mirrors/z3
3
0
Fork 0
mirror of https://github.com/Z3Prover/z3 synced 2025-04-08 02:15:19 +00:00
Code Activity

add option to "rotate" cores during core finding

Browse source 
enable to find multiple cores in a round and at the same time facilitate rotation around satisfiable subsets to explore neighborhoods for improved assignments.
This commit is contained in:
Nikolaj Bjorner 2022-05-01 20:58:11 +02:00
parent 5a9b0dd747
commit b5c7f000de
6 changed files with 510 additions and 25 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

1
src/opt/CMakeLists.txt
View file

@ -5,6 +5,7 @@ z3_add_component(opt
maxsmt.cpp
opt_cmds.cpp
opt_context.cpp
opt_cores.cpp
opt_lns.cpp
opt_pareto.cpp
opt_parse.cpp

53
src/opt/maxcore.cpp
View file

@ -11,8 +11,8 @@ Abstract:
- mu: max-sat algorithm by Nina and Bacchus, AAAI 2014.
- mus-mss: based on dual refinement of bounds.
- binary
- binary-delay
- binary: binary versino of maxres
- rc2: implementaion of rc2 heuristic using cardinality constraints
MaxRes is a core-guided approach to maxsat.
@ -66,6 +66,7 @@ Notes:
#include "opt/opt_context.h"
#include "opt/opt_params.hpp"
#include "opt/opt_lns.h"
#include "opt/opt_cores.h"
#include "opt/maxsmt.h"
#include "opt/maxcore.h"
@ -119,7 +120,6 @@ private:
bool m_hill_climb; // prefer large weight soft clauses for cores
unsigned m_last_index; // last index used during hill-climbing
bool m_add_upper_bound_block; // restrict upper bound with constraint
unsigned m_max_num_cores; // max number of cores per round.
unsigned m_max_core_size; // max core size per round.
bool m_maximize_assignment; // maximize assignment to find MCS
unsigned m_max_correction_set_size;// maximal set of correction set that is tolerated.
@ -127,9 +127,9 @@ private:
// this option is disabled if SAT core is used.
bool m_pivot_on_cs; // prefer smaller correction set to core.
bool m_dump_benchmarks; // display benchmarks (into wcnf format)
bool m_enable_lns { false }; // enable LNS improvements
unsigned m_lns_conflicts { 1000 }; // number of conflicts used for LNS improvement
bool m_enable_lns = false; // enable LNS improvements
unsigned m_lns_conflicts = 1000; // number of conflicts used for LNS improvement
bool m_enable_core_rotate = false;
std::string m_trace_id;
typedef ptr_vector<expr> exprs;
@ -148,7 +148,6 @@ public:
m_correction_set_size(0),
m_found_feasible_optimum(false),
m_hill_climb(true),
m_last_index(0),
m_add_upper_bound_block(false),
m_max_num_cores(UINT_MAX),
m_max_core_size(3),
@ -323,17 +322,15 @@ public:
Give preference to cores that have large minimal values.
*/
sort_assumptions(asms);
m_last_index = 0;
unsigned last_index = 0;
unsigned index = 0;
bool first = index > 0;
SASSERT(index < asms.size() || asms.empty());
IF_VERBOSE(10, verbose_stream() << "start hill climb " << index << " asms: " << asms.size() << "\n";);
while (index < asms.size() && is_sat == l_true) {
while (!first && asms.size() > 20*(index - m_last_index) && index < asms.size()) {
while (asms.size() > 20*(index - last_index) && index < asms.size()) {
index = next_index(asms, index);
}
first = false;
m_last_index = index;
last_index = index;
is_sat = check_sat(index, asms.data());
}
}
@ -390,13 +387,6 @@ public:
st.update("maxsat-correction-sets", m_stats.m_num_cs);
}
struct weighted_core {
exprs m_core;
rational m_weight;
weighted_core(exprs const& c, rational const& w):
m_core(c), m_weight(w) {}
};
lbool get_cores(vector<weighted_core>& cores) {
// assume m_s is unsat.
lbool is_sat = l_false;
@ -434,8 +424,8 @@ public:
remove_soft(core, m_asms);
split_core(core);
if (core.size() >= m_max_core_size) break;
if (cores.size() >= m_max_num_cores) break;
if (core.size() >= m_max_core_size)
break;
is_sat = check_sat_hill_climb(m_asms);
}
@ -511,6 +501,9 @@ public:
}
lbool process_unsat() {
if (m_enable_core_rotate)
return core_rotate();
vector<weighted_core> cores;
lbool is_sat = get_cores(cores);
if (is_sat != l_true) {
@ -525,6 +518,21 @@ public:
}
}
lbool core_rotate() {
cores find_cores(s(), m_lnsctx);
find_cores.updt_params(m_params);
vector<weighted_core> const& cores = find_cores();
for (auto const & [core, w] : cores) {
if (core.empty())
return l_false;
remove_soft(core, m_asms);
split_core(core);
process_unsat(core, w);
}
return l_true;
}
unsigned max_core_size(vector<exprs> const& cores) {
unsigned result = 0;
for (auto const& c : cores) {
@ -980,7 +988,6 @@ public:
opt_params p(_p);
m_hill_climb = p.maxres_hill_climb();
m_add_upper_bound_block = p.maxres_add_upper_bound_block();
m_max_num_cores = p.maxres_max_num_cores();
m_max_core_size = p.maxres_max_core_size();
m_maximize_assignment = p.maxres_maximize_assignment();
m_max_correction_set_size = p.maxres_max_correction_set_size();
@ -988,6 +995,7 @@ public:
m_wmax = p.maxres_wmax();
m_dump_benchmarks = p.dump_benchmarks();
m_enable_lns = p.enable_lns();
m_enable_core_rotate = p.enable_core_rotate();
m_lns_conflicts = p.lns_conflicts();
if (m_c.num_objectives() > 1)
m_add_upper_bound_block = false;
@ -1000,7 +1008,6 @@ public:
add_soft(e, w);
m_max_upper = m_upper;
m_found_feasible_optimum = false;
m_last_index = 0;
add_upper_bound_block();
m_csmodel = nullptr;
m_correction_set_size = 0;

