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ortools-clone/ortools/sat/cp_model_search.cc

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// Copyright 2010-2025 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/cp_model_search.h"
#include <algorithm>
#include <cstddef>
#include <cstdint>
#include <functional>
#include <limits>
#include <string>
#include <utility>
#include <vector>
#include "absl/algorithm/container.h"
#include "absl/container/flat_hash_map.h"
#include "absl/container/flat_hash_set.h"
#include "absl/log/check.h"
#include "absl/random/distributions.h"
#include "absl/strings/str_cat.h"
#include "absl/strings/string_view.h"
#include "absl/types/span.h"
#include "ortools/base/logging.h"
#include "ortools/sat/cp_model.pb.h"
#include "ortools/sat/cp_model_mapping.h"
#include "ortools/sat/cp_model_utils.h"
#include "ortools/sat/integer.h"
#include "ortools/sat/integer_base.h"
#include "ortools/sat/integer_search.h"
#include "ortools/sat/linear_propagation.h"
#include "ortools/sat/model.h"
#include "ortools/sat/sat_base.h"
#include "ortools/sat/sat_parameters.pb.h"
#include "ortools/sat/util.h"
#include "ortools/util/strong_integers.h"
namespace operations_research {
namespace sat {
CpModelView::CpModelView(Model* model)
: mapping_(*model->GetOrCreate<CpModelMapping>()),
boolean_assignment_(model->GetOrCreate<Trail>()->Assignment()),
integer_trail_(*model->GetOrCreate<IntegerTrail>()),
integer_encoder_(*model->GetOrCreate<IntegerEncoder>()),
random_(*model->GetOrCreate<ModelRandomGenerator>()) {}
int CpModelView::NumVariables() const { return mapping_.NumProtoVariables(); }
bool CpModelView::IsFixed(int var) const {
if (mapping_.IsBoolean(var)) {
return boolean_assignment_.VariableIsAssigned(
mapping_.Literal(var).Variable());
} else if (mapping_.IsInteger(var)) {
return integer_trail_.IsFixed(mapping_.Integer(var));
}
return true; // Default.
}
int64_t CpModelView::Min(int var) const {
if (mapping_.IsBoolean(var)) {
const Literal l = mapping_.Literal(var);
return boolean_assignment_.LiteralIsTrue(l) ? 1 : 0;
} else if (mapping_.IsInteger(var)) {
return integer_trail_.LowerBound(mapping_.Integer(var)).value();
}
return 0; // Default.
}
int64_t CpModelView::Max(int var) const {
if (mapping_.IsBoolean(var)) {
const Literal l = mapping_.Literal(var);
return boolean_assignment_.LiteralIsFalse(l) ? 0 : 1;
} else if (mapping_.IsInteger(var)) {
return integer_trail_.UpperBound(mapping_.Integer(var)).value();
}
return 0; // Default.
}
BooleanOrIntegerLiteral CpModelView::GreaterOrEqual(int var,
int64_t value) const {
DCHECK(!IsFixed(var));
BooleanOrIntegerLiteral result;
if (mapping_.IsBoolean(var)) {
DCHECK(value == 0 || value == 1);
if (value == 1) {
result.boolean_literal_index = mapping_.Literal(var).Index();
}
} else if (mapping_.IsInteger(var)) {
result.integer_literal = IntegerLiteral::GreaterOrEqual(
mapping_.Integer(var), IntegerValue(value));
}
return result;
}
BooleanOrIntegerLiteral CpModelView::LowerOrEqual(int var,
int64_t value) const {
DCHECK(!IsFixed(var));
BooleanOrIntegerLiteral result;
if (mapping_.IsBoolean(var)) {
DCHECK(value == 0 || value == 1);
if (value == 0) {
result.boolean_literal_index = mapping_.Literal(var).NegatedIndex();
}
} else if (mapping_.IsInteger(var)) {
result.integer_literal = IntegerLiteral::LowerOrEqual(mapping_.Integer(var),
IntegerValue(value));
}
return result;
}
BooleanOrIntegerLiteral CpModelView::MedianValue(int var) const {
DCHECK(!IsFixed(var));
BooleanOrIntegerLiteral result;
if (mapping_.IsBoolean(var)) {
result.boolean_literal_index = mapping_.Literal(var).NegatedIndex();
} else if (mapping_.IsInteger(var)) {
const IntegerVariable variable = mapping_.Integer(var);
const std::vector<ValueLiteralPair> encoding =
integer_encoder_.FullDomainEncoding(variable);
// 5 values -> returns the second.
