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probing.cc
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// Copyright 2010-2018 Google LLC
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "ortools/sat/probing.h"
#include <set>
#include "ortools/base/iterator_adaptors.h"
#include "ortools/base/timer.h"
#include "ortools/sat/clause.h"
#include "ortools/sat/implied_bounds.h"
#include "ortools/sat/integer.h"
#include "ortools/sat/sat_base.h"
#include "ortools/sat/sat_solver.h"
#include "ortools/sat/util.h"
namespace operations_research {
namespace sat {
bool ProbeBooleanVariables(const double deterministic_time_limit, Model* model,
bool log_info) {
auto* sat_solver = model->GetOrCreate<SatSolver>();
const int num_variables = sat_solver->NumVariables();
auto* implication_graph = model->GetOrCreate<BinaryImplicationGraph>();
std::vector<BooleanVariable> bool_vars;
for (BooleanVariable b(0); b < num_variables; ++b) {
const Literal literal(b, true);
if (implication_graph->RepresentativeOf(literal) != literal) {
continue;
}
bool_vars.push_back(b);
}
return ProbeBooleanVariables(deterministic_time_limit, bool_vars, model,
log_info);
}
bool ProbeBooleanVariables(const double deterministic_time_limit,
absl::Span<const BooleanVariable> bool_vars,
Model* model, bool log_info) {
log_info |= VLOG_IS_ON(1);
WallTimer wall_timer;
wall_timer.Start();
// Reset the solver in case it was already used.
auto* sat_solver = model->GetOrCreate<SatSolver>();
sat_solver->SetAssumptionLevel(0);
if (!sat_solver->RestoreSolverToAssumptionLevel()) return false;
auto* time_limit = model->GetOrCreate<TimeLimit>();
const auto& assignment = sat_solver->LiteralTrail().Assignment();
const int initial_num_fixed = sat_solver->LiteralTrail().Index();
const double initial_deterministic_time =
time_limit->GetElapsedDeterministicTime();
const double limit = initial_deterministic_time + deterministic_time_limit;
// For the new direct implication detected.
int64 num_new_binary = 0;
std::vector<std::pair<Literal, Literal>> new_binary_clauses;
auto* implication_graph = model->GetOrCreate<BinaryImplicationGraph>();
const int id = implication_graph->PropagatorId();
// This is used to tighten the integer variable bounds.
int num_new_holes = 0;
int num_new_integer_bounds = 0;
auto* integer_trail = model->Mutable<IntegerTrail>();
ImpliedBounds* implied_bounds = nullptr;
if (integer_trail != nullptr) {
implied_bounds = model->GetOrCreate<ImpliedBounds>();
}
std::vector<IntegerLiteral> new_integer_bounds;
// To detect literal x that must be true because b => x and not(b) => x.
// When probing b, we add all propagated literal to propagated, and when
// probing not(b) we check if any are already there.
std::vector<Literal> to_fix_at_true;
const int num_variables = sat_solver->NumVariables();
SparseBitset<LiteralIndex> propagated(LiteralIndex(2 * num_variables));
bool limit_reached = false;
int num_probed = 0;
const auto& trail = *(model->Get<Trail>());
for (const BooleanVariable b : bool_vars) {
const Literal literal(b, true);
if (implication_graph->RepresentativeOf(literal) != literal) {
continue;
}
// TODO(user): Instead of an hard deterministic limit, we should probably
// use a lower one, but reset it each time we have found something useful.
if (time_limit->LimitReached() ||
time_limit->GetElapsedDeterministicTime() > limit) {
limit_reached = true;
break;
}
// Propagate b=1 and then b=0.
