SASSERT(!this->A_mult_x_is_off() );
this->update_x(entering, t * m_sign_of_entering_delta);
if (this->A_mult_x_is_off_on_index(this->m_ed.m_index) && !this->find_x_by_solving()) {
this->init_lu();
@ -670,7 +685,7 @@ void lp_primal_core_solver<T, X>::advance_on_entering_equal_leaving(int entering
}
}
if (this->m_using_infeas_costs) {
lean_assert(is_zero(this->m_costs[entering]));
SASSERT(is_zero(this->m_costs[entering]));
init_infeasibility_costs_for_changed_basis_only();
}
if (this->m_look_for_feasible_solution_only && this->current_x_is_feasible())
@ -683,10 +698,10 @@ void lp_primal_core_solver<T, X>::advance_on_entering_equal_leaving(int entering
}
template <typename T, typename X>void lp_primal_core_solver<T, X>::advance_on_entering_and_leaving(int entering, int leaving, X & t) {
lean_assert(entering >= 0 && m_non_basis_list.back() == static_cast<unsigned>(entering));
lean_assert(this->m_using_infeas_costs || t >= zero_of_type<X>());
lean_assert(leaving >= 0 && entering >= 0);
lean_assert(entering != leaving || !is_zero(t)); // otherwise nothing changes
SASSERT(entering >= 0 && m_non_basis_list.back() == static_cast<unsigned>(entering));
SASSERT(this->m_using_infeas_costs || t >= zero_of_type<X>());
SASSERT(leaving >= 0 && entering >= 0);
SASSERT(entering != leaving || !is_zero(t)); // otherwise nothing changes
if (entering == leaving) {
advance_on_entering_equal_leaving(entering, t);
return;
@ -702,7 +717,7 @@ template <typename T, typename X>void lp_primal_core_solver<T, X>::advance_on_en
this->iters_with_no_cost_growing()++;
return;
} else {
lean_assert(pivot_compare_result == 1);
SASSERT(pivot_compare_result == 1);
this->init_lu();
if (this->m_factorization == nullptr || this->m_factorization->get_status() != LU_status::OK) {
this->set_status(UNSTABLE);
@ -746,7 +761,7 @@ template <typename T, typename X>void lp_primal_core_solver<T, X>::advance_on_en
} else {
update_reduced_costs_from_pivot_row(entering, leaving);
}
lean_assert(!need_to_switch_costs());
SASSERT(!need_to_switch_costs());
std::list<unsigned>::iterator it = m_non_basis_list.end();
it--;
* it = static_cast<unsigned>(leaving);
@ -754,8 +769,8 @@ template <typename T, typename X>void lp_primal_core_solver<T, X>::advance_on_en
template <typename T, typename X> void lp_primal_core_solver<T, X>::advance_on_entering_precise(int entering) {
lean_assert(numeric_traits<T>::precise());
lean_assert(entering > -1);
SASSERT(numeric_traits<T>::precise());
SASSERT(entering > -1);
this->solve_Bd(entering);
X t;
int leaving = find_leaving_and_t_precise(entering, t);
@ -771,7 +786,7 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::advance_on_e
advance_on_entering_precise(entering);
return;
}
lean_assert(entering > -1);
SASSERT(entering > -1);
this->solve_Bd(entering);
int refresh_result = refresh_reduced_cost_at_entering_and_check_that_it_is_off(entering);
if (refresh_result) {
@ -791,7 +806,7 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::advance_on_e
int leaving = find_leaving_and_t(entering, t);
if (leaving == -1){
if (!this->current_x_is_feasible()) {
lean_assert(!numeric_traits<T>::precise()); // we cannot have unbounded with inf costs
SASSERT(!numeric_traits<T>::precise()); // we cannot have unbounded with inf costs
// if (m_look_for_feasible_solution_only) {
// this->m_status = INFEASIBLE;
@ -865,7 +880,7 @@ template <typename T, typename X> unsigned lp_primal_core_solver<T, X>::solve()
return this->total_iterations();
}
one_iteration();
lean_assert(!this->m_using_infeas_costs || this->costs_on_nbasis_are_zeros());
SASSERT(!this->m_using_infeas_costs || this->costs_on_nbasis_are_zeros());
switch (this->get_status()) {
case OPTIMAL: // double check that we are at optimum
case INFEASIBLE:
@ -914,7 +929,7 @@ template <typename T, typename X> unsigned lp_primal_core_solver<T, X>::solve()
break;
case UNSTABLE:
lean_assert(! (numeric_traits<T>::precise()));
SASSERT(! (numeric_traits<T>::precise()));
this->init_lu();
if (this->m_factorization->get_status() != LU_status::OK) {
this->set_status(FLOATING_POINT_ERROR);
@ -940,7 +955,7 @@ template <typename T, typename X> unsigned lp_primal_core_solver<T, X>::solve()
&&
!(this->current_x_is_feasible() && this->m_look_for_feasible_solution_only));
lean_assert(this->get_status() == FLOATING_POINT_ERROR
SASSERT(this->get_status() == FLOATING_POINT_ERROR
||
this->current_x_is_feasible() == false
||
@ -957,7 +972,7 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::delete_fa
// according to Swietanowski, " A new steepest edge approximation for the simplex method for linear programming"
template <typename T, typename X> void lp_primal_core_solver<T, X>::init_column_norms() {
lean_assert(numeric_traits<T>::precise() == false);
SASSERT(numeric_traits<T>::precise() == false);
for (unsigned j = 0; j < this->m_n(); j++) {
this->m_column_norms[j] = T(static_cast<int>(this->m_A.m_columns[j].size() + 1))
@ -967,7 +982,7 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::init_column_
// debug only
template <typename T, typename X> T lp_primal_core_solver<T, X>::calculate_column_norm_exactly(unsigned j) {
lean_assert(numeric_traits<T>::precise() == false);
SASSERT(numeric_traits<T>::precise() == false);
indexed_vector<T> w(this->m_m());
this->m_A.copy_column_to_vector(j, w);
vector<T> d(this->m_m());
@ -979,8 +994,8 @@ template <typename T, typename X> T lp_primal_core_solver<T, X>::calculate_colum
}
template <typename T, typename X> void lp_primal_core_solver<T, X>::update_or_init_column_norms(unsigned entering, unsigned leaving) {
lean_assert(numeric_traits<T>::precise() == false);
lean_assert(m_column_norm_update_counter <= this->m_settings.column_norms_update_frequency);
SASSERT(numeric_traits<T>::precise() == false);
SASSERT(m_column_norm_update_counter <= this->m_settings.column_norms_update_frequency);
if (m_column_norm_update_counter == this->m_settings.column_norms_update_frequency) {
m_column_norm_update_counter = 0;
init_column_norms();
@ -992,7 +1007,7 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::update_or
// following Swietanowski - A new steepest ...
template <typename T, typename X> void lp_primal_core_solver<T, X>::update_column_norms(unsigned entering, unsigned leaving) {
lean_assert(numeric_traits<T>::precise() == false);
SASSERT(numeric_traits<T>::precise() == false);
T pivot = this->m_pivot_row[entering];
T g_ent = calculate_norm_of_entering_exactly() / pivot / pivot;
if (!numeric_traits<T>::precise()) {
@ -1027,7 +1042,7 @@ template <typename T, typename X> T lp_primal_core_solver<T, X>::calculate_no
// calling it stage1 is too cryptic
template <typename T, typename X> void lp_primal_core_solver<T, X>::find_feasible_solution() {
this->m_look_for_feasible_solution_only = true;
lean_assert(this->non_basic_columns_are_set_correctly());
SASSERT(this->non_basic_columns_are_set_correctly());
this->set_status(UNKNOWN);
solve();
}
@ -1095,8 +1110,8 @@ void lp_primal_core_solver<T, X>::init_infeasibility_costs_for_changed_basis_onl
template <typename T, typename X>
void lp_primal_core_solver<T, X>::init_infeasibility_costs() {
lean_assert(this->m_x.size() >= this->m_n());
lean_assert(this->m_column_types.size() >= this->m_n());
SASSERT(this->m_x.size() >= this->m_n());
SASSERT(this->m_column_types.size() >= this->m_n());
for (unsigned j = this->m_n(); j--;)
init_infeasibility_cost_for_column(j);
this->m_using_infeas_costs = true;
@ -1138,7 +1153,7 @@ lp_primal_core_solver<T, X>::get_infeasibility_cost_for_column(unsigned j) const
ret = numeric_traits<T>::zero();
break;
default:
lean_assert(false);
SASSERT(false);
ret = numeric_traits<T>::zero(); // does not matter
break;
}
@ -1192,7 +1207,7 @@ lp_primal_core_solver<T, X>::init_infeasibility_cost_for_column(unsigned j) {
this->m_costs[j] = numeric_traits<T>::zero();
break;
default:
lean_assert(false);
SASSERT(false);
break;
}
@ -1223,7 +1238,7 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::print_column
case column_type::free_column:
out << "( _" << this->m_x[j] << "_)" << std::endl;
default:
lean_unreachable();
SASSERT(false);
}
}
@ -1262,7 +1277,7 @@ template <typename T, typename X> std::string lp_primal_core_solver<T, X>::break
case upper_break: return "upper_break";
case fixed_break: return "fixed_break";
default:
lean_assert(false);
SASSERT(false);
break;
}
return "type is not found";
@ -1275,7 +1290,7 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::print_breakp
template <typename T, typename X>
void lp_primal_core_solver<T, X>::init_reduced_costs() {
lean_assert(!this->use_tableau());
SASSERT(!this->use_tableau());
if (this->current_x_is_infeasible() && !this->m_using_infeas_costs) {
init_infeasibility_costs();
} else if (this->current_x_is_feasible() && this->m_using_infeas_costs) {
@ -1290,12 +1305,12 @@ void lp_primal_core_solver<T, X>::init_reduced_costs() {
template <typename T, typename X> void lp_primal_core_solver<T, X>::change_slope_on_breakpoint(unsigned entering, breakpoint<X> * b, T & slope_at_entering) {
if (b->m_j == entering) {
lean_assert(b->m_type != fixed_break && (!is_zero(b->m_delta)));
SASSERT(b->m_type != fixed_break && (!is_zero(b->m_delta)));
slope_at_entering += m_sign_of_entering_delta;
return;
}
lean_assert(this->m_basis_heading[b->m_j] >= 0);
SASSERT(this->m_basis_heading[b->m_j] >= 0);
unsigned i_row = this->m_basis_heading[b->m_j];
const T & d = - this->m_ed[i_row];
if (numeric_traits<T>::is_zero(d)) return;
@ -1314,13 +1329,13 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::change_sl
slope_at_entering += delta;
break;
default:
lean_assert(false);
SASSERT(false);
}
}
template <typename T, typename X> void lp_primal_core_solver<T, X>::try_add_breakpoint_in_row(unsigned i) {
lean_assert(i < this->m_m());
SASSERT(i < this->m_m());
const T & d = this->m_ed[i]; // the coefficient before m_entering in the i-th row
if (d == 0) return; // the change of x[m_entering] will not change the corresponding basis x
unsigned j = this->m_basis[i];
@ -1342,7 +1357,7 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::try_add_b
case column_type::free_column:
break;
default:
lean_assert(false);
SASSERT(false);
break;
}
}
@ -1366,7 +1381,7 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::print_bound_
out << "inf, inf" << std::endl;
break;
default:
lean_assert(false);
SASSERT(false);
break;
}
}

37
src/util/lp/lp_primal_core_solver_instances.cpp
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <utility>
#include <memory>
#include <string>
@ -9,19 +24,19 @@
#include <functional>
#include "util/lp/lar_solver.h"
#include "util/lp/lp_primal_core_solver.hpp"
#include "util/lp/lp_primal_core_solver_tableau.hpp"
namespace lean {
#include "util/lp/lp_primal_core_solver_tableau.h"
namespace lp {
template void lp_primal_core_solver<double, double>::find_feasible_solution();
template void lean::lp_primal_core_solver<lean::mpq, lean::numeric_pair<lean::mpq> >::find_feasible_solution();
template void lp::lp_primal_core_solver<lp::mpq, lp::numeric_pair<lp::mpq> >::find_feasible_solution();
template unsigned lp_primal_core_solver<double, double>::solve();
template unsigned lp_primal_core_solver<double, double>::solve_with_tableau();
template unsigned lp_primal_core_solver<mpq, mpq>::solve();
template unsigned lp_primal_core_solver<mpq, numeric_pair<mpq> >::solve();
template void lean::lp_primal_core_solver<double, double>::clear_breakpoints();
template bool lean::lp_primal_core_solver<lean::mpq, lean::mpq>::update_basis_and_x_tableau(int, int, lean::mpq const&);
template bool lean::lp_primal_core_solver<double, double>::update_basis_and_x_tableau(int, int, double const&);
template bool lean::lp_primal_core_solver<lean::mpq, lean::numeric_pair<lean::mpq> >::update_basis_and_x_tableau(int, int, lean::numeric_pair<lean::mpq> const&);
template void lp::lp_primal_core_solver<double, double>::clear_breakpoints();
template bool lp::lp_primal_core_solver<lp::mpq, lp::mpq>::update_basis_and_x_tableau(int, int, lp::mpq const&);
template bool lp::lp_primal_core_solver<double, double>::update_basis_and_x_tableau(int, int, double const&);
template bool lp::lp_primal_core_solver<lp::mpq, lp::numeric_pair<lp::mpq> >::update_basis_and_x_tableau(int, int, lp::numeric_pair<lp::mpq> const&);
}

75
src/util/lp/lp_primal_core_solver_tableau.hpp → src/util/lp/lp_primal_core_solver_tableau.h
View file

@ -1,10 +1,25 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
// this is a part of lp_primal_core_solver that deals with the tableau
#include "util/lp/lp_primal_core_solver.h"
namespace lean {
namespace lp {
template <typename T, typename X> void lp_primal_core_solver<T, X>::one_iteration_tableau() {
int entering = choose_entering_column_tableau();
if (entering == -1) {
@ -13,7 +28,7 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::one_iteratio
else {
advance_on_entering_tableau(entering);
}
lean_assert(this->inf_set_is_correct());
SASSERT(this->inf_set_is_correct());
}
template <typename T, typename X> void lp_primal_core_solver<T, X>::advance_on_entering_tableau(int entering) {
@ -37,7 +52,7 @@ template <typename T, typename X> int lp_primal_core_solver<T, X>::choose_enteri
//this moment m_y = cB * B(-1)
unsigned number_of_benefitial_columns_to_go_over = get_number_of_non_basic_column_to_try_for_enter();
lean_assert(numeric_traits<T>::precise());
SASSERT(numeric_traits<T>::precise());
if (number_of_benefitial_columns_to_go_over == 0)
return -1;
if (this->m_basis_sort_counter == 0) {
@ -149,7 +164,7 @@ unsigned lp_primal_core_solver<T, X>::solve_with_tableau() {
break;
case UNSTABLE:
lean_assert(! (numeric_traits<T>::precise()));
SASSERT(! (numeric_traits<T>::precise()));
this->init_lu();
if (this->m_factorization->get_status() != LU_status::OK) {
this->set_status(FLOATING_POINT_ERROR);
@ -175,7 +190,7 @@ unsigned lp_primal_core_solver<T, X>::solve_with_tableau() {
&&
!(this->current_x_is_feasible() && this->m_look_for_feasible_solution_only));
lean_assert(this->get_status() == FLOATING_POINT_ERROR
SASSERT(this->get_status() == FLOATING_POINT_ERROR
||
this->current_x_is_feasible() == false
||
@ -184,13 +199,13 @@ unsigned lp_primal_core_solver<T, X>::solve_with_tableau() {
}
template <typename T, typename X>void lp_primal_core_solver<T, X>::advance_on_entering_and_leaving_tableau(int entering, int leaving, X & t) {
lean_assert(this->A_mult_x_is_off() == false);
lean_assert(leaving >= 0 && entering >= 0);
lean_assert((this->m_settings.simplex_strategy() ==
SASSERT(this->A_mult_x_is_off() == false);
SASSERT(leaving >= 0 && entering >= 0);
SASSERT((this->m_settings.simplex_strategy() ==
simplex_strategy_enum::tableau_rows) ||
m_non_basis_list.back() == static_cast<unsigned>(entering));
lean_assert(this->m_using_infeas_costs || !is_neg(t));
lean_assert(entering != leaving || !is_zero(t)); // otherwise nothing changes
SASSERT(this->m_using_infeas_costs || !is_neg(t));
SASSERT(entering != leaving || !is_zero(t)); // otherwise nothing changes
if (entering == leaving) {
advance_on_entering_equal_leaving_tableau(entering, t);
return;
@ -201,7 +216,7 @@ template <typename T, typename X>void lp_primal_core_solver<T, X>::advance_on_en
t = -t;
}
this->update_basis_and_x_tableau(entering, leaving, t);
lean_assert(this->A_mult_x_is_off() == false);
SASSERT(this->A_mult_x_is_off() == false);
this->iters_with_no_cost_growing() = 0;
} else {
this->pivot_column_tableau(entering, this->m_basis_heading[leaving]);
@ -216,7 +231,7 @@ template <typename T, typename X>void lp_primal_core_solver<T, X>::advance_on_en
this->init_reduced_costs_tableau();
}
lean_assert(!need_to_switch_costs());
SASSERT(!need_to_switch_costs());
std::list<unsigned>::iterator it = m_non_basis_list.end();
it--;
* it = static_cast<unsigned>(leaving);
@ -225,7 +240,7 @@ template <typename T, typename X>void lp_primal_core_solver<T, X>::advance_on_en
template <typename T, typename X>
void lp_primal_core_solver<T, X>::advance_on_entering_equal_leaving_tableau(int entering, X & t) {
lean_assert(!this->A_mult_x_is_off() );
SASSERT(!this->A_mult_x_is_off() );
this->update_x_tableau(entering, t * m_sign_of_entering_delta);
if (this->m_look_for_feasible_solution_only && this->current_x_is_feasible())
return;
@ -246,7 +261,7 @@ template <typename T, typename X> int lp_primal_core_solver<T, X>::find_leaving_
const column_cell & c = col[k];
unsigned i = c.m_i;
const T & ed = this->m_A.get_val(c);
lean_assert(!numeric_traits<T>::is_zero(ed));
SASSERT(!numeric_traits<T>::is_zero(ed));
unsigned j = this->m_basis[i];
limit_theta_on_basis_column(j, - ed * m_sign_of_entering_delta, t, unlimited);
if (!unlimited) {
@ -265,7 +280,7 @@ template <typename T, typename X> int lp_primal_core_solver<T, X>::find_leaving_
const column_cell & c = col[k];
unsigned i = c.m_i;
const T & ed = this->m_A.get_val(c);
lean_assert(!numeric_traits<T>::is_zero(ed));
SASSERT(!numeric_traits<T>::is_zero(ed));
unsigned j = this->m_basis[i];
unlimited = true;
limit_theta_on_basis_column(j, -ed * m_sign_of_entering_delta, ratio, unlimited);
@ -298,12 +313,12 @@ template <typename T, typename X> int lp_primal_core_solver<T, X>::find_leaving_
}
template <typename T, typename X> void lp_primal_core_solver<T, X>::init_run_tableau() {
// print_matrix(&(this->m_A), std::cout);
lean_assert(this->A_mult_x_is_off() == false);
lean_assert(basis_columns_are_set_correctly());
SASSERT(this->A_mult_x_is_off() == false);
SASSERT(basis_columns_are_set_correctly());
this->m_basis_sort_counter = 0; // to initiate the sort of the basis
this->set_total_iterations(0);
this->iters_with_no_cost_growing() = 0;
lean_assert(this->inf_set_is_correct());
SASSERT(this->inf_set_is_correct());
if (this->current_x_is_feasible() && this->m_look_for_feasible_solution_only)
return;
if (this->m_settings.backup_costs)
@ -317,13 +332,13 @@ template <typename T, typename X> void lp_primal_core_solver<T, X>::init_run_tab
}
if (this->m_settings.simplex_strategy() == simplex_strategy_enum::tableau_rows)
init_tableau_rows();
lean_assert(this->reduced_costs_are_correct_tableau());
lean_assert(!this->need_to_pivot_to_basis_tableau());
SASSERT(this->reduced_costs_are_correct_tableau());
SASSERT(!this->need_to_pivot_to_basis_tableau());
}
template <typename T, typename X> bool lp_primal_core_solver<T, X>::
update_basis_and_x_tableau(int entering, int leaving, X const & tt) {
lean_assert(this->use_tableau());
SASSERT(this->use_tableau());
update_x_tableau(entering, tt);
this->pivot_column_tableau(entering, this->m_basis_heading[leaving]);
this->change_basis(entering, leaving);
@ -340,8 +355,8 @@ update_x_tableau(unsigned entering, const X& delta) {
}
} else { // m_using_infeas_costs == true
this->m_x[entering] += delta;
lean_assert(this->column_is_feasible(entering));
lean_assert(this->m_costs[entering] == zero_of_type<T>());
SASSERT(this->column_is_feasible(entering));
SASSERT(this->m_costs[entering] == zero_of_type<T>());
// m_d[entering] can change because of the cost change for basic columns.
for (const auto & c : this->m_A.m_columns[entering]) {
unsigned i = c.m_i;
@ -354,13 +369,13 @@ update_x_tableau(unsigned entering, const X& delta) {
this->m_inf_set.insert(j);
}
}
lean_assert(this->A_mult_x_is_off() == false);
SASSERT(this->A_mult_x_is_off() == false);
}
template <typename T, typename X> void lp_primal_core_solver<T, X>::
update_inf_cost_for_column_tableau(unsigned j) {
lean_assert(this->m_settings.simplex_strategy() != simplex_strategy_enum::tableau_rows);
lean_assert(this->m_using_infeas_costs);
SASSERT(this->m_settings.simplex_strategy() != simplex_strategy_enum::tableau_rows);
SASSERT(this->m_using_infeas_costs);
T new_cost = get_infeasibility_cost_for_column(j);
T delta = this->m_costs[j] - new_cost;
if (is_zero(delta))

25
src/util/lp/lp_primal_simplex.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
#include <unordered_map>
@ -12,7 +27,7 @@
#include "util/lp/lp_primal_core_solver.h"
#include "util/lp/lp_solver.h"
#include "util/lp/iterator_on_row.h"
namespace lean {
namespace lp {
template <typename T, typename X>
class lp_primal_simplex: public lp_solver<T, X> {
lp_primal_core_solver<T, X> * m_core_solver;

