bool Solver::addClause(vec<Lit>& ps)
{
assert(decisionLevel() == 0);
if (!ok)
return false;
else{
// Check if clause is satisfied and remove false/duplicate literals:
sort(ps);
Lit p; int i, j;
for (i = j = 0, p = lit_Undef; i < ps.size(); i++)
if (value(ps[i]) == l_True || ps[i] == ~p)
return true;
else if (value(ps[i]) != l_False && ps[i] != p)
ps[j++] = p = ps[i];
ps.shrink(i - j);
}
if (ps.size() == 0)
return ok = false;
else if (ps.size() == 1){
assert(value(ps[0]) == l_Undef);
uncheckedEnqueue(ps[0]);
return ok = (propagate() == NULL);
}else{
Clause* c = Clause_new(ps, false);
clauses.push(c);
attachClause(*c);
}
return true;
}
/*_________________________________________________________________________________________________
|
| search : (nof_conflicts : int) (nof_learnts : int) (params : const SearchParams&) -> [lbool]
|
| Description:
| Search for a model the specified number of conflicts, keeping the number of learnt clauses
| below the provided limit. NOTE! Use negative value for 'nof_conflicts' or 'nof_learnts' to
| indicate infinity.
|
| Output:
| 'l_True' if a partial assigment that is consistent with respect to the clauseset is found. If
| all variables are decision variables, this means that the clause set is satisfiable. 'l_False'
| if the clause set is unsatisfiable. 'l_Undef' if the bound on number of conflicts is reached.
|________________________________________________________________________________________________@*/
lbool Solver::search(int nof_conflicts, int nof_learnts)
{
assert(ok);
int backtrack_level;
int conflictC = 0;
vec<Lit> learnt_clause;
starts++;
for (;;){
#ifdef _DEBUGSEARCH
for(int k=0; k<decisionLevel(); ++k)
std::cout << " ";
std::cout << "propagate" << std::endl;
#endif
Clause* confl = propagate();
if (confl != NULL){
// CONFLICT
conflicts++; conflictC++;
if (decisionLevel() <= init_level) {
#ifdef _DEBUGSEARCH
std::cout << "infeasible" << std::endl;
#endif
return l_False;
}
learnt_clause.clear();
analyze(confl, learnt_clause, backtrack_level);
cancelUntil(backtrack_level);
#ifdef _DEBUGSEARCH
for(int k=0; k<decisionLevel(); ++k)
std::cout << " ";
std::cout << "backtrack to " << decisionLevel() << std::endl;
#endif
assert(value(learnt_clause[0]) == l_Undef);
if (learnt_clause.size() == 1){
uncheckedEnqueue(learnt_clause[0]);
#ifdef _DEBUGSEARCH
for(int k=0; k<decisionLevel(); ++k)
std::cout << " ";
std::cout << "deduce " ;
printLit(learnt_clause[0]);
std::cout << std::endl;
#endif
}else{
Clause* c = Clause_new(learnt_clause, true);
#ifdef _DEBUGSEARCH
for(int k=0; k<decisionLevel(); ++k)
std::cout << " ";
std::cout << "deduce ";
printClause(*c);
std::cout << std::endl;
#endif
learnts.push(c);
attachClause(*c);
claBumpActivity(*c);
uncheckedEnqueue(learnt_clause[0], c);
}
varDecayActivity();
claDecayActivity();
}else{
// NO CONFLICT
if (nof_conflicts >= 0 && conflictC >= nof_conflicts){
// Reached bound on number of conflicts:
progress_estimate = progressEstimate();
cancelUntil(0);
return l_Undef; }
// Simplify the set of problem clauses:
if (decisionLevel() == 0 && !simplify())
return l_False;
if (nof_learnts >= 0 && learnts.size()-nAssigns() >= nof_learnts)
// Reduce the set of learnt clauses:
reduceDB();
Lit next = lit_Undef;
while (decisionLevel() < assumptions.size()){
// Perform user provided assumption:
Lit p = assumptions[decisionLevel()];
if (value(p) == l_True){
// Dummy decision level:
newDecisionLevel();
}else if (value(p) == l_False){
analyzeFinal(~p, conflict);
return l_False;
}else{
next = p;
break;
}
}
if (next == lit_Undef){
// New variable decision:
decisions++;
next = pickBranchLit(polarity_mode, random_var_freq);
if (next == lit_Undef)
// Model found:
return l_True;
}
#ifdef _DEBUGSEARCH
for(int k=0; k<decisionLevel(); ++k)
std::cout << " ";
std::cout << "branch on " ;
printLit(next);
std::cout << std::endl;
#endif
// Increase decision level and enqueue 'next'
assert(value(next) == l_Undef);
newDecisionLevel();
uncheckedEnqueue(next);
}
}
}
/*_________________________________________________________________________________________________
|
| search : (nof_conflicts : int) (nof_learnts : int) (params : const SearchParams&) -> [lbool]
|
| Description:
| Search for a model the specified number of conflicts, keeping the number of learnt clauses
| below the provided limit. NOTE! Use negative value for 'nof_conflicts' or 'nof_learnts' to
| indicate infinity.