++num_probed;
new_integer_bounds.clear();
propagated.SparseClearAll();
for (const Literal decision : {Literal(b, true), Literal(b, false)}) {
if (assignment.LiteralIsAssigned(decision)) continue;
CHECK_EQ(sat_solver->CurrentDecisionLevel(), 0);
const int saved_index = trail.Index();
sat_solver->EnqueueDecisionAndBackjumpOnConflict(decision);
sat_solver->AdvanceDeterministicTime(time_limit);
if (sat_solver->IsModelUnsat()) return false;
if (sat_solver->CurrentDecisionLevel() == 0) continue;
if (integer_trail != nullptr) {
implied_bounds->ProcessIntegerTrail(decision);
integer_trail->AppendNewBounds(&new_integer_bounds);
}
for (int i = saved_index + 1; i < trail.Index(); ++i) {
const Literal l = trail[i];
// We mark on the first run (b.IsPositive()) and check on the second.
if (decision.IsPositive()) {
propagated.Set(l.Index());
} else {
if (propagated[l.Index()]) {
to_fix_at_true.push_back(l);
}
}
// Anything not propagated by the BinaryImplicationGraph is a "new"
// binary clause. This is becaue the BinaryImplicationGraph has the
// highest priority of all propagators.
if (trail.AssignmentType(l.Variable()) != id) {
new_binary_clauses.push_back({decision.Negated(), l});
}
}
// Fix variable and add new binary clauses.
if (!sat_solver->RestoreSolverToAssumptionLevel()) return false;
for (const Literal l : to_fix_at_true) {
sat_solver->AddUnitClause(l);
}
to_fix_at_true.clear();
if (!sat_solver->FinishPropagation()) return false;
num_new_binary += new_binary_clauses.size();
for (auto binary : new_binary_clauses) {
sat_solver->AddBinaryClause(binary.first, binary.second);
}
new_binary_clauses.clear();
if (!sat_solver->FinishPropagation()) return false;
}
// We have at most two lower bounds for each variables (one for b==0 and one
// for b==1), so the min of the two is a valid level zero bound! More
// generally, the domain of a variable can be intersected with the union
// of the two propagated domains. This also allow to detect "holes".
//
// TODO(user): More generally, for any clauses (b or not(b) is one), we
// could probe all the literal inside, and for any integer variable, we can
// take the union of the propagated domain as a new domain.
//
// TODO(user): fix binary variable in the same way? It might not be as
// useful since probing on such variable will also fix it. But then we might
// abort probing early, so it might still be good.
std::sort(new_integer_bounds.begin(), new_integer_bounds.end(),
[](IntegerLiteral a, IntegerLiteral b) { return a.var < b.var; });
// This is used for the hole detection.
IntegerVariable prev_var = kNoIntegerVariable;
IntegerValue lb_max = kMinIntegerValue;
IntegerValue ub_min = kMaxIntegerValue;
new_integer_bounds.push_back(IntegerLiteral()); // Sentinel.
for (int i = 0; i < new_integer_bounds.size(); ++i) {
const IntegerVariable var = new_integer_bounds[i].var;
// Hole detection.
if (i > 0 && PositiveVariable(var) != prev_var) {
if (ub_min + 1 < lb_max) {
// The variable cannot take value in (ub_min, lb_max) !
//
// TODO(user): do not create domain with a complexity that is too
// large?
const Domain old_domain =
integer_trail->InitialVariableDomain(prev_var);
const Domain new_domain = old_domain.IntersectionWith(
Domain(ub_min.value() + 1, lb_max.value() - 1).Complement());
if (new_domain != old_domain) {
++num_new_holes;
if (!integer_trail->UpdateInitialDomain(prev_var, new_domain)) {
return false;
}
}
}
// Reinitialize.
lb_max = kMinIntegerValue;
ub_min = kMaxIntegerValue;
}
prev_var = PositiveVariable(var);
if (VariableIsPositive(var)) {
lb_max = std::max(lb_max, new_integer_bounds[i].bound);
} else {
ub_min = std::min(ub_min, -new_integer_bounds[i].bound);
}
// Bound tightening.