45
src/util/lp/lp_primal_simplex.hpp
View file

@ -1,12 +1,27 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <string>
#include "util/vector.h"
#include "util/lp/lp_primal_simplex.h"
namespace lean {
namespace lp {
template <typename T, typename X> void lp_primal_simplex<T, X>::fill_costs_and_x_for_first_stage_solver(unsigned original_number_of_columns) {
unsigned slack_var = original_number_of_columns;
unsigned artificial = original_number_of_columns + this->m_slacks;
@ -61,7 +76,7 @@ template <typename T, typename X> void lp_primal_simplex<T, X>::fill_costs_and_x
int row,
unsigned & slack_var,
unsigned & artificial) {
lean_assert(row >= 0 && row < this->row_count());
SASSERT(row >= 0 && row < this->row_count());
auto & constraint = this->m_constraints[this->m_core_solver_rows_to_external_rows[row]];
// we need to bring the program to the form Ax = b
T rs = this->m_b[row];
@ -86,7 +101,7 @@ template <typename T, typename X> void lp_primal_simplex<T, X>::fill_costs_and_x
(*this->m_A)(row, slack_var) = - numeric_traits<T>::one();
if (rs > 0) {
lean_assert(numeric_traits<T>::is_zero(this->m_x[slack_var]));
SASSERT(numeric_traits<T>::is_zero(this->m_x[slack_var]));
// adding one artificial
this->m_column_types[artificial] = column_type::low_bound;
(*this->m_A)(row, artificial) = numeric_traits<T>::one();
@ -108,7 +123,7 @@ template <typename T, typename X> void lp_primal_simplex<T, X>::fill_costs_and_x
if (rs < 0) {
// adding one artificial
lean_assert(numeric_traits<T>::is_zero(this->m_x[slack_var]));
SASSERT(numeric_traits<T>::is_zero(this->m_x[slack_var]));
this->m_column_types[artificial] = column_type::low_bound;
(*this->m_A)(row, artificial) = - numeric_traits<T>::one();
this->m_costs[artificial] = artificial_cost;
@ -177,12 +192,12 @@ template <typename T, typename X> void lp_primal_simplex<T, X>::fill_A_x_and_bas
}
template <typename T, typename X> void lp_primal_simplex<T, X>::fill_A_x_and_basis_for_stage_one_total_inf_for_row(unsigned row) {
lean_assert(row < this->row_count());
SASSERT(row < this->row_count());
auto ext_row_it = this->m_core_solver_rows_to_external_rows.find(row);
lean_assert(ext_row_it != this->m_core_solver_rows_to_external_rows.end());
SASSERT(ext_row_it != this->m_core_solver_rows_to_external_rows.end());
unsigned ext_row = ext_row_it->second;
auto constr_it = this->m_constraints.find(ext_row);
lean_assert(constr_it != this->m_constraints.end());
SASSERT(constr_it != this->m_constraints.end());
auto & constraint = constr_it->second;
unsigned j = this->m_A->column_count(); // j is a slack variable
this->m_A->add_column();
@ -209,7 +224,7 @@ template <typename T, typename X> void lp_primal_simplex<T, X>::fill_A_x_and_bas
this->m_upper_bounds[j] = m_low_bounds[j] = zero_of_type<X>();
break;
default:
lean_unreachable();
SASSERT(false);
}
}
@ -281,10 +296,10 @@ template <typename T, typename X> T lp_primal_simplex<T, X>::get_row_value(unsig
T ret = numeric_traits<T>::zero();
for (auto & pair : it->second) {
auto cit = this->m_map_from_var_index_to_column_info.find(pair.first);
lean_assert(cit != this->m_map_from_var_index_to_column_info.end());
SASSERT(cit != this->m_map_from_var_index_to_column_info.end());
column_info<T> * ci = cit->second;
auto sol_it = solution.find(ci->get_name());
lean_assert(sol_it != solution.end());
SASSERT(sol_it != solution.end());
T column_val = sol_it->second;
if (out != nullptr) {
(*out) << pair.second << "(" << ci->get_name() << "=" << column_val << ") ";
@ -329,7 +344,7 @@ template <typename T, typename X> bool lp_primal_simplex<T, X>::row_constraint_h
}
return true;;
}
lean_unreachable();
SASSERT(false);
return false; // it is unreachable
}

43
src/util/lp/lp_primal_simplex_instances.cpp
View file

@ -1,20 +1,35 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <utility>
#include <memory>
#include <string>
#include "util/vector.h"
#include <functional>
#include "util/lp/lp_primal_simplex.hpp"
template bool lean::lp_primal_simplex<double, double>::bounds_hold(std::unordered_map<std::string, double, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, double> > > const&);
template bool lean::lp_primal_simplex<double, double>::row_constraints_hold(std::unordered_map<std::string, double, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, double> > > const&);
template double lean::lp_primal_simplex<double, double>::get_current_cost() const;
template double lean::lp_primal_simplex<double, double>::get_column_value(unsigned int) const;
template lean::lp_primal_simplex<double, double>::~lp_primal_simplex();
template lean::lp_primal_simplex<lean::mpq, lean::mpq>::~lp_primal_simplex();
template lean::mpq lean::lp_primal_simplex<lean::mpq, lean::mpq>::get_current_cost() const;
template lean::mpq lean::lp_primal_simplex<lean::mpq, lean::mpq>::get_column_value(unsigned int) const;
template void lean::lp_primal_simplex<double, double>::find_maximal_solution();
template void lean::lp_primal_simplex<lean::mpq, lean::mpq>::find_maximal_solution();
template bool lp::lp_primal_simplex<double, double>::bounds_hold(std::unordered_map<std::string, double, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, double> > > const&);
template bool lp::lp_primal_simplex<double, double>::row_constraints_hold(std::unordered_map<std::string, double, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, double> > > const&);
template double lp::lp_primal_simplex<double, double>::get_current_cost() const;
template double lp::lp_primal_simplex<double, double>::get_column_value(unsigned int) const;
template lp::lp_primal_simplex<double, double>::~lp_primal_simplex();
template lp::lp_primal_simplex<lp::mpq, lp::mpq>::~lp_primal_simplex();
template lp::mpq lp::lp_primal_simplex<lp::mpq, lp::mpq>::get_current_cost() const;
template lp::mpq lp::lp_primal_simplex<lp::mpq, lp::mpq>::get_column_value(unsigned int) const;
template void lp::lp_primal_simplex<double, double>::find_maximal_solution();
template void lp::lp_primal_simplex<lp::mpq, lp::mpq>::find_maximal_solution();

31
src/util/lp/lp_settings.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
@ -12,7 +27,7 @@
#include "util/lp/lp_utils.h"
#include "util/stopwatch.h"
namespace lean {
namespace lp {
typedef unsigned var_index;
typedef unsigned constraint_index;
typedef unsigned row_index;
@ -296,7 +311,7 @@ public:
unsigned column_norms_update_frequency;
bool scale_with_ratio;
double density_threshold; // need to tune it up, todo
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
static unsigned ddd; // used for debugging
#endif
bool use_breakpoints_in_feasibility_search;
@ -366,7 +381,7 @@ inline void print_blanks(int n, std::ostream & out) {
// after a push of the last element we ensure that the vector increases
// we also suppose that before the last push the vector was increasing
inline void ensure_increasing(vector<unsigned> & v) {
lean_assert(v.size() > 0);
SASSERT(v.size() > 0);
unsigned j = v.size() - 1;
for (; j > 0; j-- )
if (v[j] <= v[j - 1]) {
@ -381,7 +396,7 @@ inline void ensure_increasing(vector<unsigned> & v) {
#if LEAN_DEBUG
#if Z3DEBUG
bool D();
#endif
}

33
src/util/lp/lp_settings.hpp
View file

@ -1,12 +1,27 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <cmath>
#include <string>
#include "util/vector.h"
#include "util/lp/lp_settings.h"
namespace lean {
namespace lp {
std::string column_type_to_string(column_type t) {
switch (t) {
case column_type::fixed: return "fixed";
@ -14,7 +29,7 @@ std::string column_type_to_string(column_type t) {
case column_type::low_bound: return "low_bound";
case column_type::upper_bound: return "upper_bound";
case column_type::free_column: return "free_column";
default: lean_unreachable();
default: SASSERT(false);
}
return "unknown"; // it is unreachable
}
@ -34,7 +49,7 @@ const char* lp_status_to_string(lp_status status) {
case EMPTY: return "EMPTY";
case UNSTABLE: return "UNSTABLE";
default:
lean_unreachable();
SASSERT(false);
}
return "UNKNOWN"; // it is unreachable
}
@ -49,7 +64,7 @@ lp_status lp_status_from_string(std::string status) {
if (status == "TIME_EXHAUSTED") return lp_status::TIME_EXHAUSTED;
if (status == "ITERATIONS_EXHAUSTED") return lp_status::ITERATIONS_EXHAUSTED;
if (status == "EMPTY") return lp_status::EMPTY;
lean_unreachable();
SASSERT(false);
return lp_status::UNKNOWN; // it is unreachable
}
@ -104,7 +119,7 @@ bool vectors_are_equal(const vector<T> & a, const vector<T> &b) {
}
return true;
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
unsigned lp_settings::ddd = 0;
#endif
}

27
src/util/lp/lp_settings_instances.cpp
View file

@ -1,10 +1,25 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <memory>
#include "util/vector.h"
#include "util/lp/lp_settings.hpp"
template bool lean::vectors_are_equal<double>(vector<double> const&, vector<double> const&);
template bool lean::vectors_are_equal<lean::mpq>(vector<lean::mpq > const&, vector<lean::mpq> const&);
template bool lp::vectors_are_equal<double>(vector<double> const&, vector<double> const&);
template bool lp::vectors_are_equal<lp::mpq>(vector<lp::mpq > const&, vector<lp::mpq> const&);

25
src/util/lp/lp_solver.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include <string>
@ -15,7 +30,7 @@
#include "util/lp/scaler.h"
#include "util/lp/linear_combination_iterator.h"
#include "util/lp/bound_analyzer_on_row.h"
namespace lean {
namespace lp {
enum lp_relation {
Less_or_equal,
Equal,

53
src/util/lp/lp_solver.hpp
View file

@ -1,12 +1,27 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <string>
#include <algorithm>
#include "util/vector.h"
#include "util/lp/lp_solver.h"
namespace lean {
namespace lp {
template <typename T, typename X> column_info<T> * lp_solver<T, X>::get_or_create_column_info(unsigned column) {
auto it = m_map_from_var_index_to_column_info.find(column);
return (it == m_map_from_var_index_to_column_info.end())? (m_map_from_var_index_to_column_info[column] = new column_info<T>(static_cast<unsigned>(-1))) : it->second;
@ -32,7 +47,7 @@ template <typename T, typename X> T lp_solver<T, X>::get_column_cost_value(unsig
return ci->get_cost() * get_column_value(j);
}
template <typename T, typename X> void lp_solver<T, X>::add_constraint(lp_relation relation, T right_side, unsigned row_index) {
lean_assert(m_constraints.find(row_index) == m_constraints.end());
SASSERT(m_constraints.find(row_index) == m_constraints.end());
lp_constraint<T, X> cs(right_side, relation);
m_constraints[row_index] = cs;
}
@ -158,10 +173,10 @@ template <typename T, typename X> void lp_solver<T, X>::pin_vars_on_row_with_sig
column_info<T> * ci = m_map_from_var_index_to_column_info[j];
T a = t.second;
if (a * sign > numeric_traits<T>::zero()) {
lean_assert(ci->upper_bound_is_set());
SASSERT(ci->upper_bound_is_set());
ci->set_fixed_value(ci->get_upper_bound());
} else {
lean_assert(ci->low_bound_is_set());
SASSERT(ci->low_bound_is_set());
ci->set_fixed_value(ci->get_low_bound());
}
}
@ -328,7 +343,7 @@ template <typename T, typename X> bool lp_solver<T, X>::row_is_obsolete(std::
case lp_relation::Less_or_equal:
return row_le_is_obsolete(row, row_index);
}
lean_unreachable();
SASSERT(false);
return false; // it is unreachable
}
@ -343,7 +358,7 @@ template <typename T, typename X> void lp_solver<T, X>::remove_fixed_or_zero_col
vector<unsigned> removed;
for (auto & col : row) {
unsigned j = col.first;
lean_assert(m_map_from_var_index_to_column_info.find(j) != m_map_from_var_index_to_column_info.end());
SASSERT(m_map_from_var_index_to_column_info.find(j) != m_map_from_var_index_to_column_info.end());
column_info<T> * ci = m_map_from_var_index_to_column_info[j];
if (ci->is_fixed()) {
removed.push_back(j);
@ -412,7 +427,7 @@ template <typename T, typename X> void lp_solver<T, X>::map_external_columns_to_
}
unsigned j = col.first;
auto column_info_it = m_map_from_var_index_to_column_info.find(j);
lean_assert(column_info_it != m_map_from_var_index_to_column_info.end());
SASSERT(column_info_it != m_map_from_var_index_to_column_info.end());
auto j_column = column_info_it->second->get_column_index();
if (!is_valid(j_column)) { // j is a newcomer
@ -435,14 +450,14 @@ template <typename T, typename X> void lp_solver<T, X>::fill_A_from_A_values() {
m_A = new static_matrix<T, X>(static_cast<unsigned>(m_A_values.size()), number_of_core_structurals());
for (auto & t : m_A_values) {
auto row_it = m_external_rows_to_core_solver_rows.find(t.first);
lean_assert(row_it != m_external_rows_to_core_solver_rows.end());
SASSERT(row_it != m_external_rows_to_core_solver_rows.end());
unsigned row = row_it->second;
for (auto k : t.second) {
auto column_info_it = m_map_from_var_index_to_column_info.find(k.first);
lean_assert(column_info_it != m_map_from_var_index_to_column_info.end());
SASSERT(column_info_it != m_map_from_var_index_to_column_info.end());
column_info<T> *ci = column_info_it->second;
unsigned col = ci->get_column_index();
lean_assert(is_valid(col));
SASSERT(is_valid(col));
bool col_is_flipped = m_map_from_var_index_to_column_info[k.first]->is_flipped();
if (!col_is_flipped) {
(*m_A)(row, col) = k.second;
@ -456,7 +471,7 @@ template <typename T, typename X> void lp_solver<T, X>::fill_A_from_A_values() {
template <typename T, typename X> void lp_solver<T, X>::fill_matrix_A_and_init_right_side() {
map_external_rows_to_core_solver_rows();
map_external_columns_to_core_solver_columns();
lean_assert(m_A == nullptr);
SASSERT(m_A == nullptr);
fill_A_from_A_values();
m_b.resize(m_A->row_count());
}
@ -468,7 +483,7 @@ template <typename T, typename X> void lp_solver<T, X>::count_slacks_and_artific
}
template <typename T, typename X> void lp_solver<T, X>::count_slacks_and_artificials_for_row(unsigned i) {
lean_assert(this->m_constraints.find(this->m_core_solver_rows_to_external_rows[i]) != this->m_constraints.end());
SASSERT(this->m_constraints.find(this->m_core_solver_rows_to_external_rows[i]) != this->m_constraints.end());
auto & constraint = this->m_constraints[this->m_core_solver_rows_to_external_rows[i]];
switch (constraint.m_relation) {
case Equal:
@ -504,7 +519,7 @@ template <typename T, typename X> T lp_solver<T, X>::low_bound_shift_for_row(
template <typename T, typename X> void lp_solver<T, X>::fill_m_b() {
for (int i = this->row_count() - 1; i >= 0; i--) {
lean_assert(this->m_constraints.find(this->m_core_solver_rows_to_external_rows[i]) != this->m_constraints.end());
SASSERT(this->m_constraints.find(this->m_core_solver_rows_to_external_rows[i]) != this->m_constraints.end());
unsigned external_i = this->m_core_solver_rows_to_external_rows[i];
auto & constraint = this->m_constraints[external_i];
this->m_b[i] = constraint.m_rs - low_bound_shift_for_row(external_i);
@ -542,13 +557,13 @@ template <typename T, typename X> T lp_solver<T, X>::get_column_value_with_core_
template <typename T, typename X> void lp_solver<T, X>::set_scaled_cost(unsigned j) {
// grab original costs but modify it with the column scales
lean_assert(j < this->m_column_scale.size());
SASSERT(j < this->m_column_scale.size());
column_info<T> * ci = this->m_map_from_var_index_to_column_info[this->m_core_solver_columns_to_external_columns[j]];
T cost = ci->get_cost();
if (ci->is_flipped()){
cost *= -1;
}
lean_assert(ci->is_fixed() == false);
SASSERT(ci->is_fixed() == false);
this->m_costs[j] = cost * this->m_column_scale[j];
}
}

91
src/util/lp/lp_solver_instances.cpp
View file

@ -1,40 +1,55 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <string>
#include "util/lp/lp_solver.hpp"
template void lean::lp_solver<double, double>::add_constraint(lean::lp_relation, double, unsigned int);
template void lean::lp_solver<double, double>::cleanup();
template void lean::lp_solver<double, double>::count_slacks_and_artificials();
template void lean::lp_solver<double, double>::fill_m_b();
template void lean::lp_solver<double, double>::fill_matrix_A_and_init_right_side();
template void lean::lp_solver<double, double>::flip_costs();
template double lean::lp_solver<double, double>::get_column_cost_value(unsigned int, lean::column_info<double>*) const;
template int lean::lp_solver<double, double>::get_column_index_by_name(std::string) const;
template double lean::lp_solver<double, double>::get_column_value_with_core_solver(unsigned int, lean::lp_core_solver_base<double, double>*) const;
template lean::column_info<double>* lean::lp_solver<double, double>::get_or_create_column_info(unsigned int);
template void lean::lp_solver<double, double>::give_symbolic_name_to_column(std::string, unsigned int);
template void lean::lp_solver<double, double>::print_statistics_on_A(std::ostream & out);
template bool lean::lp_solver<double, double>::problem_is_empty();
template void lean::lp_solver<double, double>::scale();
template void lean::lp_solver<double, double>::set_scaled_cost(unsigned int);
template lean::lp_solver<double, double>::~lp_solver();
template void lean::lp_solver<lean::mpq, lean::mpq>::add_constraint(lean::lp_relation, lean::mpq, unsigned int);
template void lean::lp_solver<lean::mpq, lean::mpq>::cleanup();
template void lean::lp_solver<lean::mpq, lean::mpq>::count_slacks_and_artificials();
template void lean::lp_solver<lean::mpq, lean::mpq>::fill_m_b();
template void lean::lp_solver<lean::mpq, lean::mpq>::fill_matrix_A_and_init_right_side();
template void lean::lp_solver<lean::mpq, lean::mpq>::flip_costs();
template lean::mpq lean::lp_solver<lean::mpq, lean::mpq>::get_column_cost_value(unsigned int, lean::column_info<lean::mpq>*) const;
template int lean::lp_solver<lean::mpq, lean::mpq>::get_column_index_by_name(std::string) const;
template lean::mpq lean::lp_solver<lean::mpq, lean::mpq>::get_column_value_by_name(std::string) const;
template lean::mpq lean::lp_solver<lean::mpq, lean::mpq>::get_column_value_with_core_solver(unsigned int, lean::lp_core_solver_base<lean::mpq, lean::mpq>*) const;
template lean::column_info<lean::mpq>* lean::lp_solver<lean::mpq, lean::mpq>::get_or_create_column_info(unsigned int);
template void lean::lp_solver<lean::mpq, lean::mpq>::give_symbolic_name_to_column(std::string, unsigned int);
template void lean::lp_solver<lean::mpq, lean::mpq>::print_statistics_on_A(std::ostream & out);
template bool lean::lp_solver<lean::mpq, lean::mpq>::problem_is_empty();
template void lean::lp_solver<lean::mpq, lean::mpq>::scale();
template void lean::lp_solver<lean::mpq, lean::mpq>::set_scaled_cost(unsigned int);
template lean::lp_solver<lean::mpq, lean::mpq>::~lp_solver();
template double lean::lp_solver<double, double>::get_column_value_by_name(std::string) const;
template void lp::lp_solver<double, double>::add_constraint(lp::lp_relation, double, unsigned int);
template void lp::lp_solver<double, double>::cleanup();
template void lp::lp_solver<double, double>::count_slacks_and_artificials();
template void lp::lp_solver<double, double>::fill_m_b();
template void lp::lp_solver<double, double>::fill_matrix_A_and_init_right_side();
template void lp::lp_solver<double, double>::flip_costs();
template double lp::lp_solver<double, double>::get_column_cost_value(unsigned int, lp::column_info<double>*) const;
template int lp::lp_solver<double, double>::get_column_index_by_name(std::string) const;
template double lp::lp_solver<double, double>::get_column_value_with_core_solver(unsigned int, lp::lp_core_solver_base<double, double>*) const;
template lp::column_info<double>* lp::lp_solver<double, double>::get_or_create_column_info(unsigned int);
template void lp::lp_solver<double, double>::give_symbolic_name_to_column(std::string, unsigned int);
template void lp::lp_solver<double, double>::print_statistics_on_A(std::ostream & out);
template bool lp::lp_solver<double, double>::problem_is_empty();
template void lp::lp_solver<double, double>::scale();
template void lp::lp_solver<double, double>::set_scaled_cost(unsigned int);
template lp::lp_solver<double, double>::~lp_solver();
template void lp::lp_solver<lp::mpq, lp::mpq>::add_constraint(lp::lp_relation, lp::mpq, unsigned int);
template void lp::lp_solver<lp::mpq, lp::mpq>::cleanup();
template void lp::lp_solver<lp::mpq, lp::mpq>::count_slacks_and_artificials();
template void lp::lp_solver<lp::mpq, lp::mpq>::fill_m_b();
template void lp::lp_solver<lp::mpq, lp::mpq>::fill_matrix_A_and_init_right_side();
template void lp::lp_solver<lp::mpq, lp::mpq>::flip_costs();
template lp::mpq lp::lp_solver<lp::mpq, lp::mpq>::get_column_cost_value(unsigned int, lp::column_info<lp::mpq>*) const;
template int lp::lp_solver<lp::mpq, lp::mpq>::get_column_index_by_name(std::string) const;
template lp::mpq lp::lp_solver<lp::mpq, lp::mpq>::get_column_value_by_name(std::string) const;
template lp::mpq lp::lp_solver<lp::mpq, lp::mpq>::get_column_value_with_core_solver(unsigned int, lp::lp_core_solver_base<lp::mpq, lp::mpq>*) const;
template lp::column_info<lp::mpq>* lp::lp_solver<lp::mpq, lp::mpq>::get_or_create_column_info(unsigned int);
template void lp::lp_solver<lp::mpq, lp::mpq>::give_symbolic_name_to_column(std::string, unsigned int);
template void lp::lp_solver<lp::mpq, lp::mpq>::print_statistics_on_A(std::ostream & out);
template bool lp::lp_solver<lp::mpq, lp::mpq>::problem_is_empty();
template void lp::lp_solver<lp::mpq, lp::mpq>::scale();
template void lp::lp_solver<lp::mpq, lp::mpq>::set_scaled_cost(unsigned int);
template lp::lp_solver<lp::mpq, lp::mpq>::~lp_solver();
template double lp::lp_solver<double, double>::get_column_value_by_name(std::string) const;

29
src/util/lp/lp_utils.cpp
View file

@ -1,11 +1,26 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/lp_utils.h"
#ifdef lp_for_z3
namespace lean {
namespace lp {
double numeric_traits<double>::g_zero = 0.0;
double numeric_traits<double>::g_one = 1.0;
}
#endif

