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Merge branch 'opt' of https://github.com/nikolajbjorner/z3 into opt

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Nikolaj Bjorner 2017-02-25 16:19:59 -08:00
commit 54920783dc
3 changed files with 279 additions and 151 deletions
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269
src/sat/sat_local_search.cpp
View file

@ -25,15 +25,21 @@
namespace sat {
void local_search::init() {
best_solution.resize(num_vars() + 1, false);
constraint_slack.resize(num_constraints(), 0);
cur_solution.resize(num_vars(), false);
cur_solution.resize(num_vars() + 1, false);
m_index_in_unsat_stack.resize(num_constraints(), 0);
coefficient_in_ob_constraint.resize(num_vars(), 0);
coefficient_in_ob_constraint.resize(num_vars() + 1, 0);
var_neighbor.reset();
for (bool_var v = 0; v < num_vars(); ++v) {
bool_vector is_neighbor(num_vars(), false);
// for dummy var 0
var_neighbor.push_back(bool_var_vector());
for (bool_var v = 1; v <= num_vars(); ++v) {
bool_vector is_neighbor(num_vars() + 1, false);
var_neighbor.push_back(bool_var_vector());
for (unsigned i = 0; i < var_term[v].size(); ++ i) {
for (unsigned i = 0; i < var_term[v].size(); ++i) {
unsigned c = var_term[v][i].constraint_id;
for (unsigned j = 0; j < constraint_term[c].size(); ++j) {
bool_var w = constraint_term[c][j].var_id;
@ -42,17 +48,20 @@ namespace sat {
var_neighbor.back().push_back(w);
}
}
}
}
for (unsigned i = 0; i < ob_constraint.size(); ++i)
coefficient_in_ob_constraint[ob_constraint[i].var_id] = ob_constraint[i].coefficient;
set_parameters();
}
void local_search::reinit() {
reinit_greedy();
reinit_orig();
}
void local_search::init_cur_solution() {
cur_solution.resize(num_vars() + 1, false);
for (unsigned v = 0; v < num_vars(); ++v) {
//cur_solution.resize(num_vars() + 1, false);
for (unsigned v = 1; v <= num_vars(); ++v) {
cur_solution[v] = (rand() % 2 == 1);
}
}
@ -72,9 +81,9 @@ namespace sat {
}
}
// figure out variables scores, pscores and sscores
// figure out variables scores and sscores
void local_search::init_scores() {
for (unsigned v = 0; v < num_vars(); ++v) {
for (unsigned v = 1; v <= num_vars(); ++v) {
for (unsigned i = 0; i < var_term[v].size(); ++i) {
int c = var_term[v][i].constraint_id;
if (cur_solution[v] != var_term[v][i].sense) {
@ -83,7 +92,7 @@ namespace sat {
if (constraint_slack[c] <= 0) {
--sscore[v];
if (constraint_slack[c] == 0)
--score[v];
--m_var_info[v].m_score;
}
}
else { // if (cur_solution[v] == var_term[v][i].sense)
@ -92,20 +101,19 @@ namespace sat {
if (constraint_slack[c] <= -1) {
++sscore[v];
if (constraint_slack[c] == -1)
++score[v];
++m_var_info[v].m_score;
}
}
}
}
}
// init goodvars and okvars stack
// init goodvars
void local_search::init_goodvars() {
goodvar_stack.reset();
already_in_goodvar_stack.resize(num_vars(), false);
for (unsigned v = 0; v < num_vars(); ++v) {
if (score[v] > 0) { // && conf_change[v] == true
already_in_goodvar_stack[v] = true;
for (unsigned v = 1; v <= num_vars(); ++v) {
if (score(v) > 0) { // && conf_change[v] == true
m_var_info[v].m_in_goodvar_stack = true;
goodvar_stack.push_back(v);
}
}
@ -123,60 +131,39 @@ namespace sat {
// init varibale information
// variable 0 is the virtual variable
score.reset();
m_var_info.reset();
sscore.reset();
time_stamp.reset();
conf_change.reset();