if (i == 0 || new_integer_bounds[i - 1].var != var) continue;
const IntegerValue new_bound = std::min(new_integer_bounds[i - 1].bound,
new_integer_bounds[i].bound);
if (new_bound > integer_trail->LowerBound(var)) {
++num_new_integer_bounds;
if (!integer_trail->Enqueue(
IntegerLiteral::GreaterOrEqual(var, new_bound), {}, {})) {
return false;
}
}
}
// We might have updates some integer domain, lets propagate.
if (!sat_solver->FinishPropagation()) return false;
}
// Display stats.
if (log_info) {
const double time_diff =
time_limit->GetElapsedDeterministicTime() - initial_deterministic_time;
const int num_fixed = sat_solver->LiteralTrail().Index();
const int num_newly_fixed = num_fixed - initial_num_fixed;
LOG(INFO) << "Probing deterministic_time: " << time_diff
<< " (limit: " << deterministic_time_limit
<< ") wall_time: " << wall_timer.Get() << " ("
<< (limit_reached ? "Aborted " : "") << num_probed << "/"
<< num_variables << ")";
LOG_IF(INFO, num_newly_fixed > 0)
<< "Probing new fixed Boolean: " << num_newly_fixed << " (" << num_fixed
<< "/" << num_variables << ")";
LOG_IF(INFO, num_new_holes > 0)
<< "Probing new integer holes: " << num_new_holes;
LOG_IF(INFO, num_new_integer_bounds > 0)
<< "Probing new integer bounds: " << num_new_integer_bounds;
LOG_IF(INFO, num_new_binary > 0)
<< "Probing new binary clause: " << num_new_binary;
}
return true;
}
bool LookForTrivialSatSolution(double deterministic_time_limit, Model* model,
bool log_info) {
log_info |= VLOG_IS_ON(1);
WallTimer wall_timer;
wall_timer.Start();
// Reset the solver in case it was already used.
auto* sat_solver = model->GetOrCreate<SatSolver>();
sat_solver->SetAssumptionLevel(0);
if (!sat_solver->RestoreSolverToAssumptionLevel()) return false;
auto* time_limit = model->GetOrCreate<TimeLimit>();
const int initial_num_fixed = sat_solver->LiteralTrail().Index();
// Note that this code do not care about the non-Boolean part and just try to
// assign the existing Booleans.
SatParameters initial_params = *model->GetOrCreate<SatParameters>();
SatParameters new_params = initial_params;
new_params.set_log_search_progress(false);
new_params.set_max_number_of_conflicts(1);
new_params.set_max_deterministic_time(deterministic_time_limit);
double elapsed_dtime = 0.0;
const int num_times = 1000;
bool limit_reached = false;
auto* random = model->GetOrCreate<ModelRandomGenerator>();
for (int i = 0; i < num_times; ++i) {
if (time_limit->LimitReached() ||
elapsed_dtime > deterministic_time_limit) {
limit_reached = true;
break;
}
// SetParameters() reset the deterministic time to zero inside time_limit.
sat_solver->SetParameters(new_params);
sat_solver->ResetDecisionHeuristic();
const SatSolver::Status result = sat_solver->SolveWithTimeLimit(time_limit);
elapsed_dtime += time_limit->GetElapsedDeterministicTime();
if (result == SatSolver::FEASIBLE) {
LOG_IF(INFO, log_info) << "Trivial exploration found feasible solution!";
time_limit->AdvanceDeterministicTime(elapsed_dtime);
return true;
}
if (!sat_solver->RestoreSolverToAssumptionLevel()) {
LOG_IF(INFO, log_info) << "UNSAT during trivial exploration heuristic.";
time_limit->AdvanceDeterministicTime(elapsed_dtime);
return false;
}
// We randomize at the end so that the default params is executed
// at least once.