99
src/util/lp/lp_utils.h
View file

@ -1,8 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
This file should be present in z3 and in Lean.
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include <string>
#include "util/lp/numeric_pair.h"
@ -21,20 +35,12 @@ bool contains(const std::unordered_map<A, B> & map, const A& key) {
return map.find(key) != map.end();
}
#ifdef lp_for_z3
#ifdef Z3DEBUG
#define LEAN_DEBUG 1
#endif
namespace lean {
namespace lp {
inline void throw_exception(const std::string & str) {
throw default_exception(str);
}
typedef z3_exception exception;
#define lean_assert(_x_) { SASSERT(_x_); }
inline void lean_unreachable() { lean_assert(false); }
template <typename X> inline X zero_of_type() { return numeric_traits<X>::zero(); }
template <typename X> inline X one_of_type() { return numeric_traits<X>::one(); }
template <typename X> inline bool is_zero(const X & v) { return numeric_traits<X>::is_zero(v); }
@ -68,8 +74,8 @@ template<typename S, typename T> struct hash<pair<S, T>> {
};
template<>
struct hash<lean::numeric_pair<lean::mpq>> {
inline size_t operator()(const lean::numeric_pair<lean::mpq> & v) const {
struct hash<lp::numeric_pair<lp::mpq>> {
inline size_t operator()(const lp::numeric_pair<lp::mpq> & v) const {
size_t seed = 0;
hash_combine(seed, v.x);
hash_combine(seed, v.y);
@ -78,64 +84,3 @@ struct hash<lean::numeric_pair<lean::mpq>> {
};
}
#else // else of #if lp_for_z3
#include <utility>
#include <functional>
//include "util/numerics/mpq.h"
//include "util/numerics/numeric_traits.h"
//include "util/numerics/double.h"
#ifdef __CLANG__
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wmismatched-tags"
#endif
namespace std {
template<>
struct hash<lean::mpq> {
inline size_t operator()(const lean::mpq & v) const {
return v.hash();
}
};
}
namespace lean {
template <typename X> inline bool precise() { return numeric_traits<X>::precise();}
template <typename X> inline X one_of_type() { return numeric_traits<X>::one(); }
template <typename X> inline bool is_zero(const X & v) { return numeric_traits<X>::is_zero(v); }
template <typename X> inline double get_double(const X & v) { return numeric_traits<X>::get_double(v); }
template <typename T> inline T zero_of_type() {return numeric_traits<T>::zero();}
inline void throw_exception(std::string str) { throw exception(str); }
template <typename T> inline T from_string(std::string const & ) { lean_unreachable();}
template <> double inline from_string<double>(std::string const & str) { return atof(str.c_str());}
template <> mpq inline from_string<mpq>(std::string const & str) {
return mpq(atof(str.c_str()));
}
} // closing lean
template <class T>
inline void hash_combine(std::size_t & seed, const T & v) {
seed ^= std::hash<T>()(v) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
}
namespace std {
template<typename S, typename T> struct hash<pair<S, T>> {
inline size_t operator()(const pair<S, T> & v) const {
size_t seed = 0;
hash_combine(seed, v.first);
hash_combine(seed, v.second);
return seed;
}
};
template<>
struct hash<lean::numeric_pair<lean::mpq>> {
inline size_t operator()(const lean::numeric_pair<lean::mpq> & v) const {
size_t seed = 0;
hash_combine(seed, v.x);
hash_combine(seed, v.y);
return seed;
}
};
} // std
#ifdef __CLANG__
#pragma clang diagnostic pop
#endif
#endif

53
src/util/lp/lu.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
@ -18,8 +33,8 @@
#include "util/lp/row_eta_matrix.h"
#include "util/lp/square_dense_submatrix.h"
#include "util/lp/dense_matrix.h"
namespace lean {
#ifdef LEAN_DEBUG
namespace lp {
#ifdef Z3DEBUG
template <typename T, typename X> // print the nr x nc submatrix at the top left corner
void print_submatrix(sparse_matrix<T, X> & m, unsigned mr, unsigned nc);
@ -32,7 +47,7 @@ void print_matrix(sparse_matrix<T, X>& m, std::ostream & out);
template <typename T, typename X>
X dot_product(const vector<T> & a, const vector<X> & b) {
lean_assert(a.size() == b.size());
SASSERT(a.size() == b.size());
auto r = zero_of_type<X>();
for (unsigned i = 0; i < a.size(); i++) {
r += a[i] * b[i];
@ -47,7 +62,7 @@ class one_elem_on_diag: public tail_matrix<T, X> {
T m_val;
public:
one_elem_on_diag(unsigned i, T val) : m_i(i), m_val(val) {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
m_one_over_val = numeric_traits<T>::one() / m_val;
#endif
}
@ -56,7 +71,7 @@ public:
one_elem_on_diag(const one_elem_on_diag & o);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
unsigned m_m;
unsigned m_n;
virtual void set_number_of_rows(unsigned m) { m_m = m; m_n = m; }
@ -91,15 +106,15 @@ public:
void conjugate_by_permutation(permutation_matrix<T, X> & p) {
// this = p * this * p(-1)
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// auto rev = p.get_reverse();
// auto deb = ((*this) * rev);
// deb = p * deb;
#endif
m_i = p.apply_reverse(m_i);
#ifdef LEAN_DEBUG
// lean_assert(*this == deb);
#ifdef Z3DEBUG
// SASSERT(*this == deb);
#endif
}
}; // end of one_elem_on_diag
@ -212,7 +227,7 @@ public:
// see page 407 of Chvatal
unsigned transform_U_to_V_by_replacing_column(indexed_vector<T> & w, unsigned leaving_column_of_U);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
void check_vector_w(unsigned entering);
void check_apply_matrix_to_vector(matrix<T, X> *lp, T *w);
@ -248,7 +263,7 @@ public:
bool is_correct(const vector<unsigned>& basis);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
dense_matrix<T, X> tail_product();
dense_matrix<T, X> get_left_side(const vector<unsigned>& basis);
@ -291,7 +306,7 @@ public:
bool need_to_refactor() { return m_refactor_counter >= 200; }
void adjust_dimension_with_matrix_A() {
lean_assert(m_A.row_count() >= m_dim);
SASSERT(m_A.row_count() >= m_dim);
m_dim = m_A.row_count();
m_U.resize(m_dim);
m_Q.resize(m_dim);
@ -305,7 +320,7 @@ public:
unsigned m = m_A.row_count();
unsigned m_prev = m_U.dimension();
lean_assert(m_A.column_count() == heading.size());
SASSERT(m_A.column_count() == heading.size());
for (unsigned i = m_prev; i < m; i++) {
for (const row_cell<T> & c : m_A.m_rows[i]) {
@ -321,14 +336,14 @@ public:
void add_last_rows_to_B(const vector<int> & heading, const std::unordered_set<unsigned> & columns_to_replace) {
unsigned m = m_A.row_count();
lean_assert(m_A.column_count() == heading.size());
SASSERT(m_A.column_count() == heading.size());
adjust_dimension_with_matrix_A();
m_w_for_extension.resize(m);
// At this moment the LU is correct
// for B extended by only by ones at the diagonal in the lower right corner
for (unsigned j :columns_to_replace) {
lean_assert(heading[j] >= 0);
SASSERT(heading[j] >= 0);
replace_column_with_only_change_at_last_rows(j, heading[j]);
if (get_status() == LU_status::Degenerated)
break;
@ -352,7 +367,7 @@ public:
template <typename T, typename X>
void init_factorization(lu<T, X>* & factorization, static_matrix<T, X> & m_A, vector<unsigned> & m_basis, lp_settings &m_settings);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X>
dense_matrix<T, X> get_B(lu<T, X>& f, const vector<unsigned>& basis);
#endif

137
src/util/lp/lu.hpp
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <string>
#include <algorithm>
#include <set>
@ -9,8 +24,8 @@
#include <utility>
#include "util/debug.h"
#include "util/lp/lu.h"
namespace lean {
#ifdef LEAN_DEBUG
namespace lp {
#ifdef Z3DEBUG
template <typename T, typename X> // print the nr x nc submatrix at the top left corner
void print_submatrix(sparse_matrix<T, X> & m, unsigned mr, unsigned nc, std::ostream & out) {
vector<vector<std::string>> A;
@ -72,13 +87,13 @@ template <typename T, typename X>
one_elem_on_diag<T, X>::one_elem_on_diag(const one_elem_on_diag & o) {
m_i = o.m_i;
m_val = o.m_val;
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
m_m = m_n = o.m_m;
m_one_over_val = numeric_traits<T>::one() / o.m_val;
#endif
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X>
T one_elem_on_diag<T, X>::get_elem(unsigned i, unsigned j) const {
if (i == j){
@ -122,29 +137,29 @@ lu<T, X>::lu(static_matrix<T, X> const & A,
m_failure(false),
m_row_eta_work_vector(A.row_count()),
m_refactor_counter(0) {
lean_assert(!(numeric_traits<T>::precise() && settings.use_tableau()));
#ifdef LEAN_DEBUG
SASSERT(!(numeric_traits<T>::precise() && settings.use_tableau()));
#ifdef Z3DEBUG
debug_test_of_basis(A, basis);
#endif
++m_settings.st().m_num_factorizations;
create_initial_factorization();
#ifdef LEAN_DEBUG
// lean_assert(check_correctness());
#ifdef Z3DEBUG
// SASSERT(check_correctness());
#endif
}
template <typename T, typename X>
void lu<T, X>::debug_test_of_basis(static_matrix<T, X> const & A, vector<unsigned> & basis) {
std::set<unsigned> set;
for (unsigned i = 0; i < A.row_count(); i++) {
lean_assert(basis[i]< A.column_count());
SASSERT(basis[i]< A.column_count());
set.insert(basis[i]);
}
lean_assert(set.size() == A.row_count());
SASSERT(set.size() == A.row_count());
}
template <typename T, typename X>
void lu<T, X>::solve_By(indexed_vector<X> & y) {
lean_assert(false); // not implemented
SASSERT(false); // not implemented
// init_vector_y(y);
// solve_By_when_y_is_ready(y);
}
@ -268,7 +283,7 @@ void lu<T, X>::solve_yB(vector<T>& y) {
m_U.solve_y_U(y); // got y*U=cb*R(-1)
m_Q.apply_reverse_from_right_to_T(y); //
for (auto e = m_tail.rbegin(); e != m_tail.rend(); ++e) {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
(*e)->set_number_of_columns(m_dim);
#endif
(*e)->apply_from_right(y);
@ -277,20 +292,20 @@ void lu<T, X>::solve_yB(vector<T>& y) {
template <typename T, typename X>
void lu<T, X>::solve_yB_indexed(indexed_vector<T>& y) {
lean_assert(y.is_OK());
SASSERT(y.is_OK());
// first solve yU = cb*R(-1)
m_R.apply_reverse_from_right_to_T(y); // got y = cb*R(-1)
lean_assert(y.is_OK());
SASSERT(y.is_OK());
m_U.solve_y_U_indexed(y, m_settings); // got y*U=cb*R(-1)
lean_assert(y.is_OK());
SASSERT(y.is_OK());
m_Q.apply_reverse_from_right_to_T(y);
lean_assert(y.is_OK());
SASSERT(y.is_OK());
for (auto e = m_tail.rbegin(); e != m_tail.rend(); ++e) {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
(*e)->set_number_of_columns(m_dim);
#endif
(*e)->apply_from_right(y);
lean_assert(y.is_OK());
SASSERT(y.is_OK());
}
}
@ -304,8 +319,8 @@ void lu<T, X>::add_delta_to_solution(const vector<T>& yc, vector<T>& y){
template <typename T, typename X>
void lu<T, X>::add_delta_to_solution_indexed(indexed_vector<T>& y) {
// the delta sits in m_y_copy, put result into y
lean_assert(y.is_OK());
lean_assert(m_y_copy.is_OK());
SASSERT(y.is_OK());
SASSERT(m_y_copy.is_OK());
m_ii.clear();
m_ii.resize(y.data_size());
for (unsigned i : y.m_index)
@ -315,7 +330,7 @@ void lu<T, X>::add_delta_to_solution_indexed(indexed_vector<T>& y) {
if (m_ii[i] == 0)
m_ii.set_value(1, i);
}
lean_assert(m_ii.is_OK());
SASSERT(m_ii.is_OK());
y.m_index.clear();
for (unsigned i : m_ii.m_index) {
@ -326,7 +341,7 @@ void lu<T, X>::add_delta_to_solution_indexed(indexed_vector<T>& y) {
v = zero_of_type<T>();
}
lean_assert(y.is_OK());
SASSERT(y.is_OK());
}
template <typename T, typename X>
@ -343,7 +358,7 @@ void lu<T, X>::find_error_of_yB_indexed(const indexed_vector<T>& y, const vector
// it is a non efficient version
indexed_vector<T> yc = m_y_copy;
yc.m_index.clear();
lean_assert(!numeric_traits<T>::precise());
SASSERT(!numeric_traits<T>::precise());
{
vector<unsigned> d_basis(y.m_data.size());
@ -364,10 +379,10 @@ void lu<T, X>::find_error_of_yB_indexed(const indexed_vector<T>& y, const vector
}
}
#endif
lean_assert(m_ii.is_OK());
SASSERT(m_ii.is_OK());
m_ii.clear();
m_ii.resize(y.data_size());
lean_assert(m_y_copy.is_OK());
SASSERT(m_y_copy.is_OK());
// put the error into m_y_copy
for (auto k : y.m_index) {
auto & row = m_A.m_rows[k];
@ -399,7 +414,7 @@ void lu<T, X>::find_error_of_yB_indexed(const indexed_vector<T>& y, const vector
m_y_copy.set_value(v, k);
}
}
lean_assert(m_y_copy.is_OK());
SASSERT(m_y_copy.is_OK());
}
@ -419,12 +434,12 @@ void lu<T, X>::solve_yB_with_error_check_indexed(indexed_vector<T> & y, const ve
}
return;
}
lean_assert(m_y_copy.is_OK());
lean_assert(y.is_OK());
SASSERT(m_y_copy.is_OK());
SASSERT(y.is_OK());
if (y.m_index.size() * ratio_of_index_size_to_all_size<T>() < m_A.column_count()) {
m_y_copy = y;
solve_yB_indexed(y);
lean_assert(y.is_OK());
SASSERT(y.is_OK());
if (y.m_index.size() * ratio_of_index_size_to_all_size<T>() >= m_A.column_count()) {
find_error_of_yB(m_y_copy.m_data, y.m_data, basis);
solve_yB(m_y_copy.m_data);
@ -436,7 +451,7 @@ void lu<T, X>::solve_yB_with_error_check_indexed(indexed_vector<T> & y, const ve
solve_yB_indexed(m_y_copy);
add_delta_to_solution_indexed(y);
}
lean_assert(m_y_copy.is_OK());
SASSERT(m_y_copy.is_OK());
} else {
solve_yB_with_error_check(y.m_data, basis);
y.restore_index_and_clean_from_data();
@ -489,7 +504,7 @@ template <typename T, typename X>
void lu<T, X>::perform_transformations_on_w(indexed_vector<T>& w) {
apply_lp_list_to_w(w);
m_Q.apply_reverse_from_left(w);
// TBD does not compile: lean_assert(numeric_traits<T>::precise() || check_vector_for_small_values(w, m_settings));
// TBD does not compile: SASSERT(numeric_traits<T>::precise() || check_vector_for_small_values(w, m_settings));
}
// see Chvatal 24.3
@ -503,7 +518,7 @@ template <typename T, typename X>
void lu<T, X>::apply_lp_list_to_w(indexed_vector<T> & w) {
for (unsigned i = 0; i < m_tail.size(); i++) {
m_tail[i]->apply_from_left_to_T(w, m_settings);
// TBD does not compile: lean_assert(check_vector_for_small_values(w, m_settings));
// TBD does not compile: SASSERT(check_vector_for_small_values(w, m_settings));
}
}
template <typename T, typename X>
@ -570,7 +585,7 @@ unsigned lu<T, X>::transform_U_to_V_by_replacing_column(indexed_vector<T> & w,
return column_to_replace;
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X>
void lu<T, X>::check_vector_w(unsigned entering) {
T * w = new T[m_dim];
@ -595,7 +610,7 @@ void lu<T, X>::check_apply_lp_lists_to_w(T * w) {
permutation_matrix<T, X> qr = m_Q.get_reverse();
apply_to_vector(qr, w);
for (int i = m_dim - 1; i >= 0; i--) {
lean_assert(abs(w[i] - w[i]) < 0.0000001);
SASSERT(abs(w[i] - w[i]) < 0.0000001);
}
}
@ -624,7 +639,7 @@ void lu<T, X>::process_column(int j) {
}
template <typename T, typename X>
bool lu<T, X>::is_correct(const vector<unsigned>& basis) {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
if (get_status() != LU_status::OK) {
return false;
}
@ -637,10 +652,10 @@ bool lu<T, X>::is_correct(const vector<unsigned>& basis) {
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X>
dense_matrix<T, X> lu<T, X>::tail_product() {
lean_assert(tail_size() > 0);
SASSERT(tail_size() > 0);
dense_matrix<T, X> left_side = permutation_matrix<T, X>(m_dim);
for (unsigned i = 0; i < tail_size(); i++) {
matrix<T, X>* lp = get_lp_matrix(i);
@ -690,8 +705,8 @@ template <typename T, typename X>
bool lu<T, X>::all_columns_and_rows_are_active() {
unsigned i = m_dim;
while (i--) {
lean_assert(m_U.col_is_active(i));
lean_assert(m_U.row_is_active(i));
SASSERT(m_U.col_is_active(i));
SASSERT(m_U.row_is_active(i));
}
return true;
}
@ -733,9 +748,9 @@ void lu<T, X>::create_initial_factorization(){
}
}
if (j == m_dim) {
// TBD does not compile: lean_assert(m_U.is_upper_triangular_and_maximums_are_set_correctly_in_rows(m_settings));
// lean_assert(is_correct());
// lean_assert(m_U.is_upper_triangular_and_maximums_are_set_correctly_in_rows(m_settings));
// TBD does not compile: SASSERT(m_U.is_upper_triangular_and_maximums_are_set_correctly_in_rows(m_settings));
// SASSERT(is_correct());
// SASSERT(m_U.is_upper_triangular_and_maximums_are_set_correctly_in_rows(m_settings));
return;
}
j++;
@ -748,12 +763,12 @@ void lu<T, X>::create_initial_factorization(){
}
}
m_dense_LU->update_parent_matrix(m_settings);
lean_assert(m_dense_LU->is_L_matrix());
SASSERT(m_dense_LU->is_L_matrix());
m_dense_LU->conjugate_by_permutation(m_Q);
push_matrix_to_tail(m_dense_LU);
m_refactor_counter = 0;
// lean_assert(is_correct());
// lean_assert(m_U.is_upper_triangular_and_maximums_are_set_correctly_in_rows(m_settings));
// SASSERT(is_correct());
// SASSERT(m_U.is_upper_triangular_and_maximums_are_set_correctly_in_rows(m_settings));
}
template <typename T, typename X>
@ -780,7 +795,7 @@ void lu<T, X>::scan_last_row_to_work_vector(unsigned lowest_row_of_the_bump) {
vector<indexed_value<T>> & last_row_vec = m_U.get_row_values(m_U.adjust_row(lowest_row_of_the_bump));
for (auto & iv : last_row_vec) {
if (is_zero(iv.m_value)) continue;
lean_assert(!m_settings.abs_val_is_smaller_than_drop_tolerance(iv.m_value));
SASSERT(!m_settings.abs_val_is_smaller_than_drop_tolerance(iv.m_value));
unsigned adjusted_col = m_U.adjust_column_inverse(iv.m_index);
if (adjusted_col < lowest_row_of_the_bump) {
m_row_eta_work_vector.set_value(-iv.m_value, adjusted_col);
@ -801,14 +816,14 @@ void lu<T, X>::pivot_and_solve_the_system(unsigned replaced_column, unsigned low
vector<indexed_value<T>> & row = m_U.get_row_values(aj);
for (auto & iv : row) {
unsigned col = m_U.adjust_column_inverse(iv.m_index);
lean_assert(col >= j || numeric_traits<T>::is_zero(iv.m_value));
SASSERT(col >= j || numeric_traits<T>::is_zero(iv.m_value));
if (col == j) continue;
if (numeric_traits<T>::is_zero(iv.m_value)) {
continue;
}
// the -v is for solving the system ( to zero the last row), and +v is for pivoting
T delta = col < lowest_row_of_the_bump? -v * iv.m_value: v * iv.m_value;
lean_assert(numeric_traits<T>::is_zero(delta) == false);
SASSERT(numeric_traits<T>::is_zero(delta) == false);
@ -845,7 +860,7 @@ row_eta_matrix<T, X> *lu<T, X>::get_row_eta_matrix_and_set_row_vector(unsigned r
return nullptr;
}
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
auto ret = new row_eta_matrix<T, X>(replaced_column, lowest_row_of_the_bump, m_dim);
#else
auto ret = new row_eta_matrix<T, X>(replaced_column, lowest_row_of_the_bump);
@ -885,15 +900,15 @@ void lu<T, X>::replace_column(T pivot_elem_for_checking, indexed_vector<T> & w,
push_matrix_to_tail(row_eta);
}
calculate_Lwave_Pwave_for_bump(replaced_column, lowest_row_of_the_bump);
// lean_assert(m_U.is_upper_triangular_and_maximums_are_set_correctly_in_rows(m_settings));
// lean_assert(w.is_OK() && m_row_eta_work_vector.is_OK());
// SASSERT(m_U.is_upper_triangular_and_maximums_are_set_correctly_in_rows(m_settings));
// SASSERT(w.is_OK() && m_row_eta_work_vector.is_OK());
}
template <typename T, typename X>
void lu<T, X>::calculate_Lwave_Pwave_for_bump(unsigned replaced_column, unsigned lowest_row_of_the_bump){
T diagonal_elem;
if (replaced_column < lowest_row_of_the_bump) {
diagonal_elem = m_row_eta_work_vector[lowest_row_of_the_bump];
// lean_assert(m_row_eta_work_vector.is_OK());
// SASSERT(m_row_eta_work_vector.is_OK());
m_U.set_row_from_work_vector_and_clean_work_vector_not_adjusted(m_U.adjust_row(lowest_row_of_the_bump), m_row_eta_work_vector, m_settings);
} else {
diagonal_elem = m_U(lowest_row_of_the_bump, lowest_row_of_the_bump); // todo - get it more efficiently
@ -904,13 +919,13 @@ void lu<T, X>::calculate_Lwave_Pwave_for_bump(unsigned replaced_column, unsigned
}
calculate_Lwave_Pwave_for_last_row(lowest_row_of_the_bump, diagonal_elem);
// lean_assert(m_U.is_upper_triangular_and_maximums_are_set_correctly_in_rows(m_settings));
// SASSERT(m_U.is_upper_triangular_and_maximums_are_set_correctly_in_rows(m_settings));
}
template <typename T, typename X>
void lu<T, X>::calculate_Lwave_Pwave_for_last_row(unsigned lowest_row_of_the_bump, T diagonal_element) {
auto l = new one_elem_on_diag<T, X>(lowest_row_of_the_bump, diagonal_element);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
l->set_number_of_columns(m_dim);
#endif
push_matrix_to_tail(l);
@ -927,11 +942,11 @@ void init_factorization(lu<T, X>* & factorization, static_matrix<T, X> & m_A, ve
// LP_OUT(m_settings, "failing in init_factorization" << std::endl);
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X>
dense_matrix<T, X> get_B(lu<T, X>& f, const vector<unsigned>& basis) {
lean_assert(basis.size() == f.dimension());
lean_assert(basis.size() == f.m_U.dimension());
SASSERT(basis.size() == f.dimension());
SASSERT(basis.size() == f.m_U.dimension());
dense_matrix<T, X> B(f.dimension(), f.dimension());
for (unsigned i = 0; i < f.dimension(); i++)
for (unsigned j = 0; j < f.dimension(); j++)