cscc.reset();
score.resize(num_vars(), 0); score[0] = INT_MIN;
sscore.resize(num_vars(), 0); sscore[0] = INT_MIN;
time_stamp.resize(num_vars(), 0); time_stamp[0] = max_steps;
conf_change.resize(num_vars(), true); conf_change[0] = false;
cscc.resize(num_vars(), 1); cscc[0] = 0;
m_var_info.resize(num_vars() + 1, var_info());
m_var_info[0].m_score = INT_MIN;
m_var_info[0].m_conf_change = false;
sscore.resize(num_vars() + 1, 0); sscore[0] = INT_MIN;
time_stamp.resize(num_vars() + 1, 0); time_stamp[0] = max_steps + 1;
cscc.resize(num_vars() + 1, 1); cscc[0] = 0;
init_slack();
init_scores();
init_goodvars();
}
init_goodvars();
void local_search::reinit_greedy() {
constraint_slack = constraint_k;
// init unsat stack
m_unsat_stack.reset();
// init solution: greedy
init_cur_solution();
// init varibale information
// variable 0 is the virtual variable
score.reset();
sscore.reset();
time_stamp.reset();
conf_change.reset();
cscc.reset();
score.resize(num_vars(), 0); score[0] = INT_MIN;
sscore.resize(num_vars(), 0); sscore[0] = INT_MIN;
time_stamp.resize(num_vars(), 0); time_stamp[0] = max_steps;
conf_change.resize(num_vars(), true); conf_change[0] = false;
cscc.resize(num_vars(), 1); cscc[0] = 0;
for (unsigned v = 0; v < num_vars(); ++v) {
// greedy here!!
if (coefficient_in_ob_constraint.get(v, 0) != 0)
cur_solution[v] = (coefficient_in_ob_constraint[v] > 0);
}
init_slack();
init_scores();
init_goodvars();
}
void local_search::calculate_and_update_ob() {
unsigned i, v;
objective_value = 0;
for (i = 0; i < ob_constraint.size(); ++i) {
v = ob_constraint[i].var_id;
if (cur_solution[v])
objective_value += ob_constraint[i].coefficient;
}
if (objective_value > best_objective_value) {
best_solution = cur_solution;
best_objective_value = objective_value;
stop = clock();
best_time = (double)(stop - start) / CLOCKS_PER_SEC;
}
}
void local_search::verify_solution() {
@ -192,10 +179,12 @@ namespace sat {
}
void local_search::add_cardinality(unsigned sz, literal const* c, unsigned k) {
unsigned id = constraint_term.size();
constraint_term.push_back(svector<term>());
unsigned id = m_num_constraints;
++m_num_constraints;
// constraint_term.push_back(svector<term>());
for (unsigned i = 0; i < sz; ++i) {
var_term.resize(c[i].var() + 1);
m_num_vars = std::max(c[i].var() + 1, m_num_vars);
// var_term.reserve(c[i].var() + 1);
term t;
t.constraint_id = id;
t.var_id = c[i].var();
@ -208,6 +197,9 @@ namespace sat {
local_search::local_search(solver& s) {
m_num_vars = 0;
m_num_constraints = 0;
// copy units
unsigned trail_sz = s.init_trail_size();
for (unsigned i = 0; i < trail_sz; ++i) {
@ -282,26 +274,30 @@ namespace sat {
local_search::~local_search() {
}
void local_search::add_soft(literal l, double weight) {
void local_search::add_soft(int v, int weight) {
// TBD
ob_term t;
t.var_id = v;
t.coefficient = weight;
ob_constraint.push_back(t);
}
lbool local_search::operator()() {
sat_params params;
std::cout << "my parameter value: " << params.cliff() << "\n";
//sat_params params;
//std::cout << "my parameter value: " << params.cliff() << "\n";
init();
bool reach_cutoff_time = false;
bool reach_known_best_value = false;
bool_var flipvar;
double elapsed_time = 0;
clock_t start = clock(), stop; // TBD, use stopwatch facility
srand(0); // TBD, use random facility and parameters to set random seed.