RandomizeDecisionHeuristic(random, &new_params);
new_params.set_random_seed(i);
new_params.set_max_deterministic_time(deterministic_time_limit -
elapsed_dtime);
}
// Restore the initial parameters.
sat_solver->SetParameters(initial_params);
sat_solver->ResetDecisionHeuristic();
time_limit->AdvanceDeterministicTime(elapsed_dtime);
if (!sat_solver->RestoreSolverToAssumptionLevel()) return false;
if (log_info) {
const int num_fixed = sat_solver->LiteralTrail().Index();
const int num_newly_fixed = num_fixed - initial_num_fixed;
const int num_variables = sat_solver->NumVariables();
LOG(INFO) << "Random exploration."
<< " num_fixed: +" << num_newly_fixed << " (" << num_fixed << "/"
<< num_variables << ")"
<< " dtime: " << elapsed_dtime << "/" << deterministic_time_limit
<< " wtime: " << wall_timer.Get()
<< (limit_reached ? " (Aborted)" : "");
}
return sat_solver->FinishPropagation();
}
bool FailedLiteralProbingRound(ProbingOptions options, Model* model) {
WallTimer wall_timer;
wall_timer.Start();
options.log_info |= VLOG_IS_ON(1);
// Reset the solver in case it was already used.
auto* sat_solver = model->GetOrCreate<SatSolver>();
sat_solver->SetAssumptionLevel(0);
if (!sat_solver->RestoreSolverToAssumptionLevel()) return false;
// When called from Inprocessing, the implication graph should already be a
// DAG, so these two calls should return right away. But we do need them to
// get the topological order if this is used in isolation.
auto* implication_graph = model->GetOrCreate<BinaryImplicationGraph>();
if (!implication_graph->DetectEquivalences()) return false;
if (!sat_solver->FinishPropagation()) return false;
auto* time_limit = model->GetOrCreate<TimeLimit>();
const int initial_num_fixed = sat_solver->LiteralTrail().Index();
const double initial_deterministic_time =
time_limit->GetElapsedDeterministicTime();
const double limit = initial_deterministic_time + options.deterministic_limit;
const int num_variables = sat_solver->NumVariables();
SparseBitset<LiteralIndex> processed(LiteralIndex(2 * num_variables));
int64 num_probed = 0;
int64 num_explicit_fix = 0;
int64 num_conflicts = 0;
int64 num_new_binary = 0;
int64 num_subsumed = 0;
const auto& trail = *(model->Get<Trail>());
const auto& assignment = trail.Assignment();
auto* clause_manager = model->GetOrCreate<LiteralWatchers>();
const int id = implication_graph->PropagatorId();
const int clause_id = clause_manager->PropagatorId();
// This is only needed when options.use_queue is true.
struct SavedNextLiteral {
LiteralIndex literal_index; // kNoLiteralIndex if we need to backtrack.
int rank; // Cached position_in_order, we prefer lower positions.
bool operator<(const SavedNextLiteral& o) const { return rank < o.rank; }
};
std::vector<SavedNextLiteral> queue;
gtl::ITIVector<LiteralIndex, int> position_in_order;
// This is only needed when options use_queue is false;
gtl::ITIVector<LiteralIndex, int> starts;
if (!options.use_queue) starts.resize(2 * num_variables, 0);
// We delay fixing of already assigned literal once we go back to level
// zero.
std::vector<Literal> to_fix;
// Depending on the options. we do not use the same order.
// With tree look, it is better to start with "leaf" first since we try
// to reuse propagation as much as possible. This is also interesting to
// do when extracting binary clauses since we will need to propagate
// everyone anyway, and this should result in less clauses that can be
// removed later by transitive reduction.