|
| Output:
| 'l_True' if a partial assigment that is consistent with respect to the clauseset is found. If
| all variables are decision variables, this means that the clause set is satisfiable. 'l_False'
| if the clause set is unsatisfiable. 'l_Undef' if the bound on number of conflicts is reached.
|________________________________________________________________________________________________@*/
lbool MiniSATP::search(int nof_conflicts, int nof_learnts)
{
assert(ok);
int backtrack_level;
int conflictC = 0;
vec<Lit> learnt_clause;
starts++;
bool first = true;
for (;;){
Clause* confl = propagate();
if (confl != NULL){
// CONFLICT
conflicts++; conflictC++;
if (decisionLevel() == 0)
{
// Added Lines
fillExplanation(confl);
return l_False;
}
first = false;
learnt_clause.clear();
analyze(confl, learnt_clause, backtrack_level);
cancelUntil(backtrack_level);
assert(value(learnt_clause[0]) == l_Undef);
if (learnt_clause.size() == 1){
uncheckedEnqueue(learnt_clause[0]);
}else{
Clause* c = Clause_new(learnt_clause, true);
learnts.push(c);
attachClause(*c);
claBumpActivity(*c);
uncheckedEnqueue(learnt_clause[0], c);
}
varDecayActivity();
claDecayActivity();
}else{
// NO CONFLICT
if (nof_conflicts >= 0 && conflictC >= nof_conflicts){
// Reached bound on number of conflicts:
progress_estimate = progressEstimate();
cancelUntil(0);
return l_Undef; }
// Simplify the set of problem clauses:
if (decisionLevel() == 0 && !simplify())
{
return l_False;
}
if (nof_learnts >= 0 && learnts.size()-nAssigns() >= nof_learnts)
// Reduce the set of learnt clauses:
reduceDB();
Lit next = lit_Undef;
while (decisionLevel() < assumptions.size()){
// Perform user provided assumption:
Lit p = assumptions[decisionLevel()];
if (value(p) == l_True){
// Dummy decision level:
newDecisionLevel();
}else if (value(p) == l_False){
analyzeFinal(~p, conflict);
return l_False;
}else{
next = p;
break;
}
}
if (next == lit_Undef){
// New variable decision:
decisions++;
next = pickBranchLit(polarity_mode, random_var_freq);
if (next == lit_Undef)
{
// Added Line
// Clear explanation vector if satisfiable
explanation.clear( );
// Model found:
return l_True;
}
}
// Increase decision level and enqueue 'next'
assert(value(next) == l_Undef);
newDecisionLevel();
uncheckedEnqueue(next);
}
}
}
lbool Solver::search(int nof_conflicts, int nof_learnts)
{
assert(ok);
int backtrack_level;
int conflictsC = 0;
vec<Lit> learnt_clause;
int nblevels=0,nbCC=0,merged=0;
starts++;
bool first = true;
for (;;){
Clause* confl = propagate();
//Conflict at a later stage.