123
src/util/lp/lu_instances.cpp
View file

@ -1,63 +1,78 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <utility>
#include <memory>
#include <string>
#include "util/vector.h"
#include "util/debug.h"
#include "util/lp/lu.hpp"
template double lean::dot_product<double, double>(vector<double> const&, vector<double> const&);
template lean::lu<double, double>::lu(lean::static_matrix<double, double> const&, vector<unsigned int>&, lean::lp_settings&);
template void lean::lu<double, double>::push_matrix_to_tail(lean::tail_matrix<double, double>*);
template void lean::lu<double, double>::replace_column(double, lean::indexed_vector<double>&, unsigned);
template void lean::lu<double, double>::solve_Bd(unsigned int, lean::indexed_vector<double>&, lean::indexed_vector<double>&);
template lean::lu<double, double>::~lu();
template void lean::lu<lean::mpq, lean::mpq>::push_matrix_to_tail(lean::tail_matrix<lean::mpq, lean::mpq>*);
template void lean::lu<lean::mpq, lean::mpq>::solve_Bd(unsigned int, lean::indexed_vector<lean::mpq>&, lean::indexed_vector<lean::mpq>&);
template lean::lu<lean::mpq, lean::mpq>::~lu();
template void lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >::push_matrix_to_tail(lean::tail_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >*);
template void lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_Bd(unsigned int, lean::indexed_vector<lean::mpq>&, lean::indexed_vector<lean::mpq>&);
template lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >::~lu();
template lean::mpq lean::dot_product<lean::mpq, lean::mpq>(vector<lean::mpq > const&, vector<lean::mpq > const&);
template void lean::init_factorization<double, double>(lean::lu<double, double>*&, lean::static_matrix<double, double>&, vector<unsigned int>&, lean::lp_settings&);
template void lean::init_factorization<lean::mpq, lean::mpq>(lean::lu<lean::mpq, lean::mpq>*&, lean::static_matrix<lean::mpq, lean::mpq>&, vector<unsigned int>&, lean::lp_settings&);
template void lean::init_factorization<lean::mpq, lean::numeric_pair<lean::mpq> >(lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >*&, lean::static_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >&, vector<unsigned int>&, lean::lp_settings&);
#ifdef LEAN_DEBUG
template void lean::print_matrix<double, double>(lean::sparse_matrix<double, double>&, std::ostream & out);
template void lean::print_matrix<lean::mpq, lean::mpq>(lean::static_matrix<lean::mpq, lean::mpq>&, std::ostream&);
template void lean::print_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >(lean::static_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >&, std::ostream&);
template void lean::print_matrix<double, double>(lean::static_matrix<double, double>&, std::ostream & out);
template bool lean::lu<double, double>::is_correct(const vector<unsigned>& basis);
template bool lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >::is_correct( vector<unsigned int> const &);
template lean::dense_matrix<double, double> lean::get_B<double, double>(lean::lu<double, double>&, const vector<unsigned>& basis);
template lean::dense_matrix<lean::mpq, lean::mpq> lean::get_B<lean::mpq, lean::mpq>(lean::lu<lean::mpq, lean::mpq>&, vector<unsigned int> const&);
template double lp::dot_product<double, double>(vector<double> const&, vector<double> const&);
template lp::lu<double, double>::lu(lp::static_matrix<double, double> const&, vector<unsigned int>&, lp::lp_settings&);
template void lp::lu<double, double>::push_matrix_to_tail(lp::tail_matrix<double, double>*);
template void lp::lu<double, double>::replace_column(double, lp::indexed_vector<double>&, unsigned);
template void lp::lu<double, double>::solve_Bd(unsigned int, lp::indexed_vector<double>&, lp::indexed_vector<double>&);
template lp::lu<double, double>::~lu();
template void lp::lu<lp::mpq, lp::mpq>::push_matrix_to_tail(lp::tail_matrix<lp::mpq, lp::mpq>*);
template void lp::lu<lp::mpq, lp::mpq>::solve_Bd(unsigned int, lp::indexed_vector<lp::mpq>&, lp::indexed_vector<lp::mpq>&);
template lp::lu<lp::mpq, lp::mpq>::~lu();
template void lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >::push_matrix_to_tail(lp::tail_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >*);
template void lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_Bd(unsigned int, lp::indexed_vector<lp::mpq>&, lp::indexed_vector<lp::mpq>&);
template lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >::~lu();
template lp::mpq lp::dot_product<lp::mpq, lp::mpq>(vector<lp::mpq > const&, vector<lp::mpq > const&);
template void lp::init_factorization<double, double>(lp::lu<double, double>*&, lp::static_matrix<double, double>&, vector<unsigned int>&, lp::lp_settings&);
template void lp::init_factorization<lp::mpq, lp::mpq>(lp::lu<lp::mpq, lp::mpq>*&, lp::static_matrix<lp::mpq, lp::mpq>&, vector<unsigned int>&, lp::lp_settings&);
template void lp::init_factorization<lp::mpq, lp::numeric_pair<lp::mpq> >(lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >*&, lp::static_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >&, vector<unsigned int>&, lp::lp_settings&);
#ifdef Z3DEBUG
template void lp::print_matrix<double, double>(lp::sparse_matrix<double, double>&, std::ostream & out);
template void lp::print_matrix<lp::mpq, lp::mpq>(lp::static_matrix<lp::mpq, lp::mpq>&, std::ostream&);
template void lp::print_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >(lp::static_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >&, std::ostream&);
template void lp::print_matrix<double, double>(lp::static_matrix<double, double>&, std::ostream & out);
template bool lp::lu<double, double>::is_correct(const vector<unsigned>& basis);
template bool lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >::is_correct( vector<unsigned int> const &);
template lp::dense_matrix<double, double> lp::get_B<double, double>(lp::lu<double, double>&, const vector<unsigned>& basis);
template lp::dense_matrix<lp::mpq, lp::mpq> lp::get_B<lp::mpq, lp::mpq>(lp::lu<lp::mpq, lp::mpq>&, vector<unsigned int> const&);
#endif
template bool lean::lu<double, double>::pivot_the_row(int); // NOLINT
template void lean::lu<double, double>::init_vector_w(unsigned int, lean::indexed_vector<double>&);
template void lean::lu<double, double>::solve_By(vector<double>&);
template void lean::lu<double, double>::solve_By_when_y_is_ready_for_X(vector<double>&);
template void lean::lu<double, double>::solve_yB_with_error_check(vector<double>&, const vector<unsigned>& basis);
template void lean::lu<double, double>::solve_yB_with_error_check_indexed(lean::indexed_vector<double>&, vector<int> const&, const vector<unsigned> & basis, const lp_settings&);
template void lean::lu<lean::mpq, lean::mpq>::replace_column(lean::mpq, lean::indexed_vector<lean::mpq>&, unsigned);
template void lean::lu<lean::mpq, lean::mpq>::solve_By(vector<lean::mpq >&);
template void lean::lu<lean::mpq, lean::mpq>::solve_By_when_y_is_ready_for_X(vector<lean::mpq >&);
template void lean::lu<lean::mpq, lean::mpq>::solve_yB_with_error_check(vector<lean::mpq >&, const vector<unsigned>& basis);
template void lean::lu<lean::mpq, lean::mpq>::solve_yB_with_error_check_indexed(lean::indexed_vector<lean::mpq>&, vector< int > const&, const vector<unsigned> & basis, const lp_settings&);
template void lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_yB_with_error_check_indexed(lean::indexed_vector<lean::mpq>&, vector< int > const&, const vector<unsigned> & basis, const lp_settings&);
template void lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >::init_vector_w(unsigned int, lean::indexed_vector<lean::mpq>&);
template void lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >::replace_column(lean::mpq, lean::indexed_vector<lean::mpq>&, unsigned);
template void lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_Bd_faster(unsigned int, lean::indexed_vector<lean::mpq>&);
template void lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_By(vector<lean::numeric_pair<lean::mpq> >&);
template void lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_By_when_y_is_ready_for_X(vector<lean::numeric_pair<lean::mpq> >&);
template void lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_yB_with_error_check(vector<lean::mpq >&, const vector<unsigned>& basis);
template void lean::lu<lean::mpq, lean::mpq>::solve_By(lean::indexed_vector<lean::mpq>&);
template void lean::lu<double, double>::solve_By(lean::indexed_vector<double>&);
template void lean::lu<double, double>::solve_yB_indexed(lean::indexed_vector<double>&);
template void lean::lu<lean::mpq, lean::mpq>::solve_yB_indexed(lean::indexed_vector<lean::mpq>&);
template void lean::lu<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_yB_indexed(lean::indexed_vector<lean::mpq>&);
template void lean::lu<lean::mpq, lean::mpq>::solve_By_for_T_indexed_only(lean::indexed_vector<lean::mpq>&, lean::lp_settings const&);
template void lean::lu<double, double>::solve_By_for_T_indexed_only(lean::indexed_vector<double>&, lean::lp_settings const&);
template bool lp::lu<double, double>::pivot_the_row(int); // NOLINT
template void lp::lu<double, double>::init_vector_w(unsigned int, lp::indexed_vector<double>&);
template void lp::lu<double, double>::solve_By(vector<double>&);
template void lp::lu<double, double>::solve_By_when_y_is_ready_for_X(vector<double>&);
template void lp::lu<double, double>::solve_yB_with_error_check(vector<double>&, const vector<unsigned>& basis);
template void lp::lu<double, double>::solve_yB_with_error_check_indexed(lp::indexed_vector<double>&, vector<int> const&, const vector<unsigned> & basis, const lp_settings&);
template void lp::lu<lp::mpq, lp::mpq>::replace_column(lp::mpq, lp::indexed_vector<lp::mpq>&, unsigned);
template void lp::lu<lp::mpq, lp::mpq>::solve_By(vector<lp::mpq >&);
template void lp::lu<lp::mpq, lp::mpq>::solve_By_when_y_is_ready_for_X(vector<lp::mpq >&);
template void lp::lu<lp::mpq, lp::mpq>::solve_yB_with_error_check(vector<lp::mpq >&, const vector<unsigned>& basis);
template void lp::lu<lp::mpq, lp::mpq>::solve_yB_with_error_check_indexed(lp::indexed_vector<lp::mpq>&, vector< int > const&, const vector<unsigned> & basis, const lp_settings&);
template void lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_yB_with_error_check_indexed(lp::indexed_vector<lp::mpq>&, vector< int > const&, const vector<unsigned> & basis, const lp_settings&);
template void lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >::init_vector_w(unsigned int, lp::indexed_vector<lp::mpq>&);
template void lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >::replace_column(lp::mpq, lp::indexed_vector<lp::mpq>&, unsigned);
template void lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_Bd_faster(unsigned int, lp::indexed_vector<lp::mpq>&);
template void lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_By(vector<lp::numeric_pair<lp::mpq> >&);
template void lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_By_when_y_is_ready_for_X(vector<lp::numeric_pair<lp::mpq> >&);
template void lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_yB_with_error_check(vector<lp::mpq >&, const vector<unsigned>& basis);
template void lp::lu<lp::mpq, lp::mpq>::solve_By(lp::indexed_vector<lp::mpq>&);
template void lp::lu<double, double>::solve_By(lp::indexed_vector<double>&);
template void lp::lu<double, double>::solve_yB_indexed(lp::indexed_vector<double>&);
template void lp::lu<lp::mpq, lp::mpq>::solve_yB_indexed(lp::indexed_vector<lp::mpq>&);
template void lp::lu<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_yB_indexed(lp::indexed_vector<lp::mpq>&);
template void lp::lu<lp::mpq, lp::mpq>::solve_By_for_T_indexed_only(lp::indexed_vector<lp::mpq>&, lp::lp_settings const&);
template void lp::lu<double, double>::solve_By_for_T_indexed_only(lp::indexed_vector<double>&, lp::lp_settings const&);

25
src/util/lp/matrix.h
View file

@ -1,14 +1,29 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#ifdef Z3DEBUG
#pragma once
#include "util/lp/numeric_pair.h"
#include "util/vector.h"
#include <string>
#include "util/lp/lp_settings.h"
namespace lean {
namespace lp {
// used for debugging purposes only
template <typename T, typename X>
class matrix {

25
src/util/lp/matrix.hpp
View file

@ -1,13 +1,28 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#ifdef Z3DEBUG
#include <cmath>
#include <string>
#include "util/lp/matrix.h"
namespace lean {
namespace lp {
template <typename T, typename X>
bool matrix<T, X>::is_equal(const matrix<T, X>& other) {
if (other.row_count() != row_count() || other.column_count() != column_count())

37
src/util/lp/matrix_instances.cpp
View file

@ -1,16 +1,31 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/lp_settings.h"
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
#include "util/lp/matrix.hpp"
#include "util/lp/static_matrix.h"
#include <string>
template void lean::print_matrix<double, double>(lean::matrix<double, double> const*, std::ostream & out);
template bool lean::matrix<double, double>::is_equal(lean::matrix<double, double> const&);
template void lean::print_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >(lean::matrix<lean::mpq, lean::numeric_pair<lean::mpq> > const *, std::basic_ostream<char, std::char_traits<char> > &);
template void lean::print_matrix<lean::mpq, lean::mpq>(lean::matrix<lean::mpq, lean::mpq> const*, std::ostream&);
template bool lean::matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::is_equal(lean::matrix<lean::mpq, lean::numeric_pair<lean::mpq> > const&);
template bool lean::matrix<lean::mpq, lean::mpq>::is_equal(lean::matrix<lean::mpq, lean::mpq> const&);
template void lp::print_matrix<double, double>(lp::matrix<double, double> const*, std::ostream & out);
template bool lp::matrix<double, double>::is_equal(lp::matrix<double, double> const&);
template void lp::print_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >(lp::matrix<lp::mpq, lp::numeric_pair<lp::mpq> > const *, std::basic_ostream<char, std::char_traits<char> > &);
template void lp::print_matrix<lp::mpq, lp::mpq>(lp::matrix<lp::mpq, lp::mpq> const*, std::ostream&);
template bool lp::matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::is_equal(lp::matrix<lp::mpq, lp::numeric_pair<lp::mpq> > const&);
template bool lp::matrix<lp::mpq, lp::mpq>::is_equal(lp::matrix<lp::mpq, lp::mpq> const&);
#endif

41
src/util/lp/mps_reader.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
@ -19,7 +34,7 @@
#include "util/lp/lar_solver.h"
#include "util/lp/lp_utils.h"
#include "util/lp/lp_solver.h"
namespace lean {
namespace lp {
inline bool my_white_space(const char & a) {
return a == ' ' || a == '\t';
}
@ -160,9 +175,9 @@ class mps_reader {
if (m_line[i] == ' ')
break;
}
lean_assert(m_line.size() >= offset);
lean_assert(m_line.size() >> i);
lean_assert(i >= offset);
SASSERT(m_line.size() >= offset);
SASSERT(m_line.size() >> i);
SASSERT(i >= offset);
return m_line.substr(offset, i - offset);
}
@ -497,7 +512,7 @@ class mps_reader {
void create_or_update_bound() {
const unsigned name_offset = 14;
lean_assert(m_line.size() >= 14);
SASSERT(m_line.size() >= 14);
vector<std::string> bound_string = split_and_trim(m_line.substr(name_offset, m_line.size()));
if (bound_string.size() == 0) {
@ -603,7 +618,7 @@ class mps_reader {
}
for (auto s : row_with_range->m_row_columns) {
lean_assert(m_columns.find(s.first) != m_columns.end());
SASSERT(m_columns.find(s.first) != m_columns.end());
other_bound_range_row->m_row_columns[s.first] = s.second;
}
}
@ -679,7 +694,7 @@ class mps_reader {
if (row->m_name != m_cost_row_name) {
solver->add_constraint(get_relation_from_row(row->m_type), row->m_right_side, row->m_index);
for (auto s : row->m_row_columns) {
lean_assert(m_columns.find(s.first) != m_columns.end());
SASSERT(m_columns.find(s.first) != m_columns.end());
solver->set_row_column_coefficient(row->m_index, m_columns[s.first]->m_index, s.second);
}
} else {
@ -714,7 +729,7 @@ class mps_reader {
void set_solver_cost(row * row, lp_solver<T, X> *solver) {
for (auto s : row->m_row_columns) {
std::string name = s.first;
lean_assert(m_columns.find(name) != m_columns.end());
SASSERT(m_columns.find(name) != m_columns.end());
mps_reader::column * col = m_columns[name];
solver->set_cost_for_column(col->m_index, s.second);
}
@ -723,7 +738,7 @@ class mps_reader {
public:
void set_message_stream(std::ostream * o) {
lean_assert(o != nullptr);
SASSERT(o != nullptr);
m_message_stream = o;
}
vector<std::string> column_names() {

80
src/util/lp/numeric_pair.h
View file

@ -1,33 +1,38 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
The idea is that it is only one different file in Lean and z3 source inside of LP
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#define lp_for_z3
#include <string>
#include <cmath>
#include <algorithm>
#ifdef lp_for_z3
#include "../rational.h"
#include "../sstream.h"
#include "../z3_exception.h"
#else
// include "util/numerics/mpq.h"
// include "util/numerics/numeric_traits.h"
#endif
namespace lean {
#ifdef lp_for_z3 // rename rationals
typedef rational mpq;
#else
typedef lean::mpq mpq;
#endif
namespace lp {
typedef rational mpq; // rename rationals
template <typename T>
std::string T_to_string(const T & t); // forward definition
#ifdef lp_for_z3
template <typename T> class numeric_traits {};
template <> class numeric_traits<unsigned> {
@ -67,14 +72,13 @@ template <> class numeric_traits<double> {
static bool is_pos(const rational & d) {return d.is_pos();}
static bool is_neg(const rational & d) {return d.is_neg();}
};
#endif
template <typename X, typename Y>
struct convert_struct {
static X convert(const Y & y){ return X(y);}
static bool is_epsilon_small(const X & x, const double & y) { return std::abs(numeric_traits<X>::get_double(x)) < y; }
static bool below_bound_numeric(const X &, const X &, const Y &) { /*lean_unreachable();*/ return false;}
static bool above_bound_numeric(const X &, const X &, const Y &) { /*lean_unreachable();*/ return false; }
static bool below_bound_numeric(const X &, const X &, const Y &) { /*SASSERT(false);*/ return false;}
static bool above_bound_numeric(const X &, const X &, const Y &) { /*SASSERT(false);*/ return false; }
};
@ -104,9 +108,9 @@ struct numeric_pair {
template <typename X>
numeric_pair(const X & n) : x(n), y(0) {
}
numeric_pair(const numeric_pair<T> & n) : x(n.x), y(n.y) {}
template <typename X, typename Y>
numeric_pair(X xp, Y yp) : x(convert_struct<T, X>::convert(xp)), y(convert_struct<T, Y>::convert(yp)) {}
@ -144,16 +148,16 @@ struct numeric_pair {
}
numeric_pair operator/(const numeric_pair &) const {
// lean_unreachable();
// SASSERT(false);
}
numeric_pair operator+(const numeric_pair & a) const {
return numeric_pair(a.x + x, a.y + y);
}
numeric_pair operator*(const numeric_pair & /*a*/) const {
// lean_unreachable();
// SASSERT(false);
}
numeric_pair& operator+=(const numeric_pair & a) {
@ -188,14 +192,14 @@ struct numeric_pair {
return numeric_pair(-x, -y);
}
static bool precize() { return lean::numeric_traits<T>::precize();}
static bool precize() { return lp::numeric_traits<T>::precize();}
bool is_zero() const { return x.is_zero() && y.is_zero(); }
bool is_pos() const { return x.is_pos() || (x.is_zero() && y.is_pos());}
bool is_neg() const { return x.is_neg() || (x.is_zero() && y.is_neg());}
std::string to_string() const {
return std::string("(") + T_to_string(x) + ", " + T_to_string(y) + ")";
}
@ -225,15 +229,15 @@ numeric_pair<T> operator/(const numeric_pair<T> & r, const X & a) {
}
// template <numeric_pair, typename T> bool precise() { return numeric_traits<T>::precise();}
template <typename T> double get_double(const lean::numeric_pair<T> & ) { /* lean_unreachable(); */ return 0;}
template <typename T> double get_double(const lp::numeric_pair<T> & ) { /* SASSERT(false); */ return 0;}
template <typename T>
class numeric_traits<lean::numeric_pair<T>> {
class numeric_traits<lp::numeric_pair<T>> {
public:
static bool precise() { return numeric_traits<T>::precise();}
static lean::numeric_pair<T> zero() { return lean::numeric_pair<T>(numeric_traits<T>::zero(), numeric_traits<T>::zero()); }
static bool is_zero(const lean::numeric_pair<T> & v) { return numeric_traits<T>::is_zero(v.x) && numeric_traits<T>::is_zero(v.y); }
static double get_double(const lean::numeric_pair<T> & v){ return numeric_traits<T>::get_double(v.x); } // just return the double of the first coordinate
static double one() { /*lean_unreachable();*/ return 0;}
static lp::numeric_pair<T> zero() { return lp::numeric_pair<T>(numeric_traits<T>::zero(), numeric_traits<T>::zero()); }
static bool is_zero(const lp::numeric_pair<T> & v) { return numeric_traits<T>::is_zero(v.x) && numeric_traits<T>::is_zero(v.y); }
static double get_double(const lp::numeric_pair<T> & v){ return numeric_traits<T>::get_double(v.x); } // just return the double of the first coordinate
static double one() { /*SASSERT(false);*/ return 0;}
static bool is_pos(const numeric_pair<T> &p) {
return numeric_traits<T>::is_pos(p.x) ||
(numeric_traits<T>::is_zero(p.x) && numeric_traits<T>::is_pos(p.y));
@ -242,7 +246,7 @@ class numeric_traits<lean::numeric_pair<T>> {
return numeric_traits<T>::is_neg(p.x) ||
(numeric_traits<T>::is_zero(p.x) && numeric_traits<T>::is_neg(p.y));
}
};
template <>
@ -263,11 +267,11 @@ struct convert_struct<numeric_pair<T>, double> {
return convert_struct<T, double>::is_epsilon_small(p.x, eps) && convert_struct<T, double>::is_epsilon_small(p.y, eps);
}
static bool below_bound_numeric(const numeric_pair<T> &, const numeric_pair<T> &, const double &) {
// lean_unreachable();
// SASSERT(false);
return false;
}
static bool above_bound_numeric(const numeric_pair<T> &, const numeric_pair<T> &, const double &) {
// lean_unreachable();
// SASSERT(false);
return false;
}
};

