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bop_base.cc
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bop_base.cc
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// Copyright 2010-2021 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/bop/bop_base.h"
#include <cstdint>
#include <limits>
#include <string>
#include <vector>
#include "absl/synchronization/mutex.h"
#include "ortools/sat/boolean_problem.h"
namespace operations_research {
namespace bop {
using ::operations_research::sat::LinearBooleanProblem;
using ::operations_research::sat::LinearObjective;
BopOptimizerBase::BopOptimizerBase(const std::string& name)
: name_(name), stats_(name) {
SCOPED_TIME_STAT(&stats_);
}
BopOptimizerBase::~BopOptimizerBase() {
IF_STATS_ENABLED(VLOG(1) << stats_.StatString());
}
std::string BopOptimizerBase::GetStatusString(Status status) {
switch (status) {
case OPTIMAL_SOLUTION_FOUND:
return "OPTIMAL_SOLUTION_FOUND";
case SOLUTION_FOUND:
return "SOLUTION_FOUND";
case INFEASIBLE:
return "INFEASIBLE";
case LIMIT_REACHED:
return "LIMIT_REACHED";
case INFORMATION_FOUND:
return "INFORMATION_FOUND";
case CONTINUE:
return "CONTINUE";
case ABORT:
return "ABORT";
}
// Fallback. We don't use "default:" so the compiler will return an error
// if we forgot one enum case above.
LOG(DFATAL) << "Invalid Status " << static_cast<int>(status);
return "UNKNOWN Status";
}
//------------------------------------------------------------------------------
// ProblemState
//------------------------------------------------------------------------------
const int64_t ProblemState::kInitialStampValue(0);
ProblemState::ProblemState(const LinearBooleanProblem& problem)
: original_problem_(problem),
parameters_(),
update_stamp_(kInitialStampValue + 1),
is_fixed_(problem.num_variables(), false),
fixed_values_(problem.num_variables(), false),
lp_values_(),
solution_(problem, "AllZero"),
assignment_preference_(),
lower_bound_(std::numeric_limits<int64_t>::min()),
upper_bound_(std::numeric_limits<int64_t>::max()) {
// TODO(user): Extract to a function used by all solvers.
// Compute trivial unscaled lower bound.
const LinearObjective& objective = problem.objective();
lower_bound_ = 0;
for (int i = 0; i < objective.coefficients_size(); ++i) {
// Fix template version for or-tools.
lower_bound_ += std::min<int64_t>(int64_t{0}, objective.coefficients(i));
}
upper_bound_ = solution_.IsFeasible() ? solution_.GetCost()
: std::numeric_limits<int64_t>::max();
}
// TODO(user): refactor this to not rely on the optimization status.
// All the information can be encoded in the learned_info bounds.
bool ProblemState::MergeLearnedInfo(
const LearnedInfo& learned_info,
BopOptimizerBase::Status optimization_status) {
const std::string kIndent(25, ' ');
bool new_lp_values = false;
if (!learned_info.lp_values.empty()) {
if (lp_values_ != learned_info.lp_values) {
lp_values_ = learned_info.lp_values;
new_lp_values = true;
VLOG(1) << kIndent + "New LP values.";
}
}
bool new_binary_clauses = false;
if (!learned_info.binary_clauses.empty()) {
const int old_num = binary_clause_manager_.NumClauses();
for (sat::BinaryClause c : learned_info.binary_clauses) {
const int num_vars = original_problem_.num_variables();
if (c.a.Variable() < num_vars && c.b.Variable() < num_vars) {
binary_clause_manager_.Add(c);
}
}
if (binary_clause_manager_.NumClauses() > old_num) {
new_binary_clauses = true;
VLOG(1) << kIndent + "Num binary clauses: "
<< binary_clause_manager_.NumClauses();
}
}
bool new_solution = false;
if (learned_info.solution.IsFeasible() &&
(!solution_.IsFeasible() ||
learned_info.solution.GetCost() < solution_.GetCost())) {
solution_ = learned_info.solution;
new_solution = true;
VLOG(1) << kIndent + "New solution.";
}
bool new_lower_bound = false;
if (learned_info.lower_bound > lower_bound()) {
lower_bound_ = learned_info.lower_bound;
new_lower_bound = true;
VLOG(1) << kIndent + "New lower bound.";
}
if (solution_.IsFeasible()) {
upper_bound_ = std::min(upper_bound(), solution_.GetCost());
if (upper_bound() <= lower_bound() ||
(upper_bound() - lower_bound() <=
parameters_.relative_gap_limit() *
std::max(std::abs(upper_bound()), std::abs(lower_bound())))) {
// The lower bound might be greater that the cost of a feasible solution
// due to rounding errors in the problem scaling and Glop.