set_parameters();
//clock_t start = clock(), stop; // TBD, use stopwatch facility
start = clock();
// ################## start ######################
std::cout << "Start initialize and local search, restart in every " << max_steps << " steps\n";
for (unsigned tries = 0; ; ++tries) {
//std::cout << "Start initialize and local search, restart in every " << max_steps << " steps\n";
unsigned tries, step;
for (tries = 0; ; ++tries) {
reinit();
for (int step = 1; step <= max_steps; ++step) {
for (step = 1; step <= max_steps; ++step) {
// feasible
if (m_unsat_stack.empty()) {
calculate_and_update_ob();
@ -317,30 +313,34 @@ namespace sat {
// take a look at watch
stop = clock();
elapsed_time = (double)(stop - start) / CLOCKS_PER_SEC;
if (tries % 10 == 0)
std::cout << tries << ": " << elapsed_time << '\n';
if (elapsed_time > cutoff_time)
reach_cutoff_time = true;
if (reach_known_best_value || reach_cutoff_time)
break;
//if (reach_known_best_value || reach_cutoff_time)
// break;
}
if (reach_known_best_value) {
std::cout << elapsed_time << "\n";
}
else
else {
std::cout << -1 << "\n";
}
//print_solution();
std::cout << tries * max_steps + step << '\n';
// TBD: adjust return status
return l_undef;
}
void local_search::flip(bool_var flipvar)
{
static unsigned_vector hack_vector;
hack_vector.reset();
// already changed truth value!!!!
cur_solution[flipvar] = !cur_solution[flipvar];
unsigned v, c;
int org_flipvar_score = score[flipvar];
int org_flipvar_score = score(flipvar);
int org_flipvar_sscore = sscore[flipvar];
// update related clauses and neighbor vars
@ -358,7 +358,7 @@ namespace sat {
// flipping the slack increasing var will no long sat this constraint
if (cur_solution[v] == constraint_term[c][j].sense)
//score[v] -= constraint_weight[c];
--score[v];
--m_var_info[v].m_score;
}
break;
case -1: // from 0 to -1: sat -> unsat
@ -366,7 +366,8 @@ namespace sat {
v = constraint_term[c][j].var_id;
++cscc[v];
//score[v] += constraint_weight[c];
++score[v];
++m_var_info[v].m_score;
hack_vector.push_back(v);
// slack increasing var
if (cur_solution[v] == constraint_term[c][j].sense)
++sscore[v];
@ -379,7 +380,7 @@ namespace sat {
// flip the slack decreasing var will falsify this constraint
if (cur_solution[v] != constraint_term[c][j].sense) {
//score[v] -= constraint_weight[c];
--score[v];
--m_var_info[v].m_score;
--sscore[v];
}
}
@ -398,7 +399,8 @@ namespace sat {
// flip the slack decreasing var will no long falsify this constraint
if (cur_solution[v] != constraint_term[c][j].sense) {
//score[v] += constraint_weight[c];
++score[v];
++m_var_info[v].m_score;
hack_vector.push_back(v);
++sscore[v];
}
}
@ -408,7 +410,7 @@ namespace sat {
v = constraint_term[c][j].var_id;
++cscc[v];
//score[v] -= constraint_weight[c];
--score[v];
--m_var_info[v].m_score;
// slack increasing var no longer sat this var
if (cur_solution[v] == constraint_term[c][j].sense)
--sscore[v];
@ -419,9 +421,11 @@ namespace sat {
for (unsigned j = 0; j < constraint_term[c].size(); ++j) {
v = constraint_term[c][j].var_id;
// flip the slack increasing var will satisfy this constraint
if (cur_solution[v] == constraint_term[c][j].sense)
//score[v] += constraint_weight[c];
++score[v];
if (cur_solution[v] == constraint_term[c][j].sense) {
//score[v] += constraint_weight[c];
++m_var_info[v].m_score;
hack_vector.push_back(v);
}
}
break;
default:
@ -430,39 +434,54 @@ namespace sat {
}
}
score[flipvar] = -org_flipvar_score;
m_var_info[flipvar].m_score = -org_flipvar_score;