33
src/util/lp/permutation_matrix.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
#include <algorithm>
@ -12,8 +27,8 @@
#include "util/lp/lp_settings.h"
#include "util/lp/matrix.h"
#include "util/lp/tail_matrix.h"
namespace lean {
#ifdef LEAN_DEBUG
namespace lp {
#ifdef Z3DEBUG
inline bool is_even(int k) { return (k/2)*2 == k; }
#endif
@ -50,7 +65,7 @@ class permutation_matrix : public tail_matrix<T, X> {
void init(unsigned length);
unsigned get_rev(unsigned i) { return m_rev[i]; }
bool is_dense() const { return false; }
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
permutation_matrix get_inverse() const {
return permutation_matrix(size(), m_rev);
}
@ -86,14 +101,14 @@ class permutation_matrix : public tail_matrix<T, X> {
void apply_reverse_from_right_to_X(vector<X> & w);
void set_val(unsigned i, unsigned pi) {
lean_assert(i < size() && pi < size()); m_permutation[i] = pi; m_rev[pi] = i; }
SASSERT(i < size() && pi < size()); m_permutation[i] = pi; m_rev[pi] = i; }
void transpose_from_left(unsigned i, unsigned j);
unsigned apply_reverse(unsigned i) const { return m_rev[i]; }
void transpose_from_right(unsigned i, unsigned j);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
T get_elem(unsigned i, unsigned j) const{
return m_permutation[i] == j? numeric_traits<T>::one() : numeric_traits<T>::zero();
}

83
src/util/lp/permutation_matrix.hpp
View file

@ -1,10 +1,25 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/vector.h"
#include "util/lp/permutation_matrix.h"
namespace lean {
namespace lp {
template <typename T, typename X> permutation_matrix<T, X>::permutation_matrix(unsigned length): m_permutation(length), m_rev(length), m_T_buffer(length), m_X_buffer(length) {
for (unsigned i = 0; i < length; i++) { // do not change the direction of the loop because of the vectorization bug in clang3.3
m_permutation[i] = m_rev[i] = i;
@ -27,7 +42,7 @@ template <typename T, typename X> void permutation_matrix<T, X>::init(unsigned l
}
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X> void permutation_matrix<T, X>::print(std::ostream & out) const {
out << "[";
for (unsigned i = 0; i < size(); i++) {
@ -44,13 +59,13 @@ template <typename T, typename X> void permutation_matrix<T, X>::print(std::ostr
template <typename T, typename X>
void permutation_matrix<T, X>::apply_from_left(vector<X> & w, lp_settings & ) {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// dense_matrix<L, X> deb(*this);
// L * deb_w = clone_vector<L>(w, row_count());
// deb.apply_from_left(deb_w);
#endif
// std::cout << " apply_from_left " << std::endl;
lean_assert(m_X_buffer.size() == w.size());
SASSERT(m_X_buffer.size() == w.size());
unsigned i = size();
while (i-- > 0) {
m_X_buffer[i] = w[m_permutation[i]];
@ -59,8 +74,8 @@ void permutation_matrix<T, X>::apply_from_left(vector<X> & w, lp_settings & ) {
while (i-- > 0) {
w[i] = m_X_buffer[i];
}
#ifdef LEAN_DEBUG
// lean_assert(vectors_are_equal<L>(deb_w, w, row_count()));
#ifdef Z3DEBUG
// SASSERT(vectors_are_equal<L>(deb_w, w, row_count()));
// delete [] deb_w;
#endif
}
@ -81,12 +96,12 @@ void permutation_matrix<T, X>::apply_from_left_to_T(indexed_vector<T> & w, lp_se
}
template <typename T, typename X> void permutation_matrix<T, X>::apply_from_right(vector<T> & w) {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// dense_matrix<T, X> deb(*this);
// T * deb_w = clone_vector<T>(w, row_count());
// deb.apply_from_right(deb_w);
#endif
lean_assert(m_T_buffer.size() == w.size());
SASSERT(m_T_buffer.size() == w.size());
for (unsigned i = 0; i < size(); i++) {
m_T_buffer[i] = w[m_rev[i]];
}
@ -94,14 +109,14 @@ template <typename T, typename X> void permutation_matrix<T, X>::apply_from_righ
for (unsigned i = 0; i < size(); i++) {
w[i] = m_T_buffer[i];
}
#ifdef LEAN_DEBUG
// lean_assert(vectors_are_equal<T>(deb_w, w, row_count()));
#ifdef Z3DEBUG
// SASSERT(vectors_are_equal<T>(deb_w, w, row_count()));
// delete [] deb_w;
#endif
}
template <typename T, typename X> void permutation_matrix<T, X>::apply_from_right(indexed_vector<T> & w) {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
vector<T> wcopy(w.m_data);
apply_from_right(wcopy);
#endif
@ -117,9 +132,9 @@ template <typename T, typename X> void permutation_matrix<T, X>::apply_from_righ
unsigned pj = m_permutation[j];
w.set_value(buffer[i], pj);
}
lean_assert(w.is_OK());
#ifdef LEAN_DEBUG
lean_assert(vectors_are_equal(wcopy, w.m_data));
SASSERT(w.is_OK());
#ifdef Z3DEBUG
SASSERT(vectors_are_equal(wcopy, w.m_data));
#endif
}
@ -147,7 +162,7 @@ void permutation_matrix<T, X>::clear_data(indexed_vector<L> & w) {
template <typename T, typename X>template <typename L>
void permutation_matrix<T, X>::apply_reverse_from_left(indexed_vector<L> & w) {
// the result will be w = p(-1) * w
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// dense_matrix<L, X> deb(get_reverse());
// L * deb_w = clone_vector<L>(w.m_data, row_count());
// deb.apply_from_left(deb_w);
@ -165,8 +180,8 @@ void permutation_matrix<T, X>::apply_reverse_from_left(indexed_vector<L> & w) {
w[j] = t[i];
w.m_index[i] = j;
}
#ifdef LEAN_DEBUG
// lean_assert(vectors_are_equal<L>(deb_w, w.m_data, row_count()));
#ifdef Z3DEBUG
// SASSERT(vectors_are_equal<L>(deb_w, w.m_data, row_count()));
// delete [] deb_w;
#endif
}
@ -174,7 +189,7 @@ void permutation_matrix<T, X>::apply_reverse_from_left(indexed_vector<L> & w) {
template <typename T, typename X>
void permutation_matrix<T, X>::apply_reverse_from_left_to_T(vector<T> & w) {
// the result will be w = p(-1) * w
lean_assert(m_T_buffer.size() == w.size());
SASSERT(m_T_buffer.size() == w.size());
unsigned i = size();
while (i-- > 0) {
m_T_buffer[m_permutation[i]] = w[i];
@ -187,7 +202,7 @@ void permutation_matrix<T, X>::apply_reverse_from_left_to_T(vector<T> & w) {
template <typename T, typename X>
void permutation_matrix<T, X>::apply_reverse_from_left_to_X(vector<X> & w) {
// the result will be w = p(-1) * w
lean_assert(m_X_buffer.size() == w.size());
SASSERT(m_X_buffer.size() == w.size());
unsigned i = size();
while (i-- > 0) {
m_X_buffer[m_permutation[i]] = w[i];
@ -201,7 +216,7 @@ void permutation_matrix<T, X>::apply_reverse_from_left_to_X(vector<X> & w) {
template <typename T, typename X>
void permutation_matrix<T, X>::apply_reverse_from_right_to_T(vector<T> & w) {
// the result will be w = w * p(-1)
lean_assert(m_T_buffer.size() == w.size());
SASSERT(m_T_buffer.size() == w.size());
unsigned i = size();
while (i-- > 0) {
m_T_buffer[i] = w[m_permutation[i]];
@ -215,11 +230,11 @@ void permutation_matrix<T, X>::apply_reverse_from_right_to_T(vector<T> & w) {
template <typename T, typename X>
void permutation_matrix<T, X>::apply_reverse_from_right_to_T(indexed_vector<T> & w) {
// the result will be w = w * p(-1)
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// vector<T> wcopy(w.m_data);
// apply_reverse_from_right_to_T(wcopy);
#endif
lean_assert(w.is_OK());
SASSERT(w.is_OK());
vector<T> tmp;
vector<unsigned> tmp_index(w.m_index);
for (auto i : w.m_index) {
@ -232,15 +247,15 @@ void permutation_matrix<T, X>::apply_reverse_from_right_to_T(indexed_vector<T> &
w.set_value(tmp[k], m_rev[j]);
}
// lean_assert(w.is_OK());
// lean_assert(vectors_are_equal(w.m_data, wcopy));
// SASSERT(w.is_OK());
// SASSERT(vectors_are_equal(w.m_data, wcopy));
}
template <typename T, typename X>
void permutation_matrix<T, X>::apply_reverse_from_right_to_X(vector<X> & w) {
// the result will be w = w * p(-1)
lean_assert(m_X_buffer.size() == w.size());
SASSERT(m_X_buffer.size() == w.size());
unsigned i = size();
while (i-- > 0) {
m_X_buffer[i] = w[m_permutation[i]];
@ -253,7 +268,7 @@ void permutation_matrix<T, X>::apply_reverse_from_right_to_X(vector<X> & w) {
template <typename T, typename X> void permutation_matrix<T, X>::transpose_from_left(unsigned i, unsigned j) {
// the result will be this = (i,j)*this
lean_assert(i < size() && j < size() && i != j);
SASSERT(i < size() && j < size() && i != j);
auto pi = m_rev[i];
auto pj = m_rev[j];
set_val(pi, j);
@ -262,7 +277,7 @@ template <typename T, typename X> void permutation_matrix<T, X>::transpose_from_
template <typename T, typename X> void permutation_matrix<T, X>::transpose_from_right(unsigned i, unsigned j) {
// the result will be this = this * (i,j)
lean_assert(i < size() && j < size() && i != j);
SASSERT(i < size() && j < size() && i != j);
auto pi = m_permutation[i];
auto pj = m_permutation[j];
set_val(i, pj);
@ -271,7 +286,7 @@ template <typename T, typename X> void permutation_matrix<T, X>::transpose_from_
template <typename T, typename X> void permutation_matrix<T, X>::multiply_by_permutation_from_left(permutation_matrix<T, X> & p) {
m_work_array = m_permutation;
lean_assert(p.size() == size());
SASSERT(p.size() == size());
unsigned i = size();
while (i-- > 0) {
set_val(i, m_work_array[p[i]]); // we have m(P)*m(Q) = m(QP), where m is the matrix of the permutation
@ -281,7 +296,7 @@ template <typename T, typename X> void permutation_matrix<T, X>::multiply_by_per
// this is multiplication in the matrix sense
template <typename T, typename X> void permutation_matrix<T, X>::multiply_by_permutation_from_right(permutation_matrix<T, X> & p) {
m_work_array = m_permutation;
lean_assert(p.size() == size());
SASSERT(p.size() == size());
unsigned i = size();
while (i-- > 0)
set_val(i, p[m_work_array[i]]); // we have m(P)*m(Q) = m(QP), where m is the matrix of the permutation
@ -289,7 +304,7 @@ template <typename T, typename X> void permutation_matrix<T, X>::multiply_by_per
}
template <typename T, typename X> void permutation_matrix<T, X>::multiply_by_reverse_from_right(permutation_matrix<T, X> & q){ // todo : condensed permutations ?
lean_assert(q.size() == size());
SASSERT(q.size() == size());
m_work_array = m_permutation;
// the result is this = this*q(-1)
unsigned i = size();

115
src/util/lp/permutation_matrix_instances.cpp
View file

@ -1,55 +1,70 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <memory>
#include "util/vector.h"
#include "util/lp/permutation_matrix.hpp"
#include "util/lp/numeric_pair.h"
template void lean::permutation_matrix<double, double>::apply_from_right(vector<double>&);
template void lean::permutation_matrix<double, double>::init(unsigned int);
template void lean::permutation_matrix<lean::mpq, lean::mpq>::init(unsigned int);
template void lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq>>::init(unsigned int);
template bool lean::permutation_matrix<double, double>::is_identity() const;
template void lean::permutation_matrix<double, double>::multiply_by_permutation_from_left(lean::permutation_matrix<double, double>&);
template void lean::permutation_matrix<double, double>::multiply_by_permutation_reverse_from_left(lean::permutation_matrix<double, double>&);
template void lean::permutation_matrix<double, double>::multiply_by_reverse_from_right(lean::permutation_matrix<double, double>&);
template lean::permutation_matrix<double, double>::permutation_matrix(unsigned int, vector<unsigned int> const&);
template void lean::permutation_matrix<double, double>::transpose_from_left(unsigned int, unsigned int);
template void lp::permutation_matrix<double, double>::apply_from_right(vector<double>&);
template void lp::permutation_matrix<double, double>::init(unsigned int);
template void lp::permutation_matrix<lp::mpq, lp::mpq>::init(unsigned int);
template void lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq>>::init(unsigned int);
template bool lp::permutation_matrix<double, double>::is_identity() const;
template void lp::permutation_matrix<double, double>::multiply_by_permutation_from_left(lp::permutation_matrix<double, double>&);
template void lp::permutation_matrix<double, double>::multiply_by_permutation_reverse_from_left(lp::permutation_matrix<double, double>&);
template void lp::permutation_matrix<double, double>::multiply_by_reverse_from_right(lp::permutation_matrix<double, double>&);
template lp::permutation_matrix<double, double>::permutation_matrix(unsigned int, vector<unsigned int> const&);
template void lp::permutation_matrix<double, double>::transpose_from_left(unsigned int, unsigned int);
template void lean::permutation_matrix<lean::mpq, lean::mpq>::apply_from_right(vector<lean::mpq>&);
template bool lean::permutation_matrix<lean::mpq, lean::mpq>::is_identity() const;
template void lean::permutation_matrix<lean::mpq, lean::mpq>::multiply_by_permutation_from_left(lean::permutation_matrix<lean::mpq, lean::mpq>&);
template void lean::permutation_matrix<lean::mpq, lean::mpq>::multiply_by_permutation_from_right(lean::permutation_matrix<lean::mpq, lean::mpq>&);
template void lean::permutation_matrix<lean::mpq, lean::mpq>::multiply_by_permutation_reverse_from_left(lean::permutation_matrix<lean::mpq, lean::mpq>&);
template void lean::permutation_matrix<lean::mpq, lean::mpq>::multiply_by_reverse_from_right(lean::permutation_matrix<lean::mpq, lean::mpq>&);
template lean::permutation_matrix<lean::mpq, lean::mpq>::permutation_matrix(unsigned int);
template void lean::permutation_matrix<lean::mpq, lean::mpq>::transpose_from_left(unsigned int, unsigned int);
template void lean::permutation_matrix<lean::mpq, lean::mpq>::transpose_from_right(unsigned int, unsigned int);
template void lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::apply_from_right(vector<lean::mpq>&);
template bool lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::is_identity() const;
template void lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::multiply_by_permutation_from_left(lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >&);
template void lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::multiply_by_permutation_from_right(lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >&);
template void lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::multiply_by_permutation_reverse_from_left(lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >&);
template void lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::multiply_by_reverse_from_right(lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >&);
template lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::permutation_matrix(unsigned int);
template void lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::transpose_from_left(unsigned int, unsigned int);
template void lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::transpose_from_right(unsigned int, unsigned int);
template void lean::permutation_matrix<double, double>::apply_reverse_from_left<double>(lean::indexed_vector<double>&);
template void lean::permutation_matrix<double, double>::apply_reverse_from_left_to_T(vector<double>&);
template void lean::permutation_matrix<double, double>::apply_reverse_from_right_to_T(vector<double>&);
template void lean::permutation_matrix<double, double>::transpose_from_right(unsigned int, unsigned int);
template void lean::permutation_matrix<lean::mpq, lean::mpq>::apply_reverse_from_left<lean::mpq>(lean::indexed_vector<lean::mpq>&);
template void lean::permutation_matrix<lean::mpq, lean::mpq>::apply_reverse_from_left_to_T(vector<lean::mpq>&);
template void lean::permutation_matrix<lean::mpq, lean::mpq>::apply_reverse_from_right_to_T(vector<lean::mpq>&);
template void lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::apply_reverse_from_left<lean::mpq>(lean::indexed_vector<lean::mpq>&);
template void lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::apply_reverse_from_left_to_T(vector<lean::mpq>&);
template void lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::apply_reverse_from_right_to_T(vector<lean::mpq >&);
template void lean::permutation_matrix<double, double>::multiply_by_permutation_from_right(lean::permutation_matrix<double, double>&);
template lean::permutation_matrix<double, double>::permutation_matrix(unsigned int);
template void lean::permutation_matrix<double, double>::apply_reverse_from_left_to_X(vector<double> &);
template void lean::permutation_matrix< lean::mpq, lean::mpq>::apply_reverse_from_left_to_X(vector<lean::mpq> &);
template void lean::permutation_matrix< lean::mpq, lean::numeric_pair< lean::mpq> >::apply_reverse_from_left_to_X(vector<lean::numeric_pair< lean::mpq>> &);
template void lean::permutation_matrix<double, double>::apply_reverse_from_right_to_T(lean::indexed_vector<double>&);
template void lean::permutation_matrix<lean::mpq, lean::mpq>::apply_reverse_from_right_to_T(lean::indexed_vector<lean::mpq>&);
template void lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::apply_reverse_from_right_to_T(lean::indexed_vector<lean::mpq>&);
template void lp::permutation_matrix<lp::mpq, lp::mpq>::apply_from_right(vector<lp::mpq>&);
template bool lp::permutation_matrix<lp::mpq, lp::mpq>::is_identity() const;
template void lp::permutation_matrix<lp::mpq, lp::mpq>::multiply_by_permutation_from_left(lp::permutation_matrix<lp::mpq, lp::mpq>&);
template void lp::permutation_matrix<lp::mpq, lp::mpq>::multiply_by_permutation_from_right(lp::permutation_matrix<lp::mpq, lp::mpq>&);
template void lp::permutation_matrix<lp::mpq, lp::mpq>::multiply_by_permutation_reverse_from_left(lp::permutation_matrix<lp::mpq, lp::mpq>&);
template void lp::permutation_matrix<lp::mpq, lp::mpq>::multiply_by_reverse_from_right(lp::permutation_matrix<lp::mpq, lp::mpq>&);
template lp::permutation_matrix<lp::mpq, lp::mpq>::permutation_matrix(unsigned int);
template void lp::permutation_matrix<lp::mpq, lp::mpq>::transpose_from_left(unsigned int, unsigned int);
template void lp::permutation_matrix<lp::mpq, lp::mpq>::transpose_from_right(unsigned int, unsigned int);
template void lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::apply_from_right(vector<lp::mpq>&);
template bool lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::is_identity() const;
template void lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::multiply_by_permutation_from_left(lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >&);
template void lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::multiply_by_permutation_from_right(lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >&);
template void lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::multiply_by_permutation_reverse_from_left(lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >&);
template void lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::multiply_by_reverse_from_right(lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >&);
template lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::permutation_matrix(unsigned int);
template void lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::transpose_from_left(unsigned int, unsigned int);
template void lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::transpose_from_right(unsigned int, unsigned int);
template void lp::permutation_matrix<double, double>::apply_reverse_from_left<double>(lp::indexed_vector<double>&);
template void lp::permutation_matrix<double, double>::apply_reverse_from_left_to_T(vector<double>&);
template void lp::permutation_matrix<double, double>::apply_reverse_from_right_to_T(vector<double>&);
template void lp::permutation_matrix<double, double>::transpose_from_right(unsigned int, unsigned int);
template void lp::permutation_matrix<lp::mpq, lp::mpq>::apply_reverse_from_left<lp::mpq>(lp::indexed_vector<lp::mpq>&);
template void lp::permutation_matrix<lp::mpq, lp::mpq>::apply_reverse_from_left_to_T(vector<lp::mpq>&);
template void lp::permutation_matrix<lp::mpq, lp::mpq>::apply_reverse_from_right_to_T(vector<lp::mpq>&);
template void lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::apply_reverse_from_left<lp::mpq>(lp::indexed_vector<lp::mpq>&);
template void lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::apply_reverse_from_left_to_T(vector<lp::mpq>&);
template void lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::apply_reverse_from_right_to_T(vector<lp::mpq >&);
template void lp::permutation_matrix<double, double>::multiply_by_permutation_from_right(lp::permutation_matrix<double, double>&);
template lp::permutation_matrix<double, double>::permutation_matrix(unsigned int);
template void lp::permutation_matrix<double, double>::apply_reverse_from_left_to_X(vector<double> &);
template void lp::permutation_matrix< lp::mpq, lp::mpq>::apply_reverse_from_left_to_X(vector<lp::mpq> &);
template void lp::permutation_matrix< lp::mpq, lp::numeric_pair< lp::mpq> >::apply_reverse_from_left_to_X(vector<lp::numeric_pair< lp::mpq>> &);
template void lp::permutation_matrix<double, double>::apply_reverse_from_right_to_T(lp::indexed_vector<double>&);
template void lp::permutation_matrix<lp::mpq, lp::mpq>::apply_reverse_from_right_to_T(lp::indexed_vector<lp::mpq>&);
template void lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::apply_reverse_from_right_to_T(lp::indexed_vector<lp::mpq>&);

47
src/util/lp/quick_xplain.cpp
View file

@ -1,9 +1,24 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/lar_solver.h"
namespace lean {
namespace lp {
quick_xplain::quick_xplain(vector<std::pair<mpq, constraint_index>> & explanation, const lar_solver & ls, lar_solver & qsol) :
m_explanation(explanation),
m_parent_solver(ls),
@ -15,7 +30,7 @@ void quick_xplain::add_constraint_to_qsol(unsigned j) {
auto ci = m_qsol.add_constraint(ls, lar_c.m_kind, lar_c.m_right_side);
m_local_ci_to_constraint_offsets[ci] = j;
}
void quick_xplain::copy_constraint_and_add_constraint_vars(const lar_constraint& lar_c) {
vector < std::pair<mpq, unsigned>> ls;
for (auto & p : lar_c.get_left_side_coefficients()) {
@ -56,9 +71,9 @@ void quick_xplain::minimize(const vector<unsigned>& u) {
}
}
if (m > 0) {
lean_assert(m_qsol.constraint_stack_size() >= initial_stack_size);
SASSERT(m_qsol.constraint_stack_size() >= initial_stack_size);
m_qsol.pop(m_qsol.constraint_stack_size() - initial_stack_size);
for (auto j : m_x)
for (auto j : m_x)
add_constraint_to_qsol(j);
if (!infeasible()) {
vector<unsigned> un;
@ -69,11 +84,11 @@ void quick_xplain::minimize(const vector<unsigned>& u) {
}
}
void quick_xplain::run(vector<std::pair<mpq, constraint_index>> & explanation, const lar_solver & ls){
if (explanation.size() <= 2) return;
lar_solver qsol;
lean_assert(ls.explanation_is_correct(explanation));
SASSERT(ls.explanation_is_correct(explanation));
quick_xplain q(explanation, ls, qsol);
q.solve();
}
@ -109,7 +124,7 @@ bool quick_xplain::x_is_minimal() const {
x.push_back(j);
for (unsigned k = 0; k < x.size(); k++) {
lean_assert(is_feasible(x, x[k]));
SASSERT(is_feasible(x, x[k]));
}
return true;
}
@ -117,8 +132,8 @@ bool quick_xplain::x_is_minimal() const {
void quick_xplain::solve() {
copy_constraints_to_local_constraints();
m_qsol.push();
lean_assert(m_qsol.constraint_count() == 0)
vector<unsigned> u;
SASSERT(m_qsol.constraint_count() == 0);
vector<unsigned> u;
for (unsigned k = 0; k < m_constraints_in_local_vars.size(); k++)
u.push_back(k);
minimize(u);
@ -127,10 +142,10 @@ void quick_xplain::solve() {
for (unsigned i : m_x)
add_constraint_to_qsol(i);
m_qsol.solve();
lean_assert(m_qsol.get_status() == INFEASIBLE);
SASSERT(m_qsol.get_status() == INFEASIBLE);
m_qsol.get_infeasibility_explanation(m_explanation);
lean_assert(m_qsol.explanation_is_correct(m_explanation));
lean_assert(x_is_minimal());
SASSERT(m_qsol.explanation_is_correct(m_explanation));
SASSERT(x_is_minimal());
for (auto & p : m_explanation) {
p.second = this->m_local_constraint_offset_to_external_ci[m_local_ci_to_constraint_offsets[p.second]];
}