// 4 values -> returns the second too.
// Array is 0 based.
const int target = (static_cast<int>(encoding.size()) + 1) / 2 - 1;
result.boolean_literal_index = encoding[target].literal.Index();
}
return result;
}
BooleanOrIntegerLiteral CpModelView::RandomSplit(int var, int64_t lb,
int64_t ub) const {
DCHECK(!IsFixed(var));
BooleanOrIntegerLiteral result;
if (mapping_.IsBoolean(var)) {
if (absl::Bernoulli(random_, 0.5)) {
result.boolean_literal_index = mapping_.Literal(var).Index();
} else {
result.boolean_literal_index = mapping_.Literal(var).NegatedIndex();
}
} else if (mapping_.IsInteger(var)) {
if (absl::Bernoulli(random_, 0.5)) {
result.integer_literal = IntegerLiteral::LowerOrEqual(
mapping_.Integer(var), IntegerValue(lb + (ub - lb) / 2));
} else {
result.integer_literal = IntegerLiteral::GreaterOrEqual(
mapping_.Integer(var), IntegerValue(ub - (ub - lb) / 2));
}
}
return result;
}
// Stores one variable and its strategy value.
struct VarValue {
int ref;
int64_t value;
};
namespace {
// TODO(user): Save this somewhere instead of recomputing it.
bool ModelHasSchedulingConstraints(const CpModelProto& cp_model_proto) {
for (const ConstraintProto& ct : cp_model_proto.constraints()) {
if (ct.constraint_case() == ConstraintProto::kNoOverlap) return true;
if (ct.constraint_case() == ConstraintProto::kCumulative) return true;
}
return false;
}
void AddExtraSchedulingPropagators(SatParameters& new_params) {
new_params.set_exploit_all_precedences(true);
new_params.set_use_hard_precedences_in_cumulative(true);
new_params.set_use_overload_checker_in_cumulative(true);
new_params.set_use_strong_propagation_in_disjunctive(true);
new_params.set_use_timetable_edge_finding_in_cumulative(true);
new_params.set_use_conservative_scale_overload_checker(true);
new_params.set_max_pairs_pairwise_reasoning_in_no_overlap_2d(5000);
new_params.set_use_timetabling_in_no_overlap_2d(true);
new_params.set_use_energetic_reasoning_in_no_overlap_2d(true);
new_params.set_use_area_energetic_reasoning_in_no_overlap_2d(true);
new_params.set_use_try_edge_reasoning_in_no_overlap_2d(true);
new_params.set_no_overlap_2d_boolean_relations_limit(100);
}
// We want a random tie breaking among variables with equivalent values.
struct NoisyInteger {
int64_t value;
double noise;
bool operator<=(const NoisyInteger& other) const {
return value < other.value ||
(value == other.value && noise <= other.noise);
}
bool operator>(const NoisyInteger& other) const {
return value > other.value || (value == other.value && noise > other.noise);
}
};
} // namespace
std::function<BooleanOrIntegerLiteral()> ConstructUserSearchStrategy(
const CpModelProto& cp_model_proto, Model* model) {
if (cp_model_proto.search_strategy().empty()) return nullptr;
std::vector<DecisionStrategyProto> strategies;
for (const DecisionStrategyProto& proto : cp_model_proto.search_strategy()) {
strategies.push_back(proto);
}
const auto& view = *model->GetOrCreate<CpModelView>();
const auto& parameters = *model->GetOrCreate<SatParameters>();
auto* random = model->GetOrCreate<ModelRandomGenerator>();
// Note that we copy strategies to keep the return function validity
// independently of the life of the passed vector.
return [&view, &parameters, random, strategies]() {
for (const DecisionStrategyProto& strategy : strategies) {
int candidate_ref = -1;
int64_t candidate_value = std::numeric_limits<int64_t>::max();
// TODO(user): Improve the complexity if this becomes an issue which
// may be the case if we do a fixed_search.