//
// However, without tree-look and without the need to extract all binary
// clauses, it is better to just probe the root of the binary implication
// graph. This is exactly what happen when we probe using the topological
// order.
int order_index(0);
std::vector<LiteralIndex> probing_order =
implication_graph->ReverseTopologicalOrder();
if (!options.use_tree_look && !options.extract_binary_clauses) {
std::reverse(probing_order.begin(), probing_order.end());
}
// We only use this for the queue version.
if (options.use_queue) {
position_in_order.assign(2 * num_variables, -1);
for (int i = 0; i < probing_order.size(); ++i) {
position_in_order[probing_order[i]] = i;
}
}
while (!time_limit->LimitReached() &&
time_limit->GetElapsedDeterministicTime() <= limit) {
// We only enqueue literal at level zero if we don't use "tree look".
if (!options.use_tree_look) sat_solver->Backtrack(0);
LiteralIndex next_decision = kNoLiteralIndex;
if (options.use_queue && sat_solver->CurrentDecisionLevel() > 0) {
// TODO(user): Instead of minimizing index in topo order (which might be
// nice for binary extraction), we could try to maximize reusability in
// some way.
const Literal prev_decision =
sat_solver->Decisions()[sat_solver->CurrentDecisionLevel() - 1]
.literal;
const auto& list =
implication_graph->Implications(prev_decision.Negated());
const int saved_queue_size = queue.size();
for (const Literal l : list) {
const Literal candidate = l.Negated();
if (processed[candidate.Index()]) continue;
if (position_in_order[candidate.Index()] == -1) continue;
if (assignment.LiteralIsAssigned(candidate)) {
if (assignment.LiteralIsFalse(candidate)) {
to_fix.push_back(Literal(candidate.Negated()));
}
continue;
}
queue.push_back(
{candidate.Index(), -position_in_order[candidate.Index()]});
}
std::sort(queue.begin() + saved_queue_size, queue.end());
// Probe a literal that implies previous decision.
while (!queue.empty()) {
const LiteralIndex index = queue.back().literal_index;
queue.pop_back();
if (index == kNoLiteralIndex) {
// This is a backtrack marker, go back one level.
CHECK_GT(sat_solver->CurrentDecisionLevel(), 0);
sat_solver->Backtrack(sat_solver->CurrentDecisionLevel() - 1);
continue;
}
const Literal candidate(index);
if (processed[candidate.Index()]) continue;
if (assignment.LiteralIsAssigned(candidate)) {
if (assignment.LiteralIsFalse(candidate)) {
to_fix.push_back(Literal(candidate.Negated()));
}
continue;
}
next_decision = candidate.Index();
break;
}
}
if (sat_solver->CurrentDecisionLevel() == 0) {
// Fix any delayed fixed literal.
for (const Literal literal : to_fix) {
if (!assignment.LiteralIsTrue(literal)) {
++num_explicit_fix;
sat_solver->AddUnitClause(literal);
}
}
to_fix.clear();
if (!sat_solver->FinishPropagation()) return false;
// Probe an unexplored node.
for (; order_index < probing_order.size(); ++order_index) {
const Literal candidate(probing_order[order_index]);
if (processed[candidate.Index()]) continue;
if (assignment.LiteralIsAssigned(candidate)) continue;
next_decision = candidate.Index();
break;
}
// The pass is finished.
if (next_decision == kNoLiteralIndex) break;
} else if (next_decision == kNoLiteralIndex) {
const int level = sat_solver->CurrentDecisionLevel();
const Literal prev_decision = sat_solver->Decisions()[level - 1].literal;
const auto& list =
implication_graph->Implications(prev_decision.Negated());
// Probe a literal that implies previous decision.
//
// Note that contrary to the queue based implementation, this do not
// process them in a particular order.
int j = starts[prev_decision.NegatedIndex()];
for (int i = 0; i < list.size(); ++i, ++j) {
j %= list.size();
const Literal candidate = Literal(list[j]).Negated();
if (processed[candidate.Index()]) continue;
if (assignment.LiteralIsFalse(candidate)) {
// candidate => previous => not(candidate), so we can fix it.
to_fix.push_back(Literal(candidate.Negated()));
continue;
}
// This shouldn't happen if extract_binary_clauses is false.
