void Slave::splitJob() {
vec<int> message;
int num_splits;
//fprintf(stderr, "%d: Split job called, assumptions.size()=%d, DL=%d!\n", thread_no, engine.assumptions.size(), engine.decisionLevel());
profile_start();
MPI_Recv(&num_splits, 1, MPI_INT, 0, STEAL_TAG, MPI_COMM_WORLD, &s);
int max_splits = engine.decisionLevel() - engine.assumptions.size() - 1;
if (num_splits > max_splits) num_splits = max_splits;
if (num_splits < 0) num_splits = 0;
if (FULL_DEBUG) fprintf(stderr, "%d: Splitting %d jobs\n", thread_no, num_splits);
for (int i = 0; i < num_splits; i++) {
engine.assumptions.push(toInt(sat.decLit(engine.assumptions.size()+1)));
sat.incVarUse(engine.assumptions.last()/2);
}
assert(num_splits == 0 || engine.decisionLevel() > engine.assumptions.size());
vec<Lit> ps;
for (int i = 0; i < engine.assumptions.size(); i++) ps.push(toLit(engine.assumptions[i]));
Clause *c = Clause_new(ps);
sat.convertToSClause(*c);
free(c);
message.push(num_splits);
sat.temp_sc->pushInVec(message);
MPI_Bsend((int*) message, message.size(), MPI_INT, 0, SPLIT_TAG, MPI_COMM_WORLD);
profile_end("send split job", message.size());
if (FULL_DEBUG) fprintf(stderr, "%d: Sent %d split job to master\n", thread_no, message[0]);
}
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;
}
bool Master::createNewJob(int thread_no, bool random){
if(NULL == engine.opt_var)
return false;
int lb = engine.opt_var->getMin() + (engine.opt_type ? 1 : 0);
int ub = engine.opt_var->getMax() - (engine.opt_type ? 1 : 0);
// val must be in (lb, ub).
int val;
if(lb == ub)
val = lb;
else
val = random? (lb + (rand() % (ub - lb)))
: (lb + (ub - lb) * ((double) (thread_no+1) / (num_threads+1)));
if(PAR_DEBUG) fprintf(stderr, "Creating new job for %d, bound=%d from (%d, %d)\n", thread_no, val, lb, ub);
if(lb + 1 >= ub)
return false;
Lit l = engine.opt_type ? engine.opt_var->operator>=(val)
: engine.opt_var->operator<=(val);
vec<Lit> ls;
ls.push(l);
assert(sat.value(l) == l_Undef);
Clause * c = Clause_new(ls, false);
sat.convertToSClause(*c);
job_queue.push(sat.temp_sc->copy());
return true;
}
void Slave::exportBounds(){
int lb = engine.opt_var->getMin();
int ub = engine.opt_var->getMax();
Lit p = engine.opt_var->getLit(lb, 2);
Lit p2 = engine.opt_var->getLit(ub, 3);
vec<Lit> ps;
ps.push(p);
Clause *c = Clause_new(ps, true);
shareClause(*c);
ps.clear();
ps.push(p2);
Clause *c2 = Clause_new(ps, true);
shareClause(*c2);
unitFound=true;
free(c);
free(c2);
}
bool SimpSMTSolver::eliminateVar(Var v, bool fail)
{
if (!fail && asymm_mode && !asymmVar(v)) return false;
const vec<Clause*>& cls = getOccurs(v);
// if (value(v) != l_Undef || cls.size() == 0) return true;
if (value(v) != l_Undef) return true;
// Split the occurrences into positive and negative:
vec<Clause*> pos, neg;
for (int i = 0; i < cls.size(); i++)
(find(*cls[i], Lit(v)) ? pos : neg).push(cls[i]);
// Check if number of clauses decreases:
int cnt = 0;
for (int i = 0; i < pos.size(); i++)
for (int j = 0; j < neg.size(); j++)
if (merge(*pos[i], *neg[j], v) && ++cnt > cls.size() + grow)
return true;
#ifdef PEDANTIC_DEBUG
cerr << "XXY gonna-remove" << endl;
#endif
// Delete and store old clauses:
setDecisionVar(v, false);
elimtable[v].order = elimorder++;
assert(elimtable[v].eliminated.size() == 0);
for (int i = 0; i < cls.size(); i++)
{
elimtable[v].eliminated.push(Clause_new(*cls[i]));
removeClause(*cls[i]);
}
// Produce clauses in cross product:
int top = clauses.size();
vec<Lit> resolvent;
for (int i = 0; i < pos.size(); i++)
for (int j = 0; j < neg.size(); j++)
if (merge(*pos[i], *neg[j], v, resolvent) && !addClause(resolvent))
return false;
// DEBUG: For checking that a clause set is saturated with respect to variable elimination.
// If the clause set is expected to be saturated at this point, this constitutes an
// error.
if (fail){
reportf("eliminated var %d, %d <= %d\n", v+1, cnt, cls.size());
reportf("previous clauses:\n");
for (int i = 0; i < cls.size(); i++){
printClause(*cls[i]); reportf("\n"); }
reportf("new clauses:\n");
for (int i = top; i < clauses.size(); i++){
printClause(*clauses[i]); reportf("\n"); }
assert(0); }
return backwardSubsumptionCheck();
}
void SAT::addClause(vec<Lit>& ps, bool one_watch) {
int i, j;
for (i = j = 0; i < ps.size(); i++) {
if (value(ps[i]) == l_True) return;
if (value(ps[i]) == l_Undef) ps[j++] = ps[i];
}
ps.resize(j);
if (ps.size() == 0) {
assert(false);
TL_FAIL();
}
addClause(*Clause_new(ps), one_watch);
}
void MIP::unboundedFailure() {
// NOT_SUPPORTED;
// if (MIP_DEBUG)
// simplex.unboundedDebug();
assert(simplex.row[0] == 0);
vec<Lit> ps;
for (int i = 1; i < vars.size(); i++) {
ps.push(simplex.shift[i] == 0 ? vars[i]->getMinLit() : vars[i]->getMaxLit());
}
Clause *m_r = Clause_new(ps);
m_r->temp_expl = 1;
sat.rtrail.last().push(m_r);
sat.confl = m_r;
}
SimpSolver::SimpSolver() :
Solver()
, grow (0)
, asymm_mode (false)
, redundancy_check (false)
, merges (0)
, asymm_lits (0)
, remembered_clauses (0)
, elimorder (1)
, use_simplification (true)
, elim_heap (ElimLt(n_occ))
, bwdsub_assigns (0)
{
vec<Lit> dummy(1,lit_Undef);
bwdsub_tmpunit = Clause_new(dummy);
remove_satisfied = false;
}
bool MIP::propagateBound(int i, long double s) {
if (s > 4e9) return true;
IntView<T> v(vars[i]);
int64_t max = v.getMin() + (int64_t) floor(s);
// fprintf(stderr, "%.3Lf %lld %lld %lld\n", s, v.getMin(), v.getMax(), max);
if (v.setMaxNotR(max)) {
Clause *m_r = NULL;
if (so.lazy) {
m_r = Clause_new(ps);
(*m_r)[place[i]] = (*m_r)[0];
m_r->temp_expl = 1;
sat.rtrail.last().push(m_r);
}
if (!v.setMax(max, m_r)) return false;
}
return true;
}
// Record a clause and drive backtracking. 'clause[0]' must contain the asserting literal.
//
void Solver::record(const vec<Lit>& clause, Deriv* deriv)
{
assert(clause.size() != 0);
Clause* c;
check(Clause_new(*this, clause, true, c, 0, doDeriv, deriv));
check(ok);
if (c != NULL) {
learnts.push(c);
check(enqueue(clause[0], c));
}
else {
assert(clause.size() == 1);
if (doDeriv) {
assert(var_deriv[var(clause[0])] == deriv);
//var_deriv[var(clause[0])]->addDeriv(deriv);
}
}
}
inline bool Engine::constrain() {
best_sol = opt_var->getVal();
opt_time = wallClockTime() - start_time - init_time;
sat.btToLevel(0);
if (so.parallel) {
Lit p = opt_type ? opt_var->getLit(best_sol+1, 2) : opt_var->getLit(best_sol-1, 3);
vec<Lit> ps;
ps.push(p);
Clause *c = Clause_new(ps, true);
slave.shareClause(*c);
free(c);
}
// printf("opt_var = %d, opt_type = %d, best_sol = %d\n", opt_var->var_id, opt_type, best_sol);
// printf("%% opt_var min = %d, opt_var max = %d\n", opt_var->getMin(), opt_var->getMax());
if (so.mip) mip->setObjective(best_sol);
return (opt_type ? opt_var->setMin(best_sol+1) : opt_var->setMax(best_sol-1));
}
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);
}
}
}
/*_________________________________________________________________________________________________
|
| 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);
}
}
}
bool SimpSMTSolver::addClause( vec<Lit> & ps
#ifdef PRODUCE_PROOF
, const ipartitions_t & in
#endif
)
{
//=================================================================================================
// Added code
if ( !use_simplification )
#ifdef PRODUCE_PROOF
return CoreSMTSolver::addClause( ps, in );
#else
return CoreSMTSolver::addClause( ps );
#endif
// Added code
//=================================================================================================
for (int i = 0; i < ps.size(); i++)
if (isEliminated(var(ps[i])))
remember(var(ps[i]));
int nclauses = clauses.size();
if (redundancy_check && implied(ps))
return true;
//=================================================================================================
// Added code
//
// Hack to consider clauses of size 1 that
// wouldn't otherwise be considered by
// MiniSAT
//
if ( config.sat_preprocess_theory != 0
&& ps.size( ) == 1 // Consider unit clauses
&& var(ps[0]) >= 2 ) // Don't consider true/false
{
Var v = var( ps[0] );
Enode * e = theory_handler->varToEnode( v );
if ( e->isTAtom( ) )
{
Clause * uc = Clause_new(ps, false);
unary_to_remove.push_back( uc );
Clause &c = *(unary_to_remove.back( ));
Enode * x, * y;
getDLVars( e, sign(ps[0]), &x, &y );
assert( x->isVar( ) );
assert( y->isVar( ) );
t_pos[ x->getId( ) ].push_back( &c );
t_neg[ y->getId( ) ].push_back( &c );
t_var[ x ].insert( y->getId( ) );
t_var[ y ].insert( x->getId( ) );
}
}
// Added code
//=================================================================================================
if (!CoreSMTSolver::addClause(ps))
return false;
if (use_simplification && clauses.size() == nclauses + 1)
{
Clause& c = *clauses.last();
subsumption_queue.insert(&c);
for (int i = 0; i < c.size(); i++)
{
assert(occurs.size() > var(c[i]));
assert(!find(occurs[var(c[i])], &c));
occurs[var(c[i])].push(&c);
n_occ[toInt(c[i])]++;
touched[var(c[i])] = 1;
assert(elimtable[var(c[i])].order == 0);
if (elim_heap.inHeap(var(c[i])))
elim_heap.increase_(var(c[i]));
}
}
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();
//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
}
}
}
}
/*_________________________________________________________________________________________________
|
| newClause : (ps : const vec<Lit>&) (learnt : bool) -> [void]
|
| Description:
| Allocate and add a new clause to the SAT solvers clause database. If a conflict is detected,
| the 'ok' flag is cleared and the solver is in an unusable state (must be disposed).
|
| Input:
| ps - The new clause as a vector of literals.
| learnt - Is the clause a learnt clause? For learnt clauses, 'ps[0]' is assumed to be the
| asserting literal. An appropriate 'enqueue()' operation will be performed on this
| literal. One of the watches will always be on this literal, the other will be set to
| the literal with the highest decision level.
|
| Effect:
| Activity heuristics are updated.
|________________________________________________________________________________________________@*/
void Solver::newClause(const vec<Lit>& ps_, bool learnt)
{
if (!ok) return;
vec<Lit> qs;
if (!learnt){
assert(decisionLevel() == 0);
ps_.copyTo(qs); // Make a copy of the input vector.
// Remove duplicates:
sortUnique(qs);
// Check if clause is satisfied:
for (int i = 0; i < qs.size()-1; i++){
if (qs[i] == ~qs[i+1])
return; }
for (int i = 0; i < qs.size(); i++){
if (value(qs[i]) == l_True)
return; }
// Remove false literals:
int i, j;
for (i = j = 0; i < qs.size(); i++)
if (value(qs[i]) != l_False)
qs[j++] = qs[i];
qs.shrink(i - j);
}
const vec<Lit>& ps = learnt ? ps_ : qs; // 'ps' is now the (possibly) reduced vector of literals.
if (ps.size() == 0){
ok = false;
}else if (ps.size() == 1){
// NOTE: If enqueue takes place at root level, the assignment will be lost in incremental use (it doesn't seem to hurt much though).
if (!enqueue(ps[0]))
ok = false;
}else if (ps.size() == 2){
// Create special binary clause watch:
watches[index(~ps[0])].push(GClause_new(ps[1]));
watches[index(~ps[1])].push(GClause_new(ps[0]));
if (learnt){
check(enqueue(ps[0], GClause_new(~ps[1])));
stats.learnts_literals += ps.size();
}else
stats.clauses_literals += ps.size();
n_bin_clauses++;
}else{
// Allocate clause:
Clause* c = Clause_new(learnt, ps);
if (learnt){
// Put the second watch on the literal with highest decision level:
int max_i = 1;
int max = level[var(ps[1])];
for (int i = 2; i < ps.size(); i++)
if (level[var(ps[i])] > max)
max = level[var(ps[i])],
max_i = i;
(*c)[1] = ps[max_i];
(*c)[max_i] = ps[1];
// Bump, enqueue, store clause:
claBumpActivity(c); // (newly learnt clauses should be considered active)
check(enqueue((*c)[0], GClause_new(c)));
learnts.push(c);
stats.learnts_literals += c->size();
}else{
// Store clause:
clauses.push(c);
stats.clauses_literals += c->size();
}
// Watch clause:
watches[index(~(*c)[0])].push(GClause_new(c));
watches[index(~(*c)[1])].push(GClause_new(c));
}
}
// 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;
}
/*_________________________________________________________________________________________________
|
| 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);
}
}
}