// To store equivalent variables in randomized search.
const bool randomize_decision =
parameters.search_random_variable_pool_size() > 1;
TopN<int, NoisyInteger> top_variables(
randomize_decision ? parameters.search_random_variable_pool_size()
: 1);
for (const LinearExpressionProto& expr : strategy.exprs()) {
const int var = expr.vars(0);
if (view.IsFixed(var)) continue;
int64_t coeff = expr.coeffs(0);
int64_t offset = expr.offset();
int64_t lb = view.Min(var);
int64_t ub = view.Max(var);
int ref = var;
if (coeff < 0) {
lb = -view.Max(var);
ub = -view.Min(var);
coeff = -coeff;
ref = NegatedRef(var);
}
int64_t value(0);
switch (strategy.variable_selection_strategy()) {
case DecisionStrategyProto::CHOOSE_FIRST:
break;
case DecisionStrategyProto::CHOOSE_LOWEST_MIN:
value = coeff * lb + offset;
break;
case DecisionStrategyProto::CHOOSE_HIGHEST_MAX:
value = -(coeff * ub + offset);
break;
case DecisionStrategyProto::CHOOSE_MIN_DOMAIN_SIZE:
// The size of the domain is not multiplied by the coeff.
value = ub - lb + 1;
break;
case DecisionStrategyProto::CHOOSE_MAX_DOMAIN_SIZE:
// The size of the domain is not multiplied by the coeff.
value = -(ub - lb + 1);
break;
default:
LOG(FATAL) << "Unknown VariableSelectionStrategy "
<< strategy.variable_selection_strategy();
}
if (randomize_decision) {
// We need to use -value as we want the minimum valued variables.
// We add a random noise to get improve the entropy.
const double noise = absl::Uniform(*random, 0., 1.0);
top_variables.Add(ref, {-value, noise});
candidate_value = std::min(candidate_value, value);
} else if (value < candidate_value) {
candidate_ref = ref;
candidate_value = value;
}
// We can stop scanning if the variable selection strategy is to use the
// first unbound variable and no randomization is needed.
if (strategy.variable_selection_strategy() ==
DecisionStrategyProto::CHOOSE_FIRST &&
!randomize_decision) {
break;
}
}
// Check if one active variable has been found.
if (candidate_value == std::numeric_limits<int64_t>::max()) continue;
// Pick the winner when decisions are randomized.