sscore[flipvar] = -org_flipvar_sscore;
conf_change[flipvar] = false;
m_var_info[flipvar].m_conf_change = false;
cscc[flipvar] = 0;
/* update CCD */
// remove the vars no longer okvar in okvar stack
// remove the vars no longer goodvar in goodvar stack
for (unsigned i = goodvar_stack.size(); i > 0;) {
--i;
v = goodvar_stack[i];
if (score[v] <= 0) {
if (score(v) <= 0) {
goodvar_stack[i] = goodvar_stack.back();
goodvar_stack.pop_back();
already_in_goodvar_stack[v] = false;
m_var_info[v].m_in_goodvar_stack = false;
}
}
// update all flipvar's neighbor's conf_change to true, add goodvar/okvar
for (unsigned i = 0; i < var_neighbor[flipvar].size(); ++i) {
#if 0
unsigned sz = var_neighbor[flipvar].size();
for (unsigned i = 0; i < sz; ++i) {
v = var_neighbor[flipvar][i];
conf_change[v] = true;
if (score[v] > 0 && !already_in_goodvar_stack[v]) {
m_var_info[v].m_conf_change = true;
if (score(v) > 0 && !already_in_goodvar_stack(v)) {
goodvar_stack.push_back(v);
already_in_goodvar_stack[v] = true;
m_var_info[v].m_in_goodvar_stack = true;
}
}
#else
unsigned sz = hack_vector.size();
for (unsigned i = 0; i < sz; ++i)
{
v = hack_vector[i];
m_var_info[v].m_conf_change = true;
if (score(v) > 0 && !already_in_goodvar_stack(v)) {
goodvar_stack.push_back(v);
m_var_info[v].m_in_goodvar_stack = true;
}
}
#endif
}
bool local_search::tie_breaker_sat(bool_var v, bool_var best_var) {
// most improvement on objective value
int v_imp = cur_solution[v] ? -coefficient_in_ob_constraint.get(v, 0) : coefficient_in_ob_constraint.get(v, 0);
int b_imp = cur_solution[best_var] ? -coefficient_in_ob_constraint.get(best_var, 0) : coefficient_in_ob_constraint.get(best_var, 0);
//std::cout << v_imp << "\n";
// break tie 1: max imp
// break tie 2: conf_change
// break tie 3: time_stamp
@ -470,8 +489,8 @@ namespace sat {
return
(v_imp > b_imp) ||
((v_imp == b_imp) &&
((conf_change[v] && !conf_change[best_var]) ||
((conf_change[v] == conf_change[best_var]) &&
((conf_change(v) && !conf_change(best_var)) ||
((conf_change(v) == conf_change(best_var)) &&
(time_stamp[v] < time_stamp[best_var]))));
}
@ -481,18 +500,18 @@ namespace sat {
// break tie 3: cscc
// break tie 4: oldest one
return
(score[v] > score[best_var]) ||
((score[v] == score[best_var]) &&
(sscore[v] > sscore[best_var]) ||
((sscore[v] == sscore[best_var]) &&
(cscc[v] > cscc[best_var]) ||
((cscc[v] == cscc[best_var]) &&
(time_stamp[v] < time_stamp[best_var]))));
((score(v) > score(best_var)) ||
((score(v) == score(best_var)) &&
((sscore[v] > sscore[best_var]) ||
((sscore[v] == sscore[best_var]) &&
((cscc[v] > cscc[best_var]) ||
((cscc[v] == cscc[best_var]) &&
(time_stamp[v] < time_stamp[best_var])))))));
}
bool_var local_search::pick_var() {
int c, v;
bool_var best_var = num_vars()-1;
bool_var best_var = 0;
// SAT Mode
if (m_unsat_stack.empty()) {
@ -521,32 +540,42 @@ namespace sat {
// Diversification Mode
c = m_unsat_stack[rand() % m_unsat_stack.size()]; // a random unsat constraint
// Within c, from all slack increasing var, choose the oldest one
for (unsigned i = 0; i < constraint_term[c].size(); ++i) {
unsigned c_size = constraint_term[c].size();
for (unsigned i = 0; i < c_size; ++i) {
v = constraint_term[c][i].var_id;
if (cur_solution[v] == constraint_term[c][i].sense && time_stamp[v] < time_stamp[best_var])
best_var = v;
}
//++rd;
return best_var;
}
void local_search::set_parameters() {
srand(m_config.seed());
cutoff_time = m_config.cutoff_time();
s_id = m_config.strategy_id();