if (backup.running
&& confl != NULL
&& backup.stage == 1
) {
#ifdef RESTORE_FULL
printf("Somebody else (maybe bin clause) did the conflict -- stage is one, going 2\n");
#endif
fullCancelUntil(backup.level, backup.sublevel);
backup.stage = 2;
continue;
}
if (confl != NULL){
assert(backup.stage == 0);
// CONFLICT
conflicts++; conflictsC++;cons++;nbCC++;
if (decisionLevel() == 0) return l_False;
first = false;
learnt_clause.clear();
analyze(confl, learnt_clause, backtrack_level,nblevels,merged);
conf4Stats++;
nbDecisionLevelHistory.push(nblevels);
totalSumOfDecisionLevel += nblevels;
cancelUntil(backtrack_level);
assert(value(learnt_clause[0]) == l_Undef);
if (learnt_clause.size() == 1){
assert(decisionLevel() == 0);
uncheckedEnqueue(learnt_clause[0]);
nbUn++;
}else{
Clause* c = Clause_new(learnt_clause, clIndex++, conflicts, true);
dumpFile << "INSERT INTO clausedata(runID, idx, timeofcreation, size) VALUES("
<< runID << ","
<< c->getIndex() << ","
<< c->getTimeOfCreation() << ","
<< c->size()
<< ");" << std::endl;
learnts.push(c);
c->setActivity(nblevels); // LS
if(nblevels<=2) nbDL2++;
if(c->size()==2) nbBin++;
attachClause(*c);
uncheckedEnqueue(learnt_clause[0], c);
if (backup.running
&& backup.stage == 0
) {
saveState();
#ifdef RESTORE
printf("Saving state after conflict at dec level: %d, sublevel: %d\n", decisionLevel(), trail.size());
#endif
}
}
varDecayActivity();
}else{
if (backup.stage == 0
&& nbDecisionLevelHistory.isvalid()
&& ((nbDecisionLevelHistory.getavg()*0.7) > (totalSumOfDecisionLevel / conf4Stats))
) {
nbDecisionLevelHistory.fastclear();
progress_estimate = progressEstimate();
cancelUntil(0);
return l_Undef;
}
// Simplify the set of problem clauses:
if (backup.stage == 0
&& decisionLevel() == 0 && !simplify()
) {
return l_False;
}
Lit next = lit_Undef;
if (backup.stage == 0
&& ((cons-curRestart * nbclausesbeforereduce) >=0)
) {
curRestart = (conflicts/ nbclausesbeforereduce)+1;
reduceDB();
nbclausesbeforereduce += 500;
}
if (next == lit_Undef){
// New variable decision:
decisions++;
next = pickBranchLit(polarity_mode, random_var_freq);
if (next == lit_Undef)
// Model found:
return l_True;
}
// Increase decision level and enqueue 'next'
assert(value(next) == l_Undef);
newDecisionLevel();
uncheckedEnqueue(next);
if (backup.running
&& backup.stage == 0
) {
saveState();
#ifdef RESTORE
printf("Saving state after pickbranch at dec level: %d, sublevel: %d\n", decisionLevel(), trail.size());
printf("sublevel: %d, level: %d, qhead:%d\n", trail.size(), decisionLevel(), qhead);
#endif
}
}
}
}
// Modified line
// bool MiniSATP::addClause(vec<Lit>& ps, Enode * e)
bool MiniSATP::addClause(vec<Lit>& psin, Enode * e)
{
assert( decisionLevel() == 0 );
assert( ok );
// Added Lines
vec< Lit > ps;
psin.copyTo( ps );
if (!ok)
return false;
// Check if clause is satisfied and remove false/duplicate literals:
sort(ps);
Lit p; int i, j;
for (i = j = 0, p = lit_Undef; i < ps.size(); i++)
{
if (value(ps[i]) == l_True || ps[i] == ~p)
return true;
else if (value(ps[i]) != l_False && ps[i] != p)
ps[j++] = p = ps[i];
}
ps.shrink(i - j);
//=================================================================================================
// Modified Code
assert( psin.size( ) == 1 || ps.size() > 0 );
//
// This case happens only when deductions are enabled
//
if (ps.size() == 0)
{