2
src/util/lp/quick_xplain.h
View file

@ -7,7 +7,7 @@ Author: Lev Nachmanson
#include "util/vector.h"
#include <unordered_set>
namespace lean {
namespace lp {
class lar_solver; // forward definition
class quick_xplain {

2
src/util/lp/random_updater.h
View file

@ -12,7 +12,7 @@ Author: Lev Nachmanson
#include "util/lp/linear_combination_iterator.h"
// see http://research.microsoft.com/projects/z3/smt07.pdf
// The class searches for a feasible solution with as many different values of variables as it can find
namespace lean {
namespace lp {
template <typename T> struct numeric_pair; // forward definition
class lar_core_solver; // forward definition
class random_updater {

61
src/util/lp/random_updater.hpp
View file

@ -1,12 +1,27 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/random_updater.h"
#include "util/lp/static_matrix.h"
#include "util/lp/lar_solver.h"
#include "util/vector.h"
namespace lean {
namespace lp {
@ -36,7 +51,7 @@ random_updater::interval random_updater::get_interval_of_non_basic_var(unsigned
ret.set_upper_bound(m_core_solver.m_r_upper_bounds[j]);
break;
default:
lean_assert(false);
SASSERT(false);
}
return ret;
}
@ -44,15 +59,15 @@ random_updater::interval random_updater::get_interval_of_non_basic_var(unsigned
void random_updater::diminish_interval_for_basic_var(numeric_pair<mpq>& nb_x, unsigned j,
mpq & a,
interval & r) {
lean_assert(m_core_solver.m_r_heading[j] >= 0);
SASSERT(m_core_solver.m_r_heading[j] >= 0);
numeric_pair<mpq> delta;
lean_assert(a != zero_of_type<mpq>());
SASSERT(a != zero_of_type<mpq>());
switch (m_core_solver.get_column_type(j)) {
case column_type::free_column:
break;
case column_type::low_bound:
delta = m_core_solver.m_r_x[j] - m_core_solver.m_r_low_bounds[j];
lean_assert(delta >= zero_of_type<numeric_pair<mpq>>());
SASSERT(delta >= zero_of_type<numeric_pair<mpq>>());
if (a > 0) {
r.set_upper_bound(nb_x + delta / a);
} else {
@ -61,7 +76,7 @@ void random_updater::diminish_interval_for_basic_var(numeric_pair<mpq>& nb_x, un
break;
case column_type::upper_bound:
delta = m_core_solver.m_r_upper_bounds()[j] - m_core_solver.m_r_x[j];
lean_assert(delta >= zero_of_type<numeric_pair<mpq>>());
SASSERT(delta >= zero_of_type<numeric_pair<mpq>>());
if (a > 0) {
r.set_low_bound(nb_x - delta / a);
} else {
@ -71,17 +86,17 @@ void random_updater::diminish_interval_for_basic_var(numeric_pair<mpq>& nb_x, un
case column_type::boxed:
if (a > 0) {
delta = m_core_solver.m_r_x[j] - m_core_solver.m_r_low_bounds[j];
lean_assert(delta >= zero_of_type<numeric_pair<mpq>>());
SASSERT(delta >= zero_of_type<numeric_pair<mpq>>());
r.set_upper_bound(nb_x + delta / a);
delta = m_core_solver.m_r_upper_bounds()[j] - m_core_solver.m_r_x[j];
lean_assert(delta >= zero_of_type<numeric_pair<mpq>>());
SASSERT(delta >= zero_of_type<numeric_pair<mpq>>());
r.set_low_bound(nb_x - delta / a);
} else { // a < 0
delta = m_core_solver.m_r_upper_bounds()[j] - m_core_solver.m_r_x[j];
lean_assert(delta >= zero_of_type<numeric_pair<mpq>>());
SASSERT(delta >= zero_of_type<numeric_pair<mpq>>());
r.set_upper_bound(nb_x - delta / a);
delta = m_core_solver.m_r_x[j] - m_core_solver.m_r_low_bounds[j];
lean_assert(delta >= zero_of_type<numeric_pair<mpq>>());
SASSERT(delta >= zero_of_type<numeric_pair<mpq>>());
r.set_low_bound(nb_x + delta / a);
}
break;
@ -90,7 +105,7 @@ void random_updater::diminish_interval_for_basic_var(numeric_pair<mpq>& nb_x, un
r.set_upper_bound(nb_x);
break;
default:
lean_assert(false);
SASSERT(false);
}
}
@ -113,15 +128,15 @@ random_updater::interval random_updater::find_shift_interval(unsigned j) {
}
void random_updater::shift_var(unsigned j, interval & r) {
lean_assert(r.contains(m_core_solver.m_r_x[j]));
lean_assert(m_core_solver.m_r_solver.column_is_feasible(j));
SASSERT(r.contains(m_core_solver.m_r_x[j]));
SASSERT(m_core_solver.m_r_solver.column_is_feasible(j));
auto old_x = m_core_solver.m_r_x[j];
remove_value(old_x);
auto new_val = m_core_solver.m_r_x[j] = get_random_from_interval(r);
add_value(new_val);
lean_assert(r.contains(m_core_solver.m_r_x[j]));
lean_assert(m_core_solver.m_r_solver.column_is_feasible(j));
SASSERT(r.contains(m_core_solver.m_r_x[j]));
SASSERT(m_core_solver.m_r_solver.column_is_feasible(j));
auto delta = m_core_solver.m_r_x[j] - old_x;
unsigned i;
@ -130,9 +145,9 @@ void random_updater::shift_var(unsigned j, interval & r) {
while(m_column_j->next(a, i)) {
unsigned bj = m_core_solver.m_r_basis[i];
m_core_solver.m_r_x[bj] -= a * delta;
lean_assert(m_core_solver.m_r_solver.column_is_feasible(bj));
SASSERT(m_core_solver.m_r_solver.column_is_feasible(bj));
}
lean_assert(m_core_solver.m_r_solver.A_mult_x_is_off() == false);
SASSERT(m_core_solver.m_r_solver.A_mult_x_is_off() == false);
}
numeric_pair<mpq> random_updater::get_random_from_interval(interval & r) {
@ -143,7 +158,7 @@ numeric_pair<mpq> random_updater::get_random_from_interval(interval & r) {
return r.low_bound + numeric_pair<mpq>(rand % range, 0);
if ((!r.low_bound_is_set) && r.upper_bound_is_set)
return r.upper_bound - numeric_pair<mpq>(rand % range, 0);
lean_assert(r.low_bound_is_set && r.upper_bound_is_set);
SASSERT(r.low_bound_is_set && r.upper_bound_is_set);
return r.low_bound + (rand % range) * (r.upper_bound - r.low_bound)/ range;
}
@ -183,7 +198,7 @@ void random_updater::add_value(numeric_pair<mpq>& v) {
void random_updater::remove_value(numeric_pair<mpq>& v) {
std::unordered_map<numeric_pair<mpq>, unsigned>::iterator it = m_values.find(v);
lean_assert(it != m_values.end());
SASSERT(it != m_values.end());
it->second--;
if (it->second == 0)
m_values.erase((std::unordered_map<numeric_pair<mpq>, unsigned>::const_iterator)it);

23
src/util/lp/random_updater_instances.cpp
View file

@ -1,5 +1,20 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/random_updater.hpp"

35
src/util/lp/row_eta_matrix.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
@ -10,26 +25,26 @@
#include "util/lp/sparse_vector.h"
#include "util/lp/indexed_vector.h"
#include "util/lp/permutation_matrix.h"
namespace lean {
namespace lp {
// This is the sum of a unit matrix and a lower triangular matrix
// with non-zero elements only in one row
template <typename T, typename X>
class row_eta_matrix
: public tail_matrix<T, X> {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
unsigned m_dimension;
#endif
unsigned m_row_start;
unsigned m_row;
sparse_vector<T> m_row_vector;
public:
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
row_eta_matrix(unsigned row_start, unsigned row, unsigned dim):
#else
row_eta_matrix(unsigned row_start, unsigned row):
#endif
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
m_dimension(dim),
#endif
m_row_start(row_start), m_row(row) {
@ -55,7 +70,7 @@ public:
}
void push_back(unsigned row_index, T val ) {
lean_assert(row_index != m_row);
SASSERT(row_index != m_row);
m_row_vector.push_back(row_index, val);
}
@ -63,7 +78,7 @@ public:
void apply_from_right(indexed_vector<T> & w);
void conjugate_by_permutation(permutation_matrix<T, X> & p);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
T get_elem(unsigned row, unsigned col) const;
unsigned row_count() const { return m_dimension; }
unsigned column_count() const { return m_dimension; }

57
src/util/lp/row_eta_matrix.hpp
View file

@ -1,13 +1,28 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/vector.h"
#include "util/lp/row_eta_matrix.h"
namespace lean {
namespace lp {
template <typename T, typename X>
void row_eta_matrix<T, X>::apply_from_left(vector<X> & w, lp_settings &) {
// #ifdef LEAN_DEBUG
// #ifdef Z3DEBUG
// dense_matrix<T> deb(*this);
// auto clone_w = clone_vector<T>(w, m_dimension);
// deb.apply_from_left(clone_w, settings);
@ -18,8 +33,8 @@ void row_eta_matrix<T, X>::apply_from_left(vector<X> & w, lp_settings &) {
w_at_row += w[it.first] * it.second;
}
// w[m_row] = w_at_row;
// #ifdef LEAN_DEBUG
// lean_assert(vectors_are_equal<T>(clone_w, w, m_dimension));
// #ifdef Z3DEBUG
// SASSERT(vectors_are_equal<T>(clone_w, w, m_dimension));
// delete [] clone_w;
// #endif
}
@ -43,7 +58,7 @@ void row_eta_matrix<T, X>::apply_from_left_local_to_T(indexed_vector<T> & w, lp_
auto it = std::find(w.m_index.begin(), w.m_index.end(), m_row);
w.m_index.erase(it);
}
// TBD: lean_assert(check_vector_for_small_values(w, settings));
// TBD: SASSERT(check_vector_for_small_values(w, settings));
}
template <typename T, typename X>
@ -65,14 +80,14 @@ void row_eta_matrix<T, X>::apply_from_left_local_to_X(indexed_vector<X> & w, lp_
auto it = std::find(w.m_index.begin(), w.m_index.end(), m_row);
w.m_index.erase(it);
}
// TBD: does not compile lean_assert(check_vector_for_small_values(w, settings));
// TBD: does not compile SASSERT(check_vector_for_small_values(w, settings));
}
template <typename T, typename X>
void row_eta_matrix<T, X>::apply_from_right(vector<T> & w) {
const T & w_row = w[m_row];
if (numeric_traits<T>::is_zero(w_row)) return;
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// dense_matrix<T> deb(*this);
// auto clone_w = clone_vector<T>(w, m_dimension);
// deb.apply_from_right(clone_w);
@ -80,18 +95,18 @@ void row_eta_matrix<T, X>::apply_from_right(vector<T> & w) {
for (auto & it : m_row_vector.m_data) {
w[it.first] += w_row * it.second;
}
#ifdef LEAN_DEBUG
// lean_assert(vectors_are_equal<T>(clone_w, w, m_dimension));
#ifdef Z3DEBUG
// SASSERT(vectors_are_equal<T>(clone_w, w, m_dimension));
// delete clone_w;
#endif
}
template <typename T, typename X>
void row_eta_matrix<T, X>::apply_from_right(indexed_vector<T> & w) {
lean_assert(w.is_OK());
SASSERT(w.is_OK());
const T & w_row = w[m_row];
if (numeric_traits<T>::is_zero(w_row)) return;
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// vector<T> wcopy(w.m_data);
// apply_from_right(wcopy);
#endif
@ -129,8 +144,8 @@ void row_eta_matrix<T, X>::apply_from_right(indexed_vector<T> & w) {
}
}
}
#ifdef LEAN_DEBUG
// lean_assert(vectors_are_equal(wcopy, w.m_data));
#ifdef Z3DEBUG
// SASSERT(vectors_are_equal(wcopy, w.m_data));
#endif
}
@ -138,7 +153,7 @@ void row_eta_matrix<T, X>::apply_from_right(indexed_vector<T> & w) {
template <typename T, typename X>
void row_eta_matrix<T, X>::conjugate_by_permutation(permutation_matrix<T, X> & p) {
// this = p * this * p(-1)
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// auto rev = p.get_reverse();
// auto deb = ((*this) * rev);
// deb = p * deb;
@ -150,11 +165,11 @@ void row_eta_matrix<T, X>::conjugate_by_permutation(permutation_matrix<T, X> & p
columns.push_back(it.first);
for (unsigned i = static_cast<unsigned>(columns.size()); i-- > 0;)
m_row_vector.m_data[i].first = p.get_rev(columns[i]);
#ifdef LEAN_DEBUG
// lean_assert(deb == *this);
#ifdef Z3DEBUG
// SASSERT(deb == *this);
#endif
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X>
T row_eta_matrix<T, X>::get_elem(unsigned row, unsigned col) const {
if (row == m_row){

27
src/util/lp/row_eta_matrix_instances.cpp
View file

@ -1,16 +1,31 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <memory>
#include "util/vector.h"
#include "util/lp/row_eta_matrix.hpp"
#include "util/lp/lu.h"
namespace lean {
namespace lp {
template void row_eta_matrix<double, double>::conjugate_by_permutation(permutation_matrix<double, double>&);
template void row_eta_matrix<mpq, numeric_pair<mpq> >::conjugate_by_permutation(permutation_matrix<mpq, numeric_pair<mpq> >&);
template void row_eta_matrix<mpq, mpq>::conjugate_by_permutation(permutation_matrix<mpq, mpq>&);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template mpq row_eta_matrix<mpq, mpq>::get_elem(unsigned int, unsigned int) const;
template mpq row_eta_matrix<mpq, numeric_pair<mpq> >::get_elem(unsigned int, unsigned int) const;
template double row_eta_matrix<double, double>::get_elem(unsigned int, unsigned int) const;

27
src/util/lp/scaler.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
@ -11,7 +26,7 @@
#include <stdlib.h> /* exit, EXIT_FAILURE */
#include "util/lp/lp_utils.h"
#include "util/lp/static_matrix.h"
namespace lean {
namespace lp {
// for scaling an LP
template <typename T, typename X>
class scaler {
@ -31,7 +46,7 @@ public:
m_scaling_maximum(scaling_maximum),
m_column_scale(column_scale),
m_settings(settings) {
lean_assert(m_column_scale.size() == 0);
SASSERT(m_column_scale.size() == 0);
m_column_scale.resize(m_A.column_count(), numeric_traits<T>::one());
}

31
src/util/lp/scaler.hpp
View file

@ -1,11 +1,26 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <algorithm>
#include "util/lp/scaler.h"
#include "util/lp/numeric_pair.h"
namespace lean {
namespace lp {
// for scaling an LP
template <typename T, typename X> T scaler<T, X>::right_side_balance() {
T ret = zero_of_type<T>();
@ -41,7 +56,7 @@ template <typename T, typename X> T scaler<T, X>::A_max() const {
template <typename T, typename X> T scaler<T, X>::get_A_ratio() const {
T min = A_min();
T max = A_max();
lean_assert(!m_settings.abs_val_is_smaller_than_zero_tolerance(min));
SASSERT(!m_settings.abs_val_is_smaller_than_zero_tolerance(min));
T ratio = max / min;
return ratio;
}
@ -51,7 +66,7 @@ template <typename T, typename X> T scaler<T, X>::get_max_ratio_on_rows() con
unsigned i = m_A.row_count();
while (i--) {
T den = m_A.get_min_abs_in_row(i);
lean_assert(!m_settings.abs_val_is_smaller_than_zero_tolerance(den));
SASSERT(!m_settings.abs_val_is_smaller_than_zero_tolerance(den));
T t = m_A.get_max_abs_in_row(i)/ den;
if (t > ret)
ret = t;
@ -78,7 +93,7 @@ template <typename T, typename X> void scaler<T, X>::scale_rows_with_geometri
while (i--) {
T max = m_A.get_max_abs_in_row(i);
T min = m_A.get_min_abs_in_row(i);
lean_assert(max > zero_of_type<T>() && min > zero_of_type<T>());
SASSERT(max > zero_of_type<T>() && min > zero_of_type<T>());
if (is_zero(max) || is_zero(min))
continue;
T gm = T(sqrt(numeric_traits<T>::get_double(max*min)));

27
src/util/lp/scaler_instances.cpp
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/lp/scaler.hpp"
template bool lean::scaler<double, double>::scale();
template bool lean::scaler<lean::mpq, lean::mpq>::scale();
template bool lp::scaler<double, double>::scale();
template bool lp::scaler<lp::mpq, lp::mpq>::scale();

25
src/util/lp/signature_bound_evidence.h
View file

@ -1,11 +1,26 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/lp/lp_settings.h"
#include "util/lp/lar_constraints.h"
namespace lean {
namespace lp {
struct bound_signature {
unsigned m_i;
bool m_at_low;

51
src/util/lp/sparse_matrix.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
@ -21,11 +36,11 @@
#include "util/lp/binary_heap_upair_queue.h"
#include "util/lp/numeric_pair.h"
#include "util/lp/int_set.h"
namespace lean {
namespace lp {
// it is a square matrix
template <typename T, typename X>
class sparse_matrix
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
: public matrix<T, X>
#endif
{
@ -57,7 +72,7 @@ public:
vector<bool> m_processed;
unsigned get_n_of_active_elems() const { return m_n_of_active_elems; }
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// dense_matrix<T> m_dense;
#endif
/*
@ -146,7 +161,7 @@ public:
unsigned dimension() const {return static_cast<unsigned>(m_row_permutation.size());}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
unsigned row_count() const {return dimension();}
unsigned column_count() const {return dimension();}
#endif
@ -206,19 +221,19 @@ public:
void multiply_from_right(permutation_matrix<T, X>& p) {
// m_dense = m_dense * p;
m_column_permutation.multiply_by_permutation_from_right(p);
// lean_assert(*this == m_dense);
// SASSERT(*this == m_dense);
}
void multiply_from_left(permutation_matrix<T, X>& p) {
// m_dense = p * m_dense;
m_row_permutation.multiply_by_permutation_from_left(p);
// lean_assert(*this == m_dense);
// SASSERT(*this == m_dense);
}
void multiply_from_left_with_reverse(permutation_matrix<T, X>& p) {
// m_dense = p * m_dense;
m_row_permutation.multiply_by_permutation_reverse_from_left(p);
// lean_assert(*this == m_dense);
// SASSERT(*this == m_dense);
}
// adding delta columns at the end of the matrix
@ -231,13 +246,13 @@ public:
// dense_matrix<T, X> d(*this);
m_column_permutation.transpose_from_left(a, b);
// d.swap_columns(a, b);
// lean_assert(*this == d);
// SASSERT(*this == d);
}
void swap_rows(unsigned a, unsigned b) {
m_row_permutation.transpose_from_right(a, b);
// m_dense.swap_rows(a, b);
// lean_assert(*this == m_dense);
// SASSERT(*this == m_dense);
}
void divide_row_by_constant(unsigned i, const T & t, lp_settings & settings);
@ -286,7 +301,7 @@ public:
template <typename L>
void solve_U_y_indexed_only(indexed_vector<L> & y, const lp_settings&, vector<unsigned> & sorted_active_rows );
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
T get_elem(unsigned i, unsigned j) const { return get(i, j); }
unsigned get_number_of_rows() const { return dimension(); }
unsigned get_number_of_columns() const { return dimension(); }
@ -341,7 +356,7 @@ public:
bool shorten_active_matrix(unsigned row, eta_matrix<T, X> *eta_matrix);
unsigned pivot_score_without_shortened_counters(unsigned i, unsigned j, unsigned k);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
bool can_improve_score_for_row(unsigned row, unsigned score, T const & c_partial_pivoting, unsigned k);
bool really_best_pivot(unsigned i, unsigned j, T const & c_partial_pivoting, unsigned k);
void print_active_matrix(unsigned k, std::ostream & out);
@ -373,7 +388,7 @@ public:
}
bool fill_eta_matrix(unsigned j, eta_matrix<T, X> ** eta);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
bool is_upper_triangular_and_maximums_are_set_correctly_in_rows(lp_settings & settings) const;
bool is_upper_triangular_until(unsigned k) const;
@ -393,7 +408,7 @@ public:
void process_index_recursively_for_y_U(unsigned j, vector<unsigned> & sorted_rows);
void resize(unsigned new_dim) {
unsigned old_dim = dimension();
lean_assert(new_dim >= old_dim);
SASSERT(new_dim >= old_dim);
for (unsigned j = old_dim; j < new_dim; j++) {
m_rows.push_back(vector<indexed_value<T>>());
m_columns.push_back(col_header());
@ -407,7 +422,7 @@ public:
add_new_element(j, j, numeric_traits<T>::one());
}
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
vector<T> get_full_row(unsigned i) const;
#endif
unsigned pivot_queue_size() const { return m_pivot_queue.size(); }