7
src/opt/maxsmt.h
View file

@ -31,6 +31,13 @@ namespace opt {
typedef vector<rational> const weights_t;
struct weighted_core {
ptr_vector<expr> m_core;
rational m_weight;
weighted_core(ptr_vector<expr> const& c, rational const& w):
m_core(c), m_weight(w) {}
};
class maxsat_context;
class maxsmt_solver {

398
src/opt/opt_cores.cpp Normal file
View file

@ -0,0 +1,398 @@
/*++
Copyright (c) 2022 Microsoft Corporation
Module Name:
opt_cores.cpp
Abstract:
"walk" subsets of soft constraints to extract multiple cores and satisfying assignments.
core rotation starts with an initial unsat core, which is a subset of soft constraints.
Then it removes one element from the core from the soft constraints and finds remaining cores.
At every stage it operates over a set of detected cores, and a subset of soft constraints have
a hitting set from the cores removed.
When enough constraints are removed, the remaining soft constraints become satisfiable.
It then attempts extend the satisfying assignment by adding soft constraints removed in
the hitting set. In this process it detects new cores and may find assignments that improve
the current feasible bound. As a final effort, it takes a maximal satisfying assignment and
rotates out elements that belong to cores to explore a neighborhood for satisfying assignments
that may potentially satisfy other soft constraints and potentially more of them.
Author:
Nikolaj Bjorner (nbjorner) 2022-04-27
--*/
#include "solver/solver.h"
#include "opt/maxsmt.h"
#include "opt/opt_cores.h"
#include "opt/opt_params.hpp"
namespace opt {
cores::cores(solver& s, lns_context& ctx):
m(s.get_manager()), s(s), ctx(ctx) {}
void cores::hitting_set(obj_hashtable<expr>& set) {
for (auto const& [core, w] : m_cores) {
bool seen = false;
for (auto * c : core)
seen |= set.contains(c);
if (seen)
continue;
set.insert(core[m_rand(core.size())]);
}
}
bool cores::improve() {
model_ref mdl;
s.get_model(mdl);
rational cost = ctx.cost(*mdl);
IF_VERBOSE(3, verbose_stream() << "(opt.maxcore new model cost " << cost << ")\n");
if (m_best_cost < 0 || cost < m_best_cost) {
m_best_cost = cost;
ctx.update_model(mdl);
return true;
}
return false;
}
/**
* retrieve cores that are disjoint modulo weights.
* weighted soft constraints are treated as multi-sets.
*/
vector<weighted_core> const& cores::disjoint_cores() {
std::sort(m_cores.begin(), m_cores.end(), [&](weighted_core const& c1, weighted_core const& c2) { return c1.m_core.size() < c2.m_core.size(); });
vector<weighted_core> result;
for (auto const& [core, w] : m_cores) {
rational weight = core_weight(core);
if (weight == 0 && !core.empty())
continue;
for (auto *c : core)
m_weight[c] -= weight;
result.push_back(weighted_core(core, weight));
}
IF_VERBOSE(3, verbose_stream() << "(opt.cores :cores-found " << m_cores.size() << " :disjoint-cores " << result.size() << ")\n");
m_cores.reset();
m_cores.append(result);
return m_cores;
}
void cores::rotate_rec(obj_hashtable<expr> const& _mss, obj_map<expr, ptr_vector<expr>>& backbone2core, unsigned depth) {
obj_map<expr, unsigned> counts;
obj_hashtable<expr> mss(_mss);
for (auto* f : mss)
counts.insert(f, 0);
for (auto const& [k, core] : backbone2core)
for (auto* c : core)
counts[c] += 1;
unsigned plateaus = 0;
for (auto const& [c, count] : counts)
if (count <= 1)
++plateaus;
IF_VERBOSE(3, verbose_stream() << "(opt.maxcore :num-plateaus " << plateaus << "\n");
for (auto const& [c, count] : counts) {
if (count <= 1)
continue;
mss.remove(c);
bool rotated = rotate(mss, c, depth + 1);