// As a feasible solution was found, the solution is proved optimal.
MarkAsOptimal();
}
}
// Merge fixed variables. Note that variables added during search, i.e. not
// in the original problem, are ignored.
int num_newly_fixed_variables = 0;
for (const sat::Literal literal : learned_info.fixed_literals) {
const VariableIndex var(literal.Variable().value());
if (var >= original_problem_.num_variables()) {
continue;
}
const bool value = literal.IsPositive();
if (is_fixed_[var]) {
if (fixed_values_[var] != value) {
MarkAsInfeasible();
return true;
}
} else {
is_fixed_[var] = true;
fixed_values_[var] = value;
++num_newly_fixed_variables;
}
}
if (num_newly_fixed_variables > 0) {
int num_fixed_variables = 0;
for (const bool is_fixed : is_fixed_) {
if (is_fixed) {
++num_fixed_variables;
}
}
VLOG(1) << kIndent << num_newly_fixed_variables
<< " newly fixed variables (" << num_fixed_variables << " / "
<< is_fixed_.size() << ").";
if (num_fixed_variables == is_fixed_.size()) {
// Set the solution to the fixed variables.
BopSolution fixed_solution = solution_;
for (VariableIndex var(0); var < is_fixed_.size(); ++var) {
fixed_solution.SetValue(var, fixed_values_[var]);
}
if (fixed_solution.IsFeasible()) {
solution_ = fixed_solution;
}
if (solution_.IsFeasible()) {
MarkAsOptimal();
VLOG(1) << kIndent << "Optimal";
} else {
MarkAsInfeasible();
}
}
}
bool known_status = false;
if (optimization_status == BopOptimizerBase::OPTIMAL_SOLUTION_FOUND) {
MarkAsOptimal();
known_status = true;
} else if (optimization_status == BopOptimizerBase::INFEASIBLE) {
MarkAsInfeasible();
known_status = true;
}
const bool updated = new_lp_values || new_binary_clauses || new_solution ||
new_lower_bound || num_newly_fixed_variables > 0 ||
known_status;
if (updated) ++update_stamp_;
return updated;
}
LearnedInfo ProblemState::GetLearnedInfo() const {
LearnedInfo learned_info(original_problem_);
for (VariableIndex var(0); var < is_fixed_.size(); ++var) {
if (is_fixed_[var]) {
learned_info.fixed_literals.push_back(
sat::Literal(sat::BooleanVariable(var.value()), fixed_values_[var]));
}
}
learned_info.solution = solution_;
learned_info.lower_bound = lower_bound();
learned_info.lp_values = lp_values_;
learned_info.binary_clauses = NewlyAddedBinaryClauses();
return learned_info;
}
void ProblemState::MarkAsOptimal() {
CHECK(solution_.IsFeasible());
lower_bound_ = upper_bound();
++update_stamp_;
}
void ProblemState::MarkAsInfeasible() {
// Mark as infeasible, i.e. set a lower_bound greater than the upper_bound.
CHECK(!solution_.IsFeasible());
if (upper_bound() == std::numeric_limits<int64_t>::max()) {
lower_bound_ = std::numeric_limits<int64_t>::max();
upper_bound_ = std::numeric_limits<int64_t>::max() - 1;
} else {
lower_bound_ = upper_bound_ - 1;
}
++update_stamp_;
}
const std::vector<sat::BinaryClause>& ProblemState::NewlyAddedBinaryClauses()
const {
return binary_clause_manager_.newly_added();
}
void ProblemState::SynchronizationDone() {
binary_clause_manager_.ClearNewlyAdded();
}
} // namespace bop
} // namespace operations_research