best_known_value = m_config.best_known_value();
if (s_id == 0)
max_steps = num_vars();
else if (s_id == 1)
max_steps = (int) (1.5 * num_vars());
else if (s_id == 1)
max_steps = 2 * num_vars();
else if (s_id == 2)
max_steps = (int) (2.5 * num_vars());
else if (s_id == 3)
max_steps = 3 * num_vars();
else {
std::cout << "Invalid strategy id!" << std::endl;
exit(-1);
}
/*std::cout << "seed:\t" << m_config.seed() << '\n';
std::cout << "cutoff time:\t" << m_config.cutoff_time() << '\n';
std::cout << "strategy id:\t" << m_config.strategy_id() << '\n';
std::cout << "best_known_value:\t" << m_config.best_known_value() << '\n';
std::cout << "max_steps:\t" << max_steps << '\n';
*/
}
void local_search::print_info() {
for (int v = 1; v <= num_vars(); ++v) {
std::cout << var_neighbor[v].size() << '\t' << cur_solution[v] << '\t' << conf_change(v) << '\t' << score(v) << '\t' << sscore[v] << '\n';
}
}
}

120
src/sat/sat_local_search.h
View file

@ -1,21 +1,21 @@
/*++
Copyright (c) 2017 Microsoft Corporation
Copyright (c) 2017 Microsoft Corporation
Module Name:
Module Name:
sat_local_search.h
sat_local_search.h
Abstract:
Abstract:
Local search module for cardinality clauses.
Local search module for cardinality clauses.
Author:
Author:
Sixue Liu 2017-2-21
Sixue Liu 2017-2-21
Notes:
Notes:
--*/
--*/
#ifndef _SAT_LOCAL_SEARCH_H_
#define _SAT_LOCAL_SEARCH_H_
@ -24,8 +24,36 @@ Notes:
namespace sat {
class local_search {
class local_search_config {
unsigned m_seed;
unsigned m_cutoff_time;
unsigned m_strategy_id;
int m_best_known_value;
public:
local_search_config()
{
m_seed = 0;
m_cutoff_time = 1;
m_strategy_id = 0;
m_best_known_value = INT_MAX;
}
unsigned seed() const { return m_seed; }
unsigned cutoff_time() const { return m_cutoff_time; }
unsigned strategy_id() const { return m_strategy_id; }
unsigned best_known_value() const { return m_best_known_value; }
void set_seed(unsigned s) { m_seed = s; }
void set_cutoff_time(unsigned t) { m_cutoff_time = t; }
void set_strategy_id(unsigned i) { m_strategy_id = i; }
void set_best_known_value(unsigned v) { m_best_known_value = v; }
};
#define MAX_VARS 5000
#define MAX_CONSTRAINTS 100
class local_search {
typedef svector<bool> bool_vector;
// data structure for a term in objective function
@ -48,21 +76,38 @@ namespace sat {
// terms arrays
vector<svector<term> > var_term; // var_term[i][j] means the j'th term of var i
vector<svector<term> > constraint_term; // constraint_term[i][j] means the j'th term of constraint i
svector<term> var_term[MAX_VARS]; // var_term[i][j] means the j'th term of var i
svector<term> constraint_term[MAX_CONSTRAINTS]; // constraint_term[i][j] means the j'th term of constraint i
unsigned m_num_vars, m_num_constraints;
// parameters of the instance
unsigned num_vars() const { return var_term.size(); } // var index from 1 to num_vars
unsigned num_constraints() const { return constraint_term.size(); } // constraint index from 1 to num_constraint
unsigned num_vars() const { return m_num_vars - 1; } // var index from 1 to num_vars
unsigned num_constraints() const { return m_num_constraints; } // constraint index from 1 to num_constraint
// information about the variable
int_vector coefficient_in_ob_constraint; // initialized to be 0
int_vector score;
// int_vector score;
int_vector sscore; // slack score
struct var_info {
bool m_conf_change; // whether its configure changes since its last flip
bool m_in_goodvar_stack;
int m_score;
var_info():
m_conf_change(true),