mss.insert(c);
if (rotated)
break;
}
}
/**
* collect soft constraints that are not in the satisfying assignment mss into the set ps.
* Try to add elements from ps in some order.
* If an element can be added, (mss + p is sat), then mss is extended.
* If mss + p is unsat, then extract core that includes p and a subset of mss
* Then Not(p) is a backbone, and we maintain a map from Not(p) to the subset of mss used
* in the core. Backbone literals are used in the satisfiability check.
* For backbone literals that are used in other cores, resolve away Not(p) by the subset
* of mss that comprised the core for p + mss.
* The set qs accumulates don't knows. If a satisfiable assignment is found that satisfies
* elements from qs, they are added to mss.
*/
bool cores::rotate(obj_hashtable<expr> const& _mss, expr* excl, unsigned depth) {
obj_hashtable<expr> ps, qs, mss(_mss);
expr_ref_vector backbones(m);
obj_map<expr, ptr_vector<expr>> backbone2core;
bool improved = false;
for (expr* f : ctx.soft())
if (!mss.contains(f) && f != excl)
ps.insert(f);
while (!ps.empty() && m.inc() && m_cores.size() < m_max_num_cores) {
expr* p = *ps.begin();
ps.remove(p);
expr_ref_vector asms(backbones);
asms.push_back(p);
for (expr* f : mss)
asms.push_back(f);
lbool is_sat = s.check_sat(asms);
switch (is_sat) {
case l_true: {
model_ref mdl;
s.get_model(mdl);
ptr_vector<expr> moved;
moved.push_back(p);
for (auto* q : qs)
if (mdl->is_true(q))
moved.push_back(q);
for (auto* q : ps)
if (mdl->is_true(q))
moved.push_back(q);
for (auto* q : moved) {
mss.insert(q);
qs.remove(q);
ps.remove(q);
}
if (improve())
improved = true;
break;
}
case l_false: {
obj_hashtable<expr> core;
for (auto* f : unsat_core()) {
ptr_vector<expr> core1;
if (backbone2core.find(f, core1))
for (expr* c : core1)
core.insert(c);
else
core.insert(f);
}
expr_ref_vector core1(m);
for (expr* c : core)
core1.push_back(c);
saturate_core(core1);
add_core(core1);
expr_ref not_p(m.mk_not(p), m);
backbones.push_back(not_p);
ptr_vector<expr> core2(core1.size(), core1.data());
core2.erase(p);
backbone2core.insert(not_p, core2);
break;
}
default:
qs.insert(p);
break;
}
}
if (improved)
rotate_rec(mss, backbone2core, depth);
return improved;
}
struct cores::scoped_update {
cores& c;
char const* par;
bool is_uint = true;
unsigned old_uval;
bool old_bval;
public:
scoped_update(cores& c, char const* par, unsigned old_val, unsigned new_val):
c(c), par(par), old_uval(old_val) {
params_ref p;
p.set_uint(par, new_val);
c.s.updt_params(p);
}
scoped_update(cores& c, char const* par, bool old_val, bool new_val):
c(c), par(par), old_bval(old_val) {
is_uint = false;
params_ref p;
p.set_bool(par, new_val);
c.s.updt_params(p);
}
~scoped_update() {
params_ref p;
if (is_uint)
p.set_uint(par, old_uval);
else
p.set_bool(par, old_bval);
c.s.updt_params(p);
}
};
void cores::saturate_core(expr_ref_vector& core) {
scoped_update _upd(*this, "max_conflicts", m_max_conflicts, m_max_saturate_conflicts);
shuffle(core.size(), core.data(), m_rand);
while (l_false == s.check_sat(core) && unsat_core().size() < core.size()) {
core.reset();
core.append(unsat_core());
shuffle(core.size(), core.data(), m_rand);
}
}
void cores::local_mss() {
obj_hashtable<expr> mss;
model_ref mdl;
s.get_model(mdl);
for (expr* f : ctx.soft())
if (mdl->is_true(f))
mss.insert(f);
rotate(mss, nullptr, 0);
}
expr_ref_vector cores::unsat_core() {
expr_ref_vector core(m);
s.get_unsat_core(core);
return core;
}
/**
* The solver state is unsatisfiable when this function is called.
* Erase one element from each code that is found
*/
void cores::rotate_cores() {
expr_ref_vector soft(m);
soft.append(ctx.soft());
unsigned num_sat = 0, num_unsat = 0, num_undef = 0;
lbool is_sat = l_false;
while (m.inc() && m_cores.size() < m_max_num_cores) {
switch (is_sat) {
case l_false: {
++num_unsat;
auto core = unsat_core();
add_core(core);
if (core.empty())
return;
soft.erase(core.get(m_rand(core.size())));
num_sat = 0;
break;
}
case l_true: {
++num_sat;
improve();
local_mss();
if (num_sat > 1)
return;
soft.reset();
obj_hashtable<expr> hs;
hitting_set(hs);
for (auto s : ctx.soft())
if (!hs.contains(s))
soft.push_back(s);
break;
}
case l_undef:
++num_undef;
if (num_undef > 2)
return;
}
is_sat = s.check_sat(soft);
}
}
rational cores::core_weight(unsigned sz, expr* const* core) {
if (sz == 0)
return rational(0);
rational min_weight = m_weight[core[0]];
for (unsigned i = 1; i < sz; ++i) {
auto* c = core[i];
if (m_weight[c] < min_weight)
min_weight = m_weight[c];
}
return min_weight;
}
vector<weighted_core> const& cores::weighted_disjoint_cores() {
lbool is_sat = l_false;
expr_ref_vector soft = ctx.soft();
while (is_sat == l_false && m.inc()) {
auto core = unsat_core();
saturate_core(core);
rational weight = core_weight(core);
add_core(core);
if (core.empty()) {
IF_VERBOSE(100, verbose_stream() << "(opt.maxres :empty-core)\n";);
TRACE("opt", tout << "empty core\n";);
break;
}
for (auto *c : core) {
m_weight[c] -= weight;
if (m_weight[c] == 0)
soft.erase(c);
}
if (core.size() >= m_max_core_size)
break;
if (m_cores.size() >= m_max_num_cores)
break;
if (m_hill_climb)
is_sat = check_sat_hill_climb(soft);
else
is_sat = s.check_sat(soft);
}
return m_cores;
}
/**
* Give preference to cores that have large minimal values.
* Explore largest values, and grow the set of explored values by at least 5%
* of all soft constraints in iterations (capping maximal iterations at 20).
*/
lbool cores::check_sat_hill_climb(expr_ref_vector const& _soft) {
expr_ref_vector soft(_soft);
lbool is_sat = l_true;
std::sort(soft.data(), soft.data() + soft.size(), [&](expr* a, expr* b) { return m_weight[a] > m_weight[b]; });
unsigned index = 0, last_index = 0;
SASSERT(index < soft.size() || soft.empty());
IF_VERBOSE(10, verbose_stream() << "start hill climb " << index << " soft: " << soft.size() << "\n";);
while (index < soft.size() && is_sat == l_true) {
while (soft.size() > 20*(index - last_index) && index < soft.size()) {
rational w = m_weight[_soft[index]];
for (++index; index < soft.size() && w == m_weight[_soft[index]]; ++index);
}
last_index = index;
is_sat = s.check_sat(index, soft.data());
}
return is_sat;
}
void cores::add_core(expr_ref_vector const& core) {
IF_VERBOSE(3, verbose_stream() << "(opt.maxcore :core-size " << core.size() << ")\n");
m_cores.push_back(weighted_core(ptr_vector<expr>(core.size(), core.data()), core_weight(core)));
}
void cores::updt_params(params_ref& _p) {
std::cout << _p << "\n";
opt_params p(_p);
m_hill_climb = p.maxres_hill_climb();
m_max_num_cores = p.maxres_max_num_cores();
m_max_core_size = p.maxres_max_core_size();
m_enable_core_rotate = p.enable_core_rotate();
}
vector<weighted_core> const& cores::operator()() {
scoped_update _upd1(*this, "max_conflicts", UINT_MAX, m_max_conflicts);
m_cores.reset();
m_weight.reset();
for (expr* s : ctx.soft())
m_weight.insert(s, ctx.weight(s));
if (m_enable_core_rotate) {
scoped_update _upd2(*this, "minimize_core", false, false);
rotate_cores();
return disjoint_cores();
}
else {
return weighted_disjoint_cores();
}
}
};

71
src/opt/opt_cores.h Normal file
View file

@ -0,0 +1,71 @@
/*++
Copyright (c) 2022 Microsoft Corporation
Module Name:
opt_cores.h
Abstract:
"walk" subsets of soft constraints to extract multiple cores and satisfying assignments.
Author:
Nikolaj Bjorner (nbjorner) 2022-04-27
--*/
#pragma once
#include "opt/opt_lns.h"
namespace opt {
class cores {
ast_manager& m;
solver& s;
lns_context& ctx;
random_gen m_rand;
rational m_best_cost = rational::minus_one();
vector<weighted_core> m_cores;
obj_map<expr, rational> m_weight;
unsigned m_max_saturate_conflicts = 500;
unsigned m_max_conflicts = 1000;
bool m_hill_climb = true;
unsigned m_max_num_cores = UINT_MAX;
unsigned m_max_core_size = 4;
bool m_enable_core_rotate = false;
struct scoped_update;
bool improve();
void rotate_rec(obj_hashtable<expr> const& mss, obj_map<expr, ptr_vector<expr>>& backbone2core, unsigned depth);
bool rotate(obj_hashtable<expr> const& mss, expr* excl, unsigned depth);
void saturate_core(expr_ref_vector& core);
void local_mss();
void hitting_set(obj_hashtable<expr>& hs);
rational core_weight(expr_ref_vector const& core) { return core_weight(core.size(), core.data()); }
rational core_weight(ptr_vector<expr> const& core) { return core_weight(core.size(), core.data()); }
rational core_weight(unsigned sz, expr* const* core);
lbool check_sat_hill_climb(expr_ref_vector const& _soft);
void add_core(expr_ref_vector const& core);
vector<weighted_core> const& disjoint_cores();
void rotate_cores();
vector<weighted_core> const& weighted_disjoint_cores();
expr_ref_vector unsat_core();
public:
cores(solver& s, lns_context& ctx);
vector<weighted_core> const& operator()();
void updt_params(params_ref& p);
};
};

5
src/opt/opt_params.pyg
View file

@ -10,8 +10,9 @@ def_module_params('opt',
('timeout', UINT, UINT_MAX, 'timeout (in milliseconds) (UINT_MAX and 0 mean no timeout)'),
('rlimit', UINT, 0, 'resource limit (0 means no limit)'),
('enable_sls', BOOL, False, 'enable SLS tuning during weighted maxsat'),
('enable_lns', BOOL, False, 'enable LNS during weighted maxsat'),
('enable_lns', BOOL, False, 'enable LNS during weighted maxsat'),
('lns_conflicts', UINT, 1000, 'initial conflict count for LNS search'),
('enable_core_rotate', BOOL, False, 'enable core rotation to both sample cores and correction sets'),
('enable_sat', BOOL, True, 'enable the new SAT core for propositional constraints'),
('elim_01', BOOL, True, 'eliminate 01 variables'),
('pp.neat', BOOL, True, 'use neat (as opposed to less readable, but faster) pretty printer when displaying context'),
@ -20,7 +21,7 @@ def_module_params('opt',
('maxlex.enable', BOOL, True, 'enable maxlex heuristic for lexicographic MaxSAT problems'),
('maxres.hill_climb', BOOL, True, 'give preference for large weight cores'),
('maxres.add_upper_bound_block', BOOL, False, 'restict upper bound with constraint'),
('maxres.max_num_cores', UINT, UINT_MAX, 'maximal number of cores per round'),
('maxres.max_num_cores', UINT, 200, 'maximal number of cores per round'),
('maxres.max_core_size', UINT, 3, 'break batch of generated cores if size reaches this number'),
('maxres.maximize_assignment', BOOL, False, 'find an MSS/MCS to improve current assignment'),
('maxres.max_correction_set_size', UINT, 3, 'allow generating correction set constraints up to maximal size'),