if (randomize_decision) {
const std::vector<int>& candidates = top_variables.UnorderedElements();
candidate_ref = candidates[absl::Uniform(
*random, 0, static_cast<int>(candidates.size()))];
}
DecisionStrategyProto::DomainReductionStrategy selection =
strategy.domain_reduction_strategy();
if (!RefIsPositive(candidate_ref)) {
switch (selection) {
case DecisionStrategyProto::SELECT_MIN_VALUE:
selection = DecisionStrategyProto::SELECT_MAX_VALUE;
break;
case DecisionStrategyProto::SELECT_MAX_VALUE:
selection = DecisionStrategyProto::SELECT_MIN_VALUE;
break;
case DecisionStrategyProto::SELECT_LOWER_HALF:
selection = DecisionStrategyProto::SELECT_UPPER_HALF;
break;
case DecisionStrategyProto::SELECT_UPPER_HALF:
selection = DecisionStrategyProto::SELECT_LOWER_HALF;
break;
default:
break;
}
}
const int var = PositiveRef(candidate_ref);
const int64_t lb = view.Min(var);
const int64_t ub = view.Max(var);
switch (selection) {
case DecisionStrategyProto::SELECT_MIN_VALUE:
return view.LowerOrEqual(var, lb);
case DecisionStrategyProto::SELECT_MAX_VALUE:
return view.GreaterOrEqual(var, ub);
case DecisionStrategyProto::SELECT_LOWER_HALF:
return view.LowerOrEqual(var, lb + (ub - lb) / 2);
case DecisionStrategyProto::SELECT_UPPER_HALF:
return view.GreaterOrEqual(var, ub - (ub - lb) / 2);
case DecisionStrategyProto::SELECT_MEDIAN_VALUE:
return view.MedianValue(var);
case DecisionStrategyProto::SELECT_RANDOM_HALF:
return view.RandomSplit(var, lb, ub);
default:
LOG(FATAL) << "Unknown DomainReductionStrategy "
<< strategy.domain_reduction_strategy();
}
}
return BooleanOrIntegerLiteral();
};
}
// TODO(user): Implement a routing search strategy.
std::function<BooleanOrIntegerLiteral()> ConstructHeuristicSearchStrategy(
const CpModelProto& cp_model_proto, Model* model) {
if (ModelHasSchedulingConstraints(cp_model_proto)) {
std::vector<std::function<BooleanOrIntegerLiteral()>> heuristics;
const auto& params = *model->GetOrCreate<SatParameters>();
bool possible_new_constraints = false;
if (params.use_dynamic_precedence_in_disjunctive()) {
possible_new_constraints = true;
heuristics.push_back(DisjunctivePrecedenceSearchHeuristic(model));
}
if (params.use_dynamic_precedence_in_cumulative()) {
possible_new_constraints = true;
heuristics.push_back(CumulativePrecedenceSearchHeuristic(model));
}
// Tricky: we need to create this at level zero in case there are no linear
// constraint in the model at the beginning.
//
// TODO(user): Alternatively, support creation of SatPropagator at positive
// level.
if (possible_new_constraints && params.new_linear_propagation()) {
model->GetOrCreate<LinearPropagator>();
}
heuristics.push_back(SchedulingSearchHeuristic(model));
return SequentialSearch(std::move(heuristics));
}
return nullptr;
}
std::function<BooleanOrIntegerLiteral()>
ConstructIntegerCompletionSearchStrategy(
absl::Span<const IntegerVariable> variable_mapping,
IntegerVariable objective_var, Model* model) {
const auto& params = *model->GetOrCreate<SatParameters>();
if (!params.instantiate_all_variables()) {
return []() { return BooleanOrIntegerLiteral(); };
}
std::vector<IntegerVariable> decisions;
for (const IntegerVariable var : variable_mapping) {
if (var == kNoIntegerVariable) continue;
// Make sure we try to fix the objective to its lowest value first.
// TODO(user): we could also fix terms of the objective in the right
// direction.
if (var == NegationOf(objective_var)) {
decisions.push_back(objective_var);
} else {
decisions.push_back(var);
}
}
return FirstUnassignedVarAtItsMinHeuristic(decisions, model);
}
// Constructs a search strategy that follow the hint from the model.
std::function<BooleanOrIntegerLiteral()> ConstructHintSearchStrategy(
const CpModelProto& cp_model_proto, CpModelMapping* mapping, Model* model) {
std::vector<BooleanOrIntegerVariable> vars;
std::vector<IntegerValue> values;
for (int i = 0; i < cp_model_proto.solution_hint().vars_size(); ++i) {
const int ref = cp_model_proto.solution_hint().vars(i);
CHECK(RefIsPositive(ref));
BooleanOrIntegerVariable var;
if (mapping->IsBoolean(ref)) {
var.bool_var = mapping->Literal(ref).Variable();
} else {
var.int_var = mapping->Integer(ref);
}
vars.push_back(var);
values.push_back(IntegerValue(cp_model_proto.solution_hint().