135
src/util/lp/sparse_matrix.hpp
View file

@ -1,13 +1,28 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/vector.h"
#include "util/lp/sparse_matrix.h"
#include <set>
#include <queue>
namespace lean {
namespace lp {
template <typename T, typename X>
void sparse_matrix<T, X>::copy_column_from_static_matrix(unsigned col, static_matrix<T, X> const &A, unsigned col_index_in_the_new_matrix) {
vector<column_cell> const & A_col_vector = A.m_columns[col];
@ -82,12 +97,12 @@ void sparse_matrix<T, X>::set_with_no_adjusting(unsigned row, unsigned col, T va
template <typename T, typename X>
void sparse_matrix<T, X>::set(unsigned row, unsigned col, T val) { // should not be used in efficient code
lean_assert(row < dimension() && col < dimension());
SASSERT(row < dimension() && col < dimension());
// m_dense.set_elem(row, col, val);
row = adjust_row(row);
col = adjust_column(col);
set_with_no_adjusting(row, col, val);
// lean_assert(*this == m_dense);
// SASSERT(*this == m_dense);
}
template <typename T, typename X>
@ -243,7 +258,7 @@ void sparse_matrix<T, X>::scan_row_to_work_vector_and_remove_pivot_column(unsign
}
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X>
vector<T> sparse_matrix<T, X>::get_full_row(unsigned i) const {
vector<T> r;
@ -261,8 +276,8 @@ vector<T> sparse_matrix<T, X>::get_full_row(unsigned i) const {
// Returns false if the resulting row is all zeroes, and true otherwise
template <typename T, typename X>
bool sparse_matrix<T, X>::pivot_row_to_row(unsigned i, const T& alpha, unsigned i0, lp_settings & settings ) {
lean_assert(i < dimension() && i0 < dimension());
lean_assert(i != i0);
SASSERT(i < dimension() && i0 < dimension());
SASSERT(i != i0);
unsigned pivot_col = adjust_column(i);
i = adjust_row(i);
i0 = adjust_row(i0);
@ -327,7 +342,7 @@ bool sparse_matrix<T, X>::set_row_from_work_vector_and_clean_work_vector_not_adj
if (numeric_traits<T>::is_zero(work_vec[j])) {
continue;
}
lean_assert(!settings.abs_val_is_smaller_than_drop_tolerance(work_vec[j]));
SASSERT(!settings.abs_val_is_smaller_than_drop_tolerance(work_vec[j]));
add_new_element(i0, adjust_column(j), work_vec[j]);
work_vec[j] = numeric_traits<T>::zero();
}
@ -372,7 +387,7 @@ void sparse_matrix<T, X>::remove_zero_elements_and_set_data_on_existing_elements
T val = work_vec[rj];
if (settings.abs_val_is_smaller_than_drop_tolerance(val)) {
remove_element(row_vals, row_el_iv);
lean_assert(numeric_traits<T>::is_zero(val));
SASSERT(numeric_traits<T>::is_zero(val));
} else {
m_columns[j].m_values[row_el_iv.m_other].set_value(row_el_iv.m_value = val);
work_vec[rj] = numeric_traits<T>::zero();
@ -393,7 +408,7 @@ void sparse_matrix<T, X>::add_columns_at_the_end(unsigned delta) {
template <typename T, typename X>
void sparse_matrix<T, X>::delete_column(int i) {
lean_assert(i < dimension());
SASSERT(i < dimension());
for (auto cell = m_columns[i].m_head; cell != nullptr;) {
auto next_cell = cell->m_down;
kill_cell(cell);
@ -403,7 +418,7 @@ void sparse_matrix<T, X>::delete_column(int i) {
template <typename T, typename X>
void sparse_matrix<T, X>::divide_row_by_constant(unsigned i, const T & t, lp_settings & settings) {
lean_assert(!settings.abs_val_is_smaller_than_zero_tolerance(t));
SASSERT(!settings.abs_val_is_smaller_than_zero_tolerance(t));
i = adjust_row(i);
for (auto & iv : m_rows[i]) {
T &v = iv.m_value;
@ -420,7 +435,7 @@ void sparse_matrix<T, X>::divide_row_by_constant(unsigned i, const T & t, lp_set
// the matrix here has to be upper triangular
template <typename T, typename X>
void sparse_matrix<T, X>::solve_y_U(vector<T> & y) const { // works by rows
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// T * rs = clone_vector<T>(y, dimension());
#endif
unsigned end = dimension();
@ -436,11 +451,11 @@ void sparse_matrix<T, X>::solve_y_U(vector<T> & y) const { // works by rows
}
}
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// dense_matrix<T> deb(*this);
// T * clone_y = clone_vector<T>(y, dimension());
// deb.apply_from_right(clone_y);
// lean_assert(vectors_are_equal(rs, clone_y, dimension()));
// SASSERT(vectors_are_equal(rs, clone_y, dimension()));
// delete [] clone_y;
// delete [] rs;
#endif
@ -450,7 +465,7 @@ void sparse_matrix<T, X>::solve_y_U(vector<T> & y) const { // works by rows
// the matrix here has to be upper triangular
template <typename T, typename X>
void sparse_matrix<T, X>::solve_y_U_indexed(indexed_vector<T> & y, const lp_settings & settings) {
#if 0 && LEAN_DEBUG
#if 0 && Z3DEBUG
vector<T> ycopy(y.m_data);
if (numeric_traits<T>::precise() == false)
solve_y_U(ycopy);
@ -474,10 +489,10 @@ void sparse_matrix<T, X>::solve_y_U_indexed(indexed_vector<T> & y, const lp_sett
y.m_data[j] = zero_of_type<T>();
}
lean_assert(y.is_OK());
#if 0 && LEAN_DEBUG
SASSERT(y.is_OK());
#if 0 && Z3DEBUG
if (numeric_traits<T>::precise() == false)
lean_assert(vectors_are_equal(ycopy, y.m_data));
SASSERT(vectors_are_equal(ycopy, y.m_data));
#endif
}
@ -537,8 +552,8 @@ void sparse_matrix<T, X>::add_delta_to_solution(const vector<L>& del, vector<L>
template <typename T, typename X>
template <typename L>
void sparse_matrix<T, X>::add_delta_to_solution(const indexed_vector<L>& del, indexed_vector<L> & y) {
// lean_assert(del.is_OK());
// lean_assert(y.is_OK());
// SASSERT(del.is_OK());
// SASSERT(y.is_OK());
for (auto i : del.m_index) {
y.add_value_at_index(i, del[i]);
}
@ -546,11 +561,11 @@ void sparse_matrix<T, X>::add_delta_to_solution(const indexed_vector<L>& del, in
template <typename T, typename X>
template <typename L>
void sparse_matrix<T, X>::double_solve_U_y(indexed_vector<L>& y, const lp_settings & settings){
lean_assert(y.is_OK());
SASSERT(y.is_OK());
indexed_vector<L> y_orig(y); // copy y aside
vector<unsigned> active_rows;
solve_U_y_indexed_only(y, settings, active_rows);
lean_assert(y.is_OK());
SASSERT(y.is_OK());
find_error_in_solution_U_y_indexed(y_orig, y, active_rows);
// y_orig contains the error now
if (y_orig.m_index.size() * ratio_of_index_size_to_all_size<T>() < 32 * dimension()) {
@ -563,7 +578,7 @@ void sparse_matrix<T, X>::double_solve_U_y(indexed_vector<L>& y, const lp_settin
add_delta_to_solution(y_orig.m_data, y.m_data);
y.restore_index_and_clean_from_data();
}
lean_assert(y.is_OK());
SASSERT(y.is_OK());
}
template <typename T, typename X>
template <typename L>
@ -581,7 +596,7 @@ void sparse_matrix<T, X>::double_solve_U_y(vector<L>& y){
template <typename T, typename X>
template <typename L>
void sparse_matrix<T, X>::solve_U_y(vector<L> & y) { // it is a column wise version
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// T * rs = clone_vector<T>(y, dimension());
#endif
@ -595,16 +610,16 @@ void sparse_matrix<T, X>::solve_U_y(vector<L> & y) { // it is a column wise vers
}
}
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// dense_matrix<T> deb(*this);
// T * clone_y = clone_vector<T>(y, dimension());
// deb.apply_from_left(clone_y);
// lean_assert(vectors_are_equal(rs, clone_y, dimension()));
// SASSERT(vectors_are_equal(rs, clone_y, dimension()));
#endif
}
template <typename T, typename X>
void sparse_matrix<T, X>::process_index_recursively_for_y_U(unsigned j, vector<unsigned> & sorted_active_rows) {
lean_assert(m_processed[j] == false);
SASSERT(m_processed[j] == false);
m_processed[j]=true;
auto & row = m_rows[adjust_row(j)];
for (auto & c : row) {
@ -619,7 +634,7 @@ void sparse_matrix<T, X>::process_index_recursively_for_y_U(unsigned j, vector<u
template <typename T, typename X>
void sparse_matrix<T, X>::process_column_recursively(unsigned j, vector<unsigned> & sorted_active_rows) {
lean_assert(m_processed[j] == false);
SASSERT(m_processed[j] == false);
auto & mc = m_columns[adjust_column(j)].m_values;
for (auto & iv : mc) {
unsigned i = adjust_row_inverse(iv.m_index);
@ -684,12 +699,12 @@ void sparse_matrix<T, X>::solve_U_y_indexed_only(indexed_vector<L> & y, const lp
y[j] = zero_of_type<L>();
}
lean_assert(y.is_OK());
#ifdef LEAN_DEBUG
SASSERT(y.is_OK());
#ifdef Z3DEBUG
// dense_matrix<T,X> deb(this);
// vector<T> clone_y(y.m_data);
// deb.apply_from_left(clone_y);
// lean_assert(vectors_are_equal(rs, clone_y));
// SASSERT(vectors_are_equal(rs, clone_y));
#endif
}
@ -802,7 +817,7 @@ void sparse_matrix<T, X>::add_new_elements_of_w_and_clear_w(unsigned column_to_r
unsigned ai = adjust_row(i);
add_new_element(ai, column_to_replace, w_at_i);
auto & row_chunk = m_rows[ai];
lean_assert(row_chunk.size() > 0);
SASSERT(row_chunk.size() > 0);
if (abs(w_at_i) > abs(row_chunk[0].m_value))
put_max_index_to_0(row_chunk, static_cast<unsigned>(row_chunk.size()) - 1);
}
@ -833,7 +848,7 @@ unsigned sparse_matrix<T, X>::pivot_score(unsigned i, unsigned j) {
template <typename T, typename X>
void sparse_matrix<T, X>::enqueue_domain_into_pivot_queue() {
lean_assert(m_pivot_queue.size() == 0);
SASSERT(m_pivot_queue.size() == 0);
for (unsigned i = 0; i < dimension(); i++) {
auto & rh = m_rows[i];
unsigned rnz = static_cast<unsigned>(rh.size());
@ -919,7 +934,7 @@ void sparse_matrix<T, X>::update_active_pivots(unsigned row) {
for (const auto & iv : m_rows[arow]) {
col_header & ch = m_columns[iv.m_index];
int cols = static_cast<int>(ch.m_values.size()) - ch.m_shortened_markovitz - 1;
lean_assert(cols >= 0);
SASSERT(cols >= 0);
for (const auto &ivc : ch.m_values) {
unsigned i = ivc.m_index;
if (adjust_row_inverse(i) <= row) continue; // the i is not an active row
@ -945,7 +960,7 @@ bool sparse_matrix<T, X>::shorten_active_matrix(unsigned row, eta_matrix<T, X> *
for (auto & iv : row_values) {
const col_header& ch = m_columns[iv.m_index];
int cnz = static_cast<int>(ch.m_values.size()) - ch.m_shortened_markovitz - 1;
lean_assert(cnz >= 0);
SASSERT(cnz >= 0);
m_pivot_queue.enqueue(row, iv.m_index, rnz * cnz);
}
}
@ -961,25 +976,25 @@ unsigned sparse_matrix<T, X>::pivot_score_without_shortened_counters(unsigned i,
if (adjust_row_inverse(iv.m_index) < k)
cnz--;
}
lean_assert(cnz > 0);
SASSERT(cnz > 0);
return m_rows[i].m_values.size() * (cnz - 1);
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X>
bool sparse_matrix<T, X>::can_improve_score_for_row(unsigned row, unsigned score, T const & c_partial_pivoting, unsigned k) {
unsigned arow = adjust_row(row);
auto & row_vals = m_rows[arow].m_values;
auto & begin_iv = row_vals[0];
T row_max = abs(begin_iv.m_value);
lean_assert(adjust_column_inverse(begin_iv.m_index) >= k);
SASSERT(adjust_column_inverse(begin_iv.m_index) >= k);
if (pivot_score_without_shortened_counters(arow, begin_iv.m_index, k) < score) {
print_active_matrix(k);
return true;
}
for (unsigned jj = 1; jj < row_vals.size(); jj++) {
auto & iv = row_vals[jj];
lean_assert(adjust_column_inverse(iv.m_index) >= k);
lean_assert(abs(iv.m_value) <= row_max);
SASSERT(adjust_column_inverse(iv.m_index) >= k);
SASSERT(abs(iv.m_value) <= row_max);
if (c_partial_pivoting * abs(iv.m_value) < row_max) continue;
if (pivot_score_without_shortened_counters(arow, iv.m_index, k) < score) {
print_active_matrix(k);
@ -993,7 +1008,7 @@ template <typename T, typename X>
bool sparse_matrix<T, X>::really_best_pivot(unsigned i, unsigned j, T const & c_partial_pivoting, unsigned k) {
unsigned queue_pivot_score = pivot_score_without_shortened_counters(i, j, k);
for (unsigned ii = k; ii < dimension(); ii++) {
lean_assert(!can_improve_score_for_row(ii, queue_pivot_score, c_partial_pivoting, k));
SASSERT(!can_improve_score_for_row(ii, queue_pivot_score, c_partial_pivoting, k));
}
return true;
}
@ -1026,7 +1041,7 @@ template <typename T, typename X>
bool sparse_matrix<T, X>::pivot_queue_is_correct_for_row(unsigned i, unsigned k) {
unsigned arow = adjust_row(i);
for (auto & iv : m_rows[arow].m_values) {
lean_assert(pivot_score_without_shortened_counters(arow, iv.m_index, k + 1) ==
SASSERT(pivot_score_without_shortened_counters(arow, iv.m_index, k + 1) ==
m_pivot_queue.get_priority(arow, iv.m_index));
}
return true;
@ -1035,8 +1050,8 @@ bool sparse_matrix<T, X>::pivot_queue_is_correct_for_row(unsigned i, unsigned k)
template <typename T, typename X>
bool sparse_matrix<T, X>::pivot_queue_is_correct_after_pivoting(int k) {
for (unsigned i = k + 1; i < dimension(); i++ )
lean_assert(pivot_queue_is_correct_for_row(i, k));
lean_assert(m_pivot_queue.is_correct());
SASSERT(pivot_queue_is_correct_for_row(i, k));
SASSERT(m_pivot_queue.is_correct());
return true;
}
#endif
@ -1052,10 +1067,10 @@ bool sparse_matrix<T, X>::get_pivot_for_column(unsigned &i, unsigned &j, int c_p
if (j_inv < k) continue;
int _small = elem_is_too_small(i, j, c_partial_pivoting);
if (!_small) {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// if (!really_best_pivot(i, j, c_partial_pivoting, k)) {
// print_active_matrix(k);
// lean_assert(false);
// SASSERT(false);
// }
#endif
recover_pivot_queue(pivots_candidates_that_are_too_small);
@ -1088,7 +1103,7 @@ bool sparse_matrix<T, X>::shorten_columns_by_pivot_row(unsigned i, unsigned pivo
for (indexed_value<T> & iv : row_chunk) {
unsigned j = iv.m_index;
if (j == pivot_column) {
lean_assert(!col_is_active(j));
SASSERT(!col_is_active(j));
continue;
}
m_columns[j].shorten_markovich_by_one();
@ -1121,7 +1136,7 @@ bool sparse_matrix<T, X>::fill_eta_matrix(unsigned j, eta_matrix<T, X> ** eta) {
return true;
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
*eta = new eta_matrix<T, X>(j, dimension());
#else
*eta = new eta_matrix<T, X>(j);
@ -1146,16 +1161,16 @@ bool sparse_matrix<T, X>::fill_eta_matrix(unsigned j, eta_matrix<T, X> ** eta) {
(*eta)->divide_by_diagonal_element();
return true;
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X>
bool sparse_matrix<T, X>::is_upper_triangular_and_maximums_are_set_correctly_in_rows(lp_settings & settings) const {
for (unsigned i = 0; i < dimension(); i++) {
vector<indexed_value<T>> const & row_chunk = get_row_values(i);
lean_assert(row_chunk.size());
SASSERT(row_chunk.size());
T const & max = abs(row_chunk[0].m_value);
unsigned ai = adjust_row_inverse(i);
for (auto & iv : row_chunk) {
lean_assert(abs(iv.m_value) <= max);
SASSERT(abs(iv.m_value) <= max);
unsigned aj = adjust_column_inverse(iv.m_index);
if (!(ai <= aj || numeric_traits<T>::is_zero(iv.m_value)))
return false;
@ -1193,18 +1208,18 @@ void sparse_matrix<T, X>::check_column_vs_rows(unsigned col) {
indexed_value<T> & row_iv = column_iv_other(column_iv);
if (row_iv.m_index != col) {
// std::cout << "m_other in row does not belong to column " << col << ", but to column " << row_iv.m_index << std::endl;
lean_assert(false);
SASSERT(false);
}
if (& row_iv_other(row_iv) != &column_iv) {
// std::cout << "row and col do not point to each other" << std::endl;
lean_assert(false);
SASSERT(false);
}
if (row_iv.m_value != column_iv.m_value) {
// std::cout << "the data from col " << col << " for row " << column_iv.m_index << " is different in the column " << std::endl;
// std::cout << "in the col it is " << column_iv.m_value << ", but in the row it is " << row_iv.m_value << std::endl;
lean_assert(false);
SASSERT(false);
}
}
}
@ -1217,18 +1232,18 @@ void sparse_matrix<T, X>::check_row_vs_columns(unsigned row) {
if (column_iv.m_index != row) {
// std::cout << "col_iv does not point to correct row " << row << " but to " << column_iv.m_index << std::endl;
lean_assert(false);
SASSERT(false);
}
if (& row_iv != & column_iv_other(column_iv)) {
// std::cout << "row and col do not point to each other" << std::endl;
lean_assert(false);
SASSERT(false);
}
if (row_iv.m_value != column_iv.m_value) {
// std::cout << "the data from col " << column_iv.m_index << " for row " << row << " is different in the column " << std::endl;
// std::cout << "in the col it is " << column_iv.m_value << ", but in the row it is " << row_iv.m_value << std::endl;
lean_assert(false);
SASSERT(false);
}
}
}

77
src/util/lp/sparse_matrix_instances.cpp
View file

@ -1,14 +1,29 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <memory>
#include "util/vector.h"
#include "util/lp/lp_settings.h"
#include "util/lp/lu.h"
#include "util/lp/sparse_matrix.hpp"
#include "util/lp/dense_matrix.h"
namespace lean {
namespace lp {
template double sparse_matrix<double, double>::dot_product_with_row<double>(unsigned int, vector<double> const&) const;
template void sparse_matrix<double, double>::add_new_element(unsigned int, unsigned int, const double&);
template void sparse_matrix<double, double>::divide_row_by_constant(unsigned int, const double&, lp_settings&);
@ -65,37 +80,37 @@ template void sparse_matrix<double, double>::double_solve_U_y<double>(indexed_ve
template void sparse_matrix<mpq, mpq>::double_solve_U_y<mpq>(indexed_vector<mpq>&, const lp_settings&);
template void sparse_matrix<mpq, numeric_pair<mpq>>::double_solve_U_y<mpq>(indexed_vector<mpq>&, const lp_settings&);
template void sparse_matrix<mpq, numeric_pair<mpq> >::double_solve_U_y<numeric_pair<mpq> >(indexed_vector<numeric_pair<mpq>>&, const lp_settings&);
template void lean::sparse_matrix<double, double>::solve_U_y_indexed_only<double>(lean::indexed_vector<double>&, const lp_settings&, vector<unsigned> &);
template void lean::sparse_matrix<lean::mpq, lean::mpq>::solve_U_y_indexed_only<lean::mpq>(lean::indexed_vector<lean::mpq>&, const lp_settings &, vector<unsigned> &);
#ifdef LEAN_DEBUG
template void lp::sparse_matrix<double, double>::solve_U_y_indexed_only<double>(lp::indexed_vector<double>&, const lp_settings&, vector<unsigned> &);
template void lp::sparse_matrix<lp::mpq, lp::mpq>::solve_U_y_indexed_only<lp::mpq>(lp::indexed_vector<lp::mpq>&, const lp_settings &, vector<unsigned> &);
#ifdef Z3DEBUG
template bool sparse_matrix<double, double>::is_upper_triangular_and_maximums_are_set_correctly_in_rows(lp_settings&) const;
template bool sparse_matrix<mpq, mpq>::is_upper_triangular_and_maximums_are_set_correctly_in_rows(lp_settings&) const;
template bool sparse_matrix<mpq, numeric_pair<mpq> >::is_upper_triangular_and_maximums_are_set_correctly_in_rows(lp_settings&) const;
#endif
}
template void lean::sparse_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_U_y_indexed_only<lean::mpq>(lean::indexed_vector<lean::mpq>&, const lp_settings &, vector<unsigned> &);
template void lean::sparse_matrix<lean::mpq, lean::mpq>::solve_U_y<lean::mpq>(vector<lean::mpq>&);
template void lean::sparse_matrix<lean::mpq, lean::mpq>::double_solve_U_y<lean::mpq>(vector<lean::mpq >&);
template void lean::sparse_matrix<double, double>::solve_U_y<double>(vector<double>&);
template void lean::sparse_matrix<double, double>::double_solve_U_y<double>(vector<double>&);
template void lean::sparse_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_U_y<lean::numeric_pair<lean::mpq> >(vector<lean::numeric_pair<lean::mpq> >&);
template void lean::sparse_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::double_solve_U_y<lean::numeric_pair<lean::mpq> >(vector<lean::numeric_pair<lean::mpq> >&);
template void lean::sparse_matrix<double, double>::find_error_in_solution_U_y_indexed<double>(lean::indexed_vector<double>&, lean::indexed_vector<double>&, const vector<unsigned> &);
template double lean::sparse_matrix<double, double>::dot_product_with_row<double>(unsigned int, lean::indexed_vector<double> const&) const;
template void lean::sparse_matrix<lean::mpq, lean::mpq>::find_error_in_solution_U_y_indexed<lean::mpq>(lean::indexed_vector<lean::mpq>&, lean::indexed_vector<lean::mpq>&, const vector<unsigned> &);
template lean::mpq lean::sparse_matrix<lean::mpq, lean::mpq>::dot_product_with_row<lean::mpq>(unsigned int, lean::indexed_vector<lean::mpq> const&) const;
template void lean::sparse_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::find_error_in_solution_U_y_indexed<lean::mpq>(lean::indexed_vector<lean::mpq>&, lean::indexed_vector<lean::mpq>&, const vector<unsigned> &);
template lean::mpq lean::sparse_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::dot_product_with_row<lean::mpq>(unsigned int, lean::indexed_vector<lean::mpq> const&) const;
template void lean::sparse_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::find_error_in_solution_U_y_indexed<lean::numeric_pair<lean::mpq> >(lean::indexed_vector<lean::numeric_pair<lean::mpq> >&, lean::indexed_vector<lean::numeric_pair<lean::mpq> >&, const vector<unsigned> &);
template lean::numeric_pair<lean::mpq> lean::sparse_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::dot_product_with_row<lean::numeric_pair<lean::mpq> >(unsigned int, lean::indexed_vector<lean::numeric_pair<lean::mpq> > const&) const;
template void lean::sparse_matrix<lean::mpq, lean::mpq>::extend_and_sort_active_rows(vector<unsigned int> const&, vector<unsigned int>&);
template void lp::sparse_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_U_y_indexed_only<lp::mpq>(lp::indexed_vector<lp::mpq>&, const lp_settings &, vector<unsigned> &);
template void lp::sparse_matrix<lp::mpq, lp::mpq>::solve_U_y<lp::mpq>(vector<lp::mpq>&);
template void lp::sparse_matrix<lp::mpq, lp::mpq>::double_solve_U_y<lp::mpq>(vector<lp::mpq >&);
template void lp::sparse_matrix<double, double>::solve_U_y<double>(vector<double>&);
template void lp::sparse_matrix<double, double>::double_solve_U_y<double>(vector<double>&);
template void lp::sparse_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_U_y<lp::numeric_pair<lp::mpq> >(vector<lp::numeric_pair<lp::mpq> >&);
template void lp::sparse_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::double_solve_U_y<lp::numeric_pair<lp::mpq> >(vector<lp::numeric_pair<lp::mpq> >&);
template void lp::sparse_matrix<double, double>::find_error_in_solution_U_y_indexed<double>(lp::indexed_vector<double>&, lp::indexed_vector<double>&, const vector<unsigned> &);
template double lp::sparse_matrix<double, double>::dot_product_with_row<double>(unsigned int, lp::indexed_vector<double> const&) const;
template void lp::sparse_matrix<lp::mpq, lp::mpq>::find_error_in_solution_U_y_indexed<lp::mpq>(lp::indexed_vector<lp::mpq>&, lp::indexed_vector<lp::mpq>&, const vector<unsigned> &);
template lp::mpq lp::sparse_matrix<lp::mpq, lp::mpq>::dot_product_with_row<lp::mpq>(unsigned int, lp::indexed_vector<lp::mpq> const&) const;
template void lp::sparse_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::find_error_in_solution_U_y_indexed<lp::mpq>(lp::indexed_vector<lp::mpq>&, lp::indexed_vector<lp::mpq>&, const vector<unsigned> &);
template lp::mpq lp::sparse_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::dot_product_with_row<lp::mpq>(unsigned int, lp::indexed_vector<lp::mpq> const&) const;
template void lp::sparse_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::find_error_in_solution_U_y_indexed<lp::numeric_pair<lp::mpq> >(lp::indexed_vector<lp::numeric_pair<lp::mpq> >&, lp::indexed_vector<lp::numeric_pair<lp::mpq> >&, const vector<unsigned> &);
template lp::numeric_pair<lp::mpq> lp::sparse_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::dot_product_with_row<lp::numeric_pair<lp::mpq> >(unsigned int, lp::indexed_vector<lp::numeric_pair<lp::mpq> > const&) const;
template void lp::sparse_matrix<lp::mpq, lp::mpq>::extend_and_sort_active_rows(vector<unsigned int> const&, vector<unsigned int>&);
template void lean::sparse_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::extend_and_sort_active_rows(vector<unsigned int> const&, vector<unsigned int>&);
template void lp::sparse_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::extend_and_sort_active_rows(vector<unsigned int> const&, vector<unsigned int>&);
template void lean::sparse_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_U_y<lean::mpq>(vector<lean::mpq >&);
template void lean::sparse_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::double_solve_U_y<lean::mpq>(vector<lean::mpq >&);
template void lean::sparse_matrix< lean::mpq,lean::numeric_pair< lean::mpq> >::set(unsigned int,unsigned int, lean::mpq);
template void lean::sparse_matrix<double, double>::solve_y_U_indexed(lean::indexed_vector<double>&, const lp_settings & );
template void lean::sparse_matrix<lean::mpq, lean::mpq>::solve_y_U_indexed(lean::indexed_vector<lean::mpq>&, const lp_settings &);
template void lean::sparse_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >::solve_y_U_indexed(lean::indexed_vector<lean::mpq>&, const lp_settings &);
template void lp::sparse_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_U_y<lp::mpq>(vector<lp::mpq >&);
template void lp::sparse_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::double_solve_U_y<lp::mpq>(vector<lp::mpq >&);
template void lp::sparse_matrix< lp::mpq,lp::numeric_pair< lp::mpq> >::set(unsigned int,unsigned int, lp::mpq);
template void lp::sparse_matrix<double, double>::solve_y_U_indexed(lp::indexed_vector<double>&, const lp_settings & );
template void lp::sparse_matrix<lp::mpq, lp::mpq>::solve_y_U_indexed(lp::indexed_vector<lp::mpq>&, const lp_settings &);
template void lp::sparse_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >::solve_y_U_indexed(lp::indexed_vector<lp::mpq>&, const lp_settings &);

29
src/util/lp/sparse_vector.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
@ -9,7 +24,7 @@
#include "util/debug.h"
#include "util/lp/lp_utils.h"
#include "util/lp/lp_settings.h"
namespace lean {
namespace lp {
template <typename T>
class sparse_vector {
@ -18,7 +33,7 @@ public:
void push_back(unsigned index, T val) {
m_data.push_back(std::make_pair(index, val));
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
T operator[] (unsigned i) const {
for (auto &t : m_data) {
if (t.first == i) return t.second;
@ -27,7 +42,7 @@ public:
}
#endif
void divide(T const & a) {
lean_assert(!lp_settings::is_eps_small_general(a, 1e-12));
SASSERT(!lp_settings::is_eps_small_general(a, 1e-12));
for (auto & t : m_data) { t.second /= a; }
}

45
src/util/lp/square_dense_submatrix.h
View file

@ -1,7 +1,22 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
#include "util/vector.h"
@ -20,7 +35,7 @@
#include "util/lp/eta_matrix.h"
#include "util/lp/binary_heap_upair_queue.h"
#include "util/lp/sparse_matrix.h"
namespace lean {
namespace lp {
template <typename T, typename X>
class square_dense_submatrix : public tail_matrix<T, X> {
// the submatrix uses the permutations of the parent matrix to access the elements
@ -30,11 +45,11 @@ class square_dense_submatrix : public tail_matrix<T, X> {
ref(unsigned i, square_dense_submatrix & s) :
m_i_offset((i - s.m_index_start) * s.m_dim), m_s(s){}
T & operator[] (unsigned j) {
lean_assert(j >= m_s.m_index_start);
SASSERT(j >= m_s.m_index_start);
return m_s.m_v[m_i_offset + m_s.adjust_column(j) - m_s.m_index_start];
}
const T & operator[] (unsigned j) const {
lean_assert(j >= m_s.m_index_start);
SASSERT(j >= m_s.m_index_start);
return m_s.m_v[m_i_offset + m_s.adjust_column(j) - m_s.m_index_start];
}
};
@ -58,8 +73,8 @@ public:
bool is_dense() const { return true; }
ref operator[] (unsigned i) {
lean_assert(i >= m_index_start);
lean_assert(i < m_parent->dimension());
SASSERT(i >= m_index_start);
SASSERT(i < m_parent->dimension());
return ref(i, *this);
}
@ -148,7 +163,7 @@ public:
}
}
}
lean_assert(wcopy.is_OK());
SASSERT(wcopy.is_OK());
apply_from_right(w.m_data);
w.m_index.clear();
if (numeric_traits<T>::precise()) {
@ -167,11 +182,11 @@ public:
}
}
#else
lean_assert(w.is_OK());
lean_assert(m_work_vector.is_OK());
SASSERT(w.is_OK());
SASSERT(m_work_vector.is_OK());
m_work_vector.resize(w.data_size());
m_work_vector.clear();
lean_assert(m_work_vector.is_OK());
SASSERT(m_work_vector.is_OK());
unsigned end = m_index_start + m_dim;
for (unsigned k : w.m_index) {
// find j such that k = adjust_row_inverse(j)
@ -188,7 +203,7 @@ public:
}
}
m_work_vector.clean_up();
lean_assert(m_work_vector.is_OK());
SASSERT(m_work_vector.is_OK());
w = m_work_vector;
#endif
}
@ -198,7 +213,7 @@ public:
void apply_from_right(vector<T> & w);
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
T get_elem (unsigned i, unsigned j) const;
unsigned row_count() const { return m_parent->row_count();}
unsigned column_count() const { return row_count();}

71
src/util/lp/square_dense_submatrix.hpp
View file

@ -1,10 +1,25 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include "util/vector.h"
#include "util/lp/square_dense_submatrix.h"
namespace lean {
namespace lp {
template <typename T, typename X>
square_dense_submatrix<T, X>::square_dense_submatrix (sparse_matrix<T, X> *parent_matrix, unsigned index_start) :
m_index_start(index_start),
@ -18,7 +33,7 @@ square_dense_submatrix<T, X>::square_dense_submatrix (sparse_matrix<T, X> *paren
unsigned row = parent_matrix->adjust_row(i);
for (auto & iv : parent_matrix->get_row_values(row)) {
unsigned j = parent_matrix->adjust_column_inverse(iv.m_index);
lean_assert(j>= m_index_start);
SASSERT(j>= m_index_start);
m_v[row_offset + j] = iv.m_value;
}
row_offset += m_dim;
@ -43,7 +58,7 @@ template <typename T, typename X> void square_dense_submatrix<T, X>::init(sparse
template <typename T, typename X> int square_dense_submatrix<T, X>::find_pivot_column_in_row(unsigned i) const {
int j = -1;
T max = zero_of_type<T>();
lean_assert(i >= m_index_start);
SASSERT(i >= m_index_start);
unsigned row_start = (i - m_index_start) * m_dim;
for (unsigned k = i; k < m_parent->dimension(); k++) {
unsigned col = adjust_column(k); // this is where the column is in the row
@ -64,14 +79,14 @@ template <typename T, typename X> void square_dense_submatrix<T, X>::pivot(un
}
template <typename T, typename X> void square_dense_submatrix<T, X>::pivot_row_to_row(unsigned i, unsigned row, lp_settings & settings) {
lean_assert(i < row);
SASSERT(i < row);
unsigned pj = adjust_column(i); // the pivot column
unsigned pjd = pj - m_index_start;
unsigned pivot_row_offset = (i-m_index_start)*m_dim;
T pivot = m_v[pivot_row_offset + pjd];
unsigned row_offset= (row-m_index_start)*m_dim;
T m = m_v[row_offset + pjd];
lean_assert(!is_zero(pivot));
SASSERT(!is_zero(pivot));
m_v[row_offset + pjd] = -m * pivot; // creating L matrix
for (unsigned j = m_index_start; j < m_parent->dimension(); j++) {
if (j == pj) {
@ -94,7 +109,7 @@ template <typename T, typename X> void square_dense_submatrix<T, X>::divide_r
unsigned pj = adjust_column(i); // the pivot column
unsigned irow_offset = (i - m_index_start) * m_dim;
T pivot = m_v[irow_offset + pj - m_index_start];
lean_assert(!is_zero(pivot));
SASSERT(!is_zero(pivot));
for (unsigned k = m_index_start; k < m_parent->dimension(); k++) {
if (k == pj){
m_v[irow_offset++] = one_of_type<T>() / pivot; // creating the L matrix diagonal
@ -158,7 +173,7 @@ template <typename T, typename X> void square_dense_submatrix<T, X>::push_new
template <typename T, typename X>
template <typename L>
L square_dense_submatrix<T, X>::row_by_vector_product(unsigned i, const vector<L> & v) {
lean_assert(i >= m_index_start);
SASSERT(i >= m_index_start);
unsigned row_in_subm = i - m_index_start;
unsigned row_offset = row_in_subm * m_dim;
@ -171,7 +186,7 @@ L square_dense_submatrix<T, X>::row_by_vector_product(unsigned i, const vector<L
template <typename T, typename X>
template <typename L>
L square_dense_submatrix<T, X>::column_by_vector_product(unsigned j, const vector<L> & v) {
lean_assert(j >= m_index_start);
SASSERT(j >= m_index_start);
unsigned offset = j - m_index_start;
L r = zero_of_type<L>();
@ -182,7 +197,7 @@ L square_dense_submatrix<T, X>::column_by_vector_product(unsigned j, const vecto
template <typename T, typename X>
template <typename L>
L square_dense_submatrix<T, X>::row_by_indexed_vector_product(unsigned i, const indexed_vector<L> & v) {
lean_assert(i >= m_index_start);
SASSERT(i >= m_index_start);
unsigned row_in_subm = i - m_index_start;
unsigned row_offset = row_in_subm * m_dim;
@ -194,7 +209,7 @@ L square_dense_submatrix<T, X>::row_by_indexed_vector_product(unsigned i, const
template <typename T, typename X>
template <typename L>
void square_dense_submatrix<T, X>::apply_from_left_local(indexed_vector<L> & w, lp_settings & settings) {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// dense_matrix<T, X> deb(*this);
// vector<L> deb_w(w.m_data.size());
// for (unsigned i = 0; i < w.m_data.size(); i++)
@ -246,11 +261,11 @@ void square_dense_submatrix<T, X>::apply_from_left_local(indexed_vector<L> & w,
w.m_data[i] = v;
}
#endif
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// cout << "w final" << endl;
// print_vector(w.m_data);
// lean_assert(vectors_are_equal<T>(deb_w, w.m_data));
// lean_assert(w.is_OK());
// SASSERT(vectors_are_equal<T>(deb_w, w.m_data));
// SASSERT(w.is_OK());
#endif
}
@ -277,19 +292,19 @@ void square_dense_submatrix<T, X>::apply_from_left_to_vector(vector<L> & w) {
for (unsigned i = 0; i < m_parent->dimension(); i++) {
w[i] = t[i];
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// cout << "w final" << endl;
// print_vector(w.m_data);
// lean_assert(vectors_are_equal<L>(deb_w, w));
// SASSERT(vectors_are_equal<L>(deb_w, w));
#endif
}
template <typename T, typename X> bool square_dense_submatrix<T, X>::is_L_matrix() const {
#ifdef LEAN_DEBUG
lean_assert(m_row_permutation.is_identity());
#ifdef Z3DEBUG
SASSERT(m_row_permutation.is_identity());
for (unsigned i = 0; i < m_parent->dimension(); i++) {
if (i < m_index_start) {
lean_assert(m_column_permutation[i] == i);
SASSERT(m_column_permutation[i] == i);
continue;
}
unsigned row_offs = (i-m_index_start)*m_dim;
@ -297,9 +312,9 @@ template <typename T, typename X> bool square_dense_submatrix<T, X>::is_L_mat
unsigned j = m_index_start + k;
unsigned jex = adjust_column_inverse(j);
if (jex > i) {
lean_assert(is_zero(m_v[row_offs + k]));
SASSERT(is_zero(m_v[row_offs + k]));
} else if (jex == i) {
lean_assert(!is_zero(m_v[row_offs + k]));
SASSERT(!is_zero(m_v[row_offs + k]));
}
}
}
@ -308,7 +323,7 @@ template <typename T, typename X> bool square_dense_submatrix<T, X>::is_L_mat
}
template <typename T, typename X> void square_dense_submatrix<T, X>::apply_from_right(vector<T> & w) {
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
// dense_matrix<T, X> deb(*this);
// vector<T> deb_w(w);
// deb.apply_from_right(deb_w);
@ -326,15 +341,15 @@ template <typename T, typename X> void square_dense_submatrix<T, X>::apply_from_
t[adjust_column_inverse(j)] = column_by_vector_product(j, w);
}
w = t;
#ifdef LEAN_DEBUG
// lean_assert(vector_are_equal<T>(deb_w, w));
#ifdef Z3DEBUG
// SASSERT(vector_are_equal<T>(deb_w, w));
#endif
}
#ifdef LEAN_DEBUG
#ifdef Z3DEBUG
template <typename T, typename X> T square_dense_submatrix<T, X>::get_elem (unsigned i, unsigned j) const {
i = adjust_row(i);

71
src/util/lp/square_dense_submatrix_instances.cpp
View file

@ -1,33 +1,48 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#include <memory>
#include "util/vector.h"
#include "util/lp/square_dense_submatrix.hpp"
template void lean::square_dense_submatrix<double, double>::init(lean::sparse_matrix<double, double>*, unsigned int);
template lean::square_dense_submatrix<double, double>::square_dense_submatrix(lean::sparse_matrix<double, double>*, unsigned int);
template void lean::square_dense_submatrix<double, double>::update_parent_matrix(lean::lp_settings&);
template bool lean::square_dense_submatrix<double, double>::is_L_matrix() const;
template void lean::square_dense_submatrix<double, double>::conjugate_by_permutation(lean::permutation_matrix<double, double>&);
template int lean::square_dense_submatrix<double, double>::find_pivot_column_in_row(unsigned int) const;
template void lean::square_dense_submatrix<double, double>::pivot(unsigned int, lean::lp_settings&);
template lean::square_dense_submatrix<lean::mpq, lean::numeric_pair<lean::mpq> >::square_dense_submatrix(lean::sparse_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >*, unsigned int);
template void lean::square_dense_submatrix<lean::mpq, lean::numeric_pair<lean::mpq> >::update_parent_matrix(lean::lp_settings&);
template bool lean::square_dense_submatrix<lean::mpq, lean::numeric_pair<lean::mpq> >::is_L_matrix() const;
template void lean::square_dense_submatrix<lean::mpq, lean::numeric_pair<lean::mpq> >::conjugate_by_permutation(lean::permutation_matrix<lean::mpq, lean::numeric_pair<lean::mpq> >&);
template int lean::square_dense_submatrix<lean::mpq, lean::numeric_pair<lean::mpq> >::find_pivot_column_in_row(unsigned int) const;
template void lean::square_dense_submatrix<lean::mpq, lean::numeric_pair<lean::mpq> >::pivot(unsigned int, lean::lp_settings&);
#ifdef LEAN_DEBUG
template double lean::square_dense_submatrix<double, double>::get_elem(unsigned int, unsigned int) const;
template void lp::square_dense_submatrix<double, double>::init(lp::sparse_matrix<double, double>*, unsigned int);
template lp::square_dense_submatrix<double, double>::square_dense_submatrix(lp::sparse_matrix<double, double>*, unsigned int);
template void lp::square_dense_submatrix<double, double>::update_parent_matrix(lp::lp_settings&);
template bool lp::square_dense_submatrix<double, double>::is_L_matrix() const;
template void lp::square_dense_submatrix<double, double>::conjugate_by_permutation(lp::permutation_matrix<double, double>&);
template int lp::square_dense_submatrix<double, double>::find_pivot_column_in_row(unsigned int) const;
template void lp::square_dense_submatrix<double, double>::pivot(unsigned int, lp::lp_settings&);
template lp::square_dense_submatrix<lp::mpq, lp::numeric_pair<lp::mpq> >::square_dense_submatrix(lp::sparse_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >*, unsigned int);
template void lp::square_dense_submatrix<lp::mpq, lp::numeric_pair<lp::mpq> >::update_parent_matrix(lp::lp_settings&);
template bool lp::square_dense_submatrix<lp::mpq, lp::numeric_pair<lp::mpq> >::is_L_matrix() const;
template void lp::square_dense_submatrix<lp::mpq, lp::numeric_pair<lp::mpq> >::conjugate_by_permutation(lp::permutation_matrix<lp::mpq, lp::numeric_pair<lp::mpq> >&);
template int lp::square_dense_submatrix<lp::mpq, lp::numeric_pair<lp::mpq> >::find_pivot_column_in_row(unsigned int) const;
template void lp::square_dense_submatrix<lp::mpq, lp::numeric_pair<lp::mpq> >::pivot(unsigned int, lp::lp_settings&);
#ifdef Z3DEBUG
template double lp::square_dense_submatrix<double, double>::get_elem(unsigned int, unsigned int) const;
#endif
template void lean::square_dense_submatrix<double, double>::apply_from_right(vector<double>&);
template void lp::square_dense_submatrix<double, double>::apply_from_right(vector<double>&);
template void lean::square_dense_submatrix<double, double>::apply_from_left_local<double>(lean::indexed_vector<double>&, lean::lp_settings&);
template void lean::square_dense_submatrix<double, double>::apply_from_left_to_vector<double>(vector<double>&);
template lean::square_dense_submatrix<lean::mpq, lean::mpq>::square_dense_submatrix(lean::sparse_matrix<lean::mpq, lean::mpq>*, unsigned int);
template void lean::square_dense_submatrix<lean::mpq, lean::mpq>::update_parent_matrix(lean::lp_settings&);
template bool lean::square_dense_submatrix<lean::mpq, lean::mpq>::is_L_matrix() const;
template void lean::square_dense_submatrix<lean::mpq, lean::mpq>::conjugate_by_permutation(lean::permutation_matrix<lean::mpq, lean::mpq>&);
template int lean::square_dense_submatrix<lean::mpq, lean::mpq>::find_pivot_column_in_row(unsigned int) const;
template void lean::square_dense_submatrix<lean::mpq, lean::mpq>::pivot(unsigned int, lean::lp_settings&);
template void lp::square_dense_submatrix<double, double>::apply_from_left_local<double>(lp::indexed_vector<double>&, lp::lp_settings&);
template void lp::square_dense_submatrix<double, double>::apply_from_left_to_vector<double>(vector<double>&);
template lp::square_dense_submatrix<lp::mpq, lp::mpq>::square_dense_submatrix(lp::sparse_matrix<lp::mpq, lp::mpq>*, unsigned int);
template void lp::square_dense_submatrix<lp::mpq, lp::mpq>::update_parent_matrix(lp::lp_settings&);
template bool lp::square_dense_submatrix<lp::mpq, lp::mpq>::is_L_matrix() const;
template void lp::square_dense_submatrix<lp::mpq, lp::mpq>::conjugate_by_permutation(lp::permutation_matrix<lp::mpq, lp::mpq>&);
template int lp::square_dense_submatrix<lp::mpq, lp::mpq>::find_pivot_column_in_row(unsigned int) const;
template void lp::square_dense_submatrix<lp::mpq, lp::mpq>::pivot(unsigned int, lp::lp_settings&);

37
src/util/lp/stacked_map.h
View file

@ -1,14 +1,29 @@
/*
Copyright (c) 2017 Microsoft Corporation
Author: Lev Nachmanson
*/
/*++
Copyright (c) 2017 Microsoft Corporation
Module Name:
<name>
Abstract:
<abstract>
Author:
Lev Nachmanson (levnach)
Revision History:
--*/
#pragma once
// this class implements a map with some stack functionality
#include <unordered_map>
#include <set>
#include <stack>
namespace lean {
namespace lp {
template <typename A, typename B,
@ -33,10 +48,10 @@ public:
m_map.emplace_replace(m_key, b);
return *this;
}
ref & operator=(const ref & b) { lean_assert(false); return *this; }
ref & operator=(const ref & b) { SASSERT(false); return *this; }
operator const B&() const {
auto it = m_map.m_map.find(m_key);
lean_assert(it != m_map.m_map.end());
SASSERT(it != m_map.m_map.end());
return it->second;
}
};
@ -73,7 +88,7 @@ public:
const B & operator[]( const A & a) const {
auto it = m_map.find(a);
if (it == m_map.end()) {
lean_assert(false);
SASSERT(false);
}
return it->second;
@ -128,7 +143,7 @@ public:
for (auto & t: d.m_original_changed) {
m_map[t.first] = t.second;
}
// lean_assert(d.m_deb_copy == m_map);
// SASSERT(d.m_deb_copy == m_map);
m_stack.pop();
}
}
@ -142,7 +157,7 @@ public:
delta & d = m_stack.top();
auto it = m_map.find(key);
if (it == m_map.end()) {
lean_assert(d.m_new.find(key) == d.m_new.end());
SASSERT(d.m_new.find(key) == d.m_new.end());
return;
}
auto &orig_changed = d.m_original_changed;
@ -151,7 +166,7 @@ public:
if (orig_changed.find(key) == orig_changed.end())
orig_changed.emplace(it->first, it->second); // need to restore
} else { // k is new
lean_assert(orig_changed.find(key) == orig_changed.end());
SASSERT(orig_changed.find(key) == orig_changed.end());
d.m_new.erase(nit);
}

Some files were not shown because too many files have changed in this diff Show more