Clause * confl = Clause_new( psin );
if ( confl == NULL ) return ok = true;
initExpDup( );
explanation.push_back( e );
storeExpDup( e );
fillExplanation( confl );
doneExpDup( );
assert( !explanation.empty( ) );
return ok = false;
}
if (ps.size() == 1)
{
assert(value(ps[0]) == l_Undef);
uncheckedEnqueue(ps[0]);
/*
const Var v = var(ps[0]);
if ( var_to_enode.size( ) <= v )
var_to_enode.resize( v + 1, NULL );
var_to_enode[ v ] = e;
*/
/*
if ( e != NULL && seen_in_conflict.insert( e->getId( ) ).second )
explanation.push_back( e );
*/
// undo_stack_oper.push_back( NEWUNIT );
// undo_stack_elem.push_back( (void *)v );
#define LAZY_PROPAGATION 0
#if LAZY_PROPAGATION
#else
// Modified Line
// return ok = (propagate() == NULL);
#if LIMIT_DEDUCTIONS
deductions_done_in_call = 0;
#endif
Clause * confl = propagate( theory_prop );
if ( confl == NULL ) return ok = true;
initExpDup( );
fillExplanation( confl );
doneExpDup( );
assert( !explanation.empty( ) );
return ok = false;
#endif
// Modified Code
//=================================================================================================
}
else
{
Clause* c = Clause_new(ps, false);
clauses.push(c);
attachClause(*c);
// Added Lines
undo_stack_oper.push_back( NEWCLAUSE );
undo_stack_elem.push_back( (void *)c );
}
return true;
}
lbool Solver::search(int nof_conflicts, int nof_learnts)
{
assert(ok);
int backtrack_level;
int conflictsC = 0;
vec<Lit> learnt_clause;
int nblevels=0,nbCC=0,merged=0;
starts++;
bool first = true;
for (;;){
Clause* confl = propagate();
if (confl != NULL){
// CONFLICT
conflicts++; conflictsC++;cons++;nbCC++;
if (decisionLevel() == 0) return l_False;
first = false;
learnt_clause.clear();
analyze(confl, learnt_clause, backtrack_level,nblevels,merged);
conf4Stats++;
nbDecisionLevelHistory.push(nblevels);
totalSumOfDecisionLevel += nblevels;
cancelUntil(backtrack_level);
assert(value(learnt_clause[0]) == l_Undef);
if (learnt_clause.size() == 1){
uncheckedEnqueue(learnt_clause[0]);
nbUn++;
}else{
Clause* c = Clause_new(learnt_clause, true);
learnts.push(c);
c->setActivity(nblevels); // LS
if(nblevels<=2) nbDL2++;
if(c->size()==2) nbBin++;
attachClause(*c);
claBumpActivity(*c);
uncheckedEnqueue(learnt_clause[0], c);
}
varDecayActivity();
claDecayActivity();
}else{
if (
( nbDecisionLevelHistory.isvalid() &&
((nbDecisionLevelHistory.getavg()*0.7) > (totalSumOfDecisionLevel / conf4Stats)))) {
nbDecisionLevelHistory.fastclear();
progress_estimate = progressEstimate();
cancelUntil(0);
return l_Undef; }
// Simplify the set of problem clauses:
if (decisionLevel() == 0 && !simplify())
return l_False;
//
Lit next = lit_Undef;
if(cons-curRestart* nbclausesbeforereduce>=0)
{
curRestart = (conflicts/ nbclausesbeforereduce)+1;
reduceDB();
nbclausesbeforereduce += 500;
}
if (next == lit_Undef){
// New variable decision:
decisions++;
next = pickBranchLit(polarity_mode, random_var_freq);
if (next == lit_Undef)
// Model found:
return l_True;
}
// Increase decision level and enqueue 'next'
assert(value(next) == l_Undef);
newDecisionLevel();
uncheckedEnqueue(next);
}
}
}
bool Solver::addClause(vec<Lit>& ps)
{
#ifdef __PRINT
for (int i = 0; i < ps.size(); i++) {
if (_literal_count.find(ps[i]) == _literal_count.end())
_literal_count[ps[i]] = 1;
else
_literal_count[ps[i]]++;
}
#endif
if (!ok)
return false;
// Check if clause is satisfied and remove false/duplicate literals:
// Special attention is put on single and double literal clauses
if (ps.size() == 1 ||
(ps.size() == 2 && ps[0] == ps[1])) {
// Unary or Binary with a duplicate literal
if (value(ps[0]) == l_True) {
#ifdef __PRINT
printf("---satisfied---\n");
#endif
return true;
} else if (value(ps[0]) == l_False) {
#ifdef __PRINT
printf("Clause empty after simplification\n");
#endif
return ok = false;
} else {
uncheckedEnqueue(ps[0]);
return ok = (propagate() == NULL);
}
} else if (ps.size() == 2) {
// Real Binary clauses
if (value(ps[0]) == l_True ||
value(ps[1]) == l_True ||
ps[0] == ~ps[1]) {
#ifdef __PRINT
printf("---satisfied---\n");
#endif
return true;
} else if (value(ps[0]) == l_False) {
if (value(ps[1]) == l_False) {
#ifdef __PRINT
printf("Clause empty after simplification\n");
#endif
return ok = false;
} else {
uncheckedEnqueue(ps[1]);
return ok = (propagate() == NULL);
}
} else {
if (value(ps[1]) == l_False) {
uncheckedEnqueue(ps[0]);
return ok = (propagate() == NULL);
} else {
Clause* c = Clause::Clause_new(ps, false);
clauses.push(c);
attachClause(*c);
}
}
} else {
sort(ps);
Lit p; int i, j;
for (i = j = 0, p = lit_Undef; i < ps.size(); i++)
if (value(ps[i]) == l_True || ps[i] == ~p) {
#ifdef __PRINT
printf("---satisfied---\n");
#endif
return true;
}
else if (value(ps[i]) != l_False && ps[i] != p)
ps[j++] = p = ps[i];
ps.shrink(i - j);
if (ps.size() == 0) {
#ifdef __PRINT
printf("Clause empty after simplification\n");
#endif
return ok = false;
}
else if (ps.size() == 1){
uncheckedEnqueue(ps[0]);
return ok = (propagate() == NULL);
}else{
Clause* c = Clause::Clause_new(ps, false);
clauses.push(c);
attachClause(*c);
#ifdef __PRINT
// printf("Simplified: ");
// printClause(*c);
// printf("\n");
#endif
}
}
return true;
}
/*_________________________________________________________________________________________________
|
| search : (nof_conflicts : int) (nof_learnts : int) (params : const SearchParams&) -> [lbool]
|
| Description:
| Search for a model the specified number of conflicts, keeping the number of learnt clauses
| below the provided limit. NOTE! Use negative value for 'nof_conflicts' or 'nof_learnts' to
| indicate infinity.
|
| Output:
| 'l_True' if a partial assigment that is consistent with respect to the clauseset is found. If
| all variables are decision variables, this means that the clause set is satisfiable. 'l_False'
| if the clause set is unsatisfiable. 'l_Undef' if the bound on number of conflicts is reached.
|________________________________________________________________________________________________@*/
lbool Solver::search(int nof_conflicts, int nof_learnts)
{
int backtrack_level;
int conflictC = 0;
vec<Lit> learnt_clause;
starts++;
// bool first = true;
for (;;){
Clause* confl = propagate();
if (confl != NULL){
// CONFLICT
conflicts++; conflictC++;
if (decisionLevel() == 0) return l_False;
// first = false;
learnt_clause.clear();
analyze(confl, learnt_clause, backtrack_level);
cancelUntil(backtrack_level);
#ifdef __PRINT
char c1 = sign(learnt_clause[0]) ? '-' : '+';
char c2 = polarity[var(learnt_clause[0])] == 0 ? '-' : '+';
if (decisionLevel() < minDecisionLevel &&
original_activity[var(learnt_clause[0])] > 0) {
printf("Conflict record: ");
printLit(learnt_clause[0]);
printf(" .%d.\t.%d. %c .%c .%g\n", decisionLevel(), trail.size(),