// We have an equivalence.
if (assignment.LiteralIsTrue(candidate)) continue;
next_decision = candidate.Index();
break;
}
starts[prev_decision.NegatedIndex()] = j;
if (next_decision == kNoLiteralIndex) {
sat_solver->Backtrack(level - 1);
continue;
}
}
++num_probed;
processed.Set(next_decision);
CHECK_NE(next_decision, kNoLiteralIndex);
queue.push_back({kNoLiteralIndex, 0}); // Backtrack marker.
const int level = sat_solver->CurrentDecisionLevel();
const int first_new_trail_index =
sat_solver->EnqueueDecisionAndBackjumpOnConflict(
Literal(next_decision));
const int new_level = sat_solver->CurrentDecisionLevel();
sat_solver->AdvanceDeterministicTime(time_limit);
if (sat_solver->IsModelUnsat()) return false;
if (new_level <= level) {
++num_conflicts;
// Sync the queue with the new level.
if (options.use_queue) {
if (new_level == 0) {
queue.clear();
} else {
int queue_level = level + 1;
while (queue_level > new_level) {
CHECK(!queue.empty());
if (queue.back().literal_index == kNoLiteralIndex) --queue_level;
queue.pop_back();
}
}
}
// Fix next_decision to false if not already done.
//
// Even if we fixed something at evel zero, next_decision might not be
// fixed! But we can fix it. It can happen because when we propagate
// with clauses, we might have a => b but not not(b) => not(a). Like a
// => b and clause (not(a), not(b), c), propagating a will set c, but
// propagating not(c) will not do anything.
//
// We "delay" the fixing if we are not at level zero so that we can
// still reuse the current propagation work via tree look.
//
// TODO(user): Can we be smarter here? Maybe we can still fix the
// literal without going back to level zero by simply enqueing it with
// no reason? it will be bactracked over, but we will still lazily fix
// it later.
if (sat_solver->CurrentDecisionLevel() != 0 ||
assignment.LiteralIsFalse(Literal(next_decision))) {
to_fix.push_back(Literal(next_decision).Negated());
}
}
// Inspect the newly propagated literals. Depending on the options, try to
// extract binary clauses via hyper binary resolution and/or mark the
// literals on the trail so that they do not need to be probed later.
if (new_level == 0) continue;
const Literal last_decision =
sat_solver->Decisions()[new_level - 1].literal;
int num_new_subsumed = 0;
for (int i = first_new_trail_index; i < trail.Index(); ++i) {
const Literal l = trail[i];
if (l == last_decision) continue;
// If we can extract a binary clause that subsume the reason clause, we
// do add the binary and remove the subsumed clause.
//
// TODO(user): We could be slightly more generic and subsume some
// clauses that do not contains last_decision.Negated().
bool subsumed = false;
if (options.subsume_with_binary_clause &&
trail.AssignmentType(l.Variable()) == clause_id) {
for (const Literal lit : trail.Reason(l.Variable())) {
if (lit == last_decision.Negated()) {
subsumed = true;
break;
}
}
if (subsumed) {
++num_new_subsumed;
++num_new_binary;
implication_graph->AddBinaryClause(last_decision.Negated(), l);
const int trail_index = trail.Info(l.Variable()).trail_index;
int test = 0;
for (const Literal lit :
clause_manager->ReasonClause(trail_index)->AsSpan()) {
if (lit == l) ++test;
if (lit == last_decision.Negated()) ++test;
}
CHECK_EQ(test, 2);
clause_manager->LazyDetach(clause_manager->ReasonClause(trail_index));
// We need to change the reason now that the clause is cleared.
implication_graph->ChangeReason(trail_index, last_decision);
}
}
if (options.extract_binary_clauses) {
// Anything not propagated by the BinaryImplicationGraph is a "new"
// binary clause. This is because the BinaryImplicationGraph has the
// highest priority of all propagators.
//
// Note(user): This is not 100% true, since when we launch the clause
// propagation for one literal we do finish it before calling again
// the binary propagation.
//
// TODO(user): Think about trying to extract clause that will not
// get removed by transitive reduction later. If we can both extract
// a => c and b => c , ideally we don't want to extract a => c first
// if we already know that a => b.
//
// TODO(user): Similar to previous point, we could find the LCA
// of all literals in the reason for this propagation. And use this
// as a reason for later hyber binary resolution. Like we do when
// this clause subsume the reason.
if (!subsumed && trail.AssignmentType(l.Variable()) != id) {
++num_new_binary;
implication_graph->AddBinaryClause(last_decision.Negated(), l);
}
} else {
// If we don't extract binary, we don't need to explore any of
// these literal until more variables are fixed.
processed.Set(l.Index());
}
}
// Inspect the watcher list for last_decision, If we have a blocking
// literal at true (implied by last decision), then we have subsumptions.
//
// The intuition behind this is that if a binary clause (a,b) subsume a
// clause, and we watch a.Negated() for this clause with a blocking
// literal b, then this watch entry will never change because we always
// propagate binary clauses first and the blocking literal will always be
// true. So after many propagations, we hope to have such configuration
// which is quite cheap to test here.
if (options.subsume_with_binary_clause) {
for (const auto& w :
clause_manager->WatcherListOnFalse(last_decision.Negated())) {
if (assignment.LiteralIsTrue(w.blocking_literal)) {
if (w.clause->empty()) continue;
CHECK_NE(w.blocking_literal, last_decision.Negated());
// Add the binary clause if needed. Note that we change the reason
// to a binary one so that we never add the same clause twice.
//
// Tricky: while last_decision would be a valid reason, we need a
// reason that was assigned before this literal, so we use the
// decision at the level where this literal was assigne which is an
// even better reasony. Maybe it is just better to change all the
// reason above to a binary one so we don't have an issue here.
if (trail.AssignmentType(w.blocking_literal.Variable()) != id) {
++num_new_binary;
implication_graph->AddBinaryClause(last_decision.Negated(),
w.blocking_literal);
const auto& info = trail.Info(w.blocking_literal.Variable());
if (info.level > 0) {
const Literal d = sat_solver->Decisions()[info.level - 1].literal;
if (d != w.blocking_literal) {
implication_graph->ChangeReason(info.trail_index, d);
}
}
}
++num_new_subsumed;
clause_manager->LazyDetach(w.clause);
}
}
}
if (num_new_subsumed > 0) {
// TODO(user): We might just want to do that even more lazily by
// checking for detached clause while propagating here? and do a big
// cleanup at the end.
clause_manager->CleanUpWatchers();
num_subsumed += num_new_subsumed;
}
}
if (!sat_solver->ResetToLevelZero()) return false;
for (const Literal literal : to_fix) {
++num_explicit_fix;
sat_solver->AddUnitClause(literal);
}
to_fix.clear();
if (!sat_solver->FinishPropagation()) return false;
// Display stats.
const int num_fixed = sat_solver->LiteralTrail().Index();
const int num_newly_fixed = num_fixed - initial_num_fixed;
const double time_diff =
time_limit->GetElapsedDeterministicTime() - initial_deterministic_time;
const bool limit_reached = time_limit->LimitReached() ||
time_limit->GetElapsedDeterministicTime() > limit;
LOG_IF(INFO, options.log_info)
<< "Probing. "
<< " num_probed: " << num_probed << " num_fixed: +" << num_newly_fixed
<< " (" << num_fixed << "/" << num_variables << ")"
<< " explicit_fix:" << num_explicit_fix
<< " num_conflicts:" << num_conflicts
<< " new_binary_clauses: " << num_new_binary
<< " subsumed: " << num_subsumed << " dtime: " << time_diff
<< " wtime: " << wall_timer.Get() << (limit_reached ? " (Aborted)" : "");
return sat_solver->FinishPropagation();
}
} // namespace sat
} // namespace operations_research