m_in_goodvar_stack(false),
m_score(0)
{}
};
svector<var_info> m_var_info;
inline int score(unsigned v) const { return m_var_info[v].m_score; }
inline bool already_in_goodvar_stack(unsigned v) const { return m_var_info[v].m_in_goodvar_stack; }
inline bool conf_change(unsigned v) const { return m_var_info[v].m_conf_change; }
int_vector time_stamp; // the flip time stamp
bool_vector conf_change; // whether its configure changes since its last flip
// bool_vector conf_change;
int_vector cscc; // how many times its constraint state configure changes since its last flip
vector<bool_var_vector> var_neighbor; // all of its neighborhoods variable
/* TBD: other scores */
@ -70,8 +115,8 @@ namespace sat {
// information about the constraints
int_vector constraint_k; // the right side k of a constraint
int_vector constraint_slack; // =constraint_k[i]-true_terms[i], if >=0 then sat
int_vector nb_slack; // constraint_k - ob_var(same in ob) - none_ob_true_terms_count. if < 0: some ob var might be flipped to false, result in an ob decreasing
bool_vector has_true_ob_terms;
//int_vector nb_slack; // constraint_k - ob_var(same in ob) - none_ob_true_terms_count. if < 0: some ob var might be flipped to false, result in an ob decreasing
//bool_vector has_true_ob_terms;
// unsat constraint stack
int_vector m_unsat_stack; // store all the unsat constraits
@ -79,19 +124,21 @@ namespace sat {
// configuration changed decreasing variables (score>0 and conf_change==true)
int_vector goodvar_stack;
bool_vector already_in_goodvar_stack;
// bool_vector already_in_goodvar_stack;
// information about solution
bool_vector cur_solution; // the current solution
int objective_value; // the objective function value corresponds to the current solution
bool_vector best_solution; // the best solution so far
int best_objective_value = 0; // the objective value corresponds to the best solution so far
int best_objective_value = -1; // the objective value corresponds to the best solution so far
// for non-known instance, set as maximal
int best_known_value = INT_MAX; // best known value for this instance
// cutoff
int cutoff_time = 1; // seconds
int max_steps = 2000000000; // < 2147483647
clock_t start, stop;
double best_time;
// for tuning
int s_id = 0; // strategy id
@ -101,7 +148,6 @@ namespace sat {
void reinit();
void reinit_orig();
void reinit_greedy();
void init_cur_solution();
void init_slack();
@ -124,11 +170,35 @@ namespace sat {
void display(std::ostream& out);
void unsat(int constraint_id) { m_unsat_stack.push_back(constraint_id); }
void print_info();
bool check_goodvar() {
unsigned g = 0;
for (unsigned v = 1; v <= num_vars(); ++v) {
if (conf_change(v) && score(v) > 0) {
++g;
if (!already_in_goodvar_stack(v))
std::cout << "3\n";
}
}
if (g == goodvar_stack.size())
return true;
else {
if (g < goodvar_stack.size())
std::cout << "1\n";
else
std::cout << "2\n"; // delete too many
return false;
}
}
void add_clause(unsigned sz, literal const* c);
void unsat(int c) {
m_index_in_unsat_stack[c] = m_unsat_stack.size();
m_unsat_stack.push_back(c);
}
// swap the deleted one with the last one and pop
void sat(int c) {
int last_unsat_constraint = m_unsat_stack.back();
@ -143,13 +213,15 @@ namespace sat {
~local_search();
void add_soft(literal l, double weight);
void add_soft(int l, int weight);
void add_cardinality(unsigned sz, literal const* c, unsigned k);
lbool operator()();
local_search_config& config() { return m_config; }
local_search_config m_config;
};
}