c1, c2, original_activity[var(learnt_clause[0])]);
if (decisionLevel() == 0) {
minDecisionLevel = (unsigned)(-1);
} else {
minDecisionLevel = decisionLevel();
}
}
#endif
if (learnt_clause.size() == 1){
uncheckedEnqueue(learnt_clause[0]);
}else{
Clause* c = Clause::Clause_new(learnt_clause, true);
learnts.push(c);
attachClause(*c);
claBumpActivity(*c);
uncheckedEnqueue(learnt_clause[0], c);
}
#ifdef _MINISAT_DEFAULT_VSS
varDecayActivity();
#endif
claDecayActivity();
}else{
// NO CONFLICT
if (nof_conflicts >= 0 && conflictC >= nof_conflicts){
// Reached bound on number of conflicts:
progress_estimate = progressEstimate();
// cancelUntil(0);
return l_Undef; }
// Simplify the set of problem clauses:
if (decisionLevel() == 0 && !simplify())
return l_False;
if (nof_learnts >= 0 && learnts.size()-nAssigns() >= nof_learnts)
// Reduce the set of learnt clauses:
reduceDB();
Lit next = lit_Undef;
while (decisionLevel() < assumptions.size()){
// Perform user provided assumption:
Lit p = assumptions[decisionLevel()];
if (value(p) == l_True){
// Dummy decision level:
newDecisionLevel();
}else if (value(p) == l_False){
analyzeFinal(~p, conflict);
return l_False;
}else{
next = p;
break;
}
}
if (next == lit_Undef){
// New variable decision:
decisions++;
next = pickBranchLit(polarity_mode, random_var_freq);
if (next == lit_Undef)
// Model found:
return l_True;
#ifdef __PRINT
printf("Decision: ");
printLit(next);
printf("\t.%f.\t.%d.\t.%d.", activity[var(next)], decisionLevel(), trail.size());
printf("\n");
#endif
}
// Increase decision level and enqueue 'next'
newDecisionLevel();
uncheckedEnqueue(next);
}
//#ifdef __PRINT
// printTrail();
//#endif
}
}
void Solver::addConflictingClause(vec<Lit>& lits) {
if (lits.size() == 1) {
if (value(lits[0]) == l_True)
return;
if (value(lits[0]) == l_False) {
if (level[var(lits[0])] == 0) {
ok = false;
return;
}
cancelUntil(level[var(lits[0])] - 1);
}
uncheckedEnqueue(~lits[0]);
} else {
vec<Lit> learnt_clause;
int backtrack_level;
for (int i = 1; i < lits.size(); i++) {
if (value(lits[0]) == l_False) {
if (value(lits[i]) != l_False){
Lit tmp = lits[0];
lits[0] = lits[i];
lits[i] = tmp;
}
} else if (value(lits[1]) == l_False) {
if (value(lits[i]) != l_False){
Lit tmp = lits[1];
lits[1] = lits[i];
lits[i] = tmp;
break;
}
}
}
if (value(lits[0]) == l_False) {
for (int i = 1; i < lits.size(); i++) {
if (level[var(lits[i])] > level[var(lits[0])]) {
Lit tmp = lits[0];
lits[0] = lits[i];
lits[i] = tmp;
}
}
}
if (value(lits[1]) == l_False) {
for (int i = 2; i < lits.size(); i++) {
if (level[var(lits[i])] > level[var(lits[1])]) {
Lit tmp = lits[1];
lits[1] = lits[i];
lits[i] = tmp;
}
}
}
Clause* confl = Clause::Clause_new(lits, true);
clauses.push(confl);
attachClause(*confl);
if (value(lits[0]) != l_False) {
if (value(lits[1]) == l_False) {
uncheckedEnqueue(lits[0], confl);
}
return;
}
int maxLevel = level[var(lits[0])];
cancelUntil(maxLevel);
if (maxLevel == 0) {
ok = false;
return;
}
learnt_clause.clear();
analyze(confl, learnt_clause, backtrack_level);
cancelUntil(backtrack_level);
if (learnt_clause.size() == 1){
uncheckedEnqueue(learnt_clause[0]);
} else {
Clause* c = Clause::Clause_new(learnt_clause, true);
learnts.push(c);
attachClause(*c);
claBumpActivity(*c);
uncheckedEnqueue(learnt_clause[0], c);
}
#ifdef _MINISAT_DEFAULT_VSS
varDecayActivity();
#endif
claDecayActivity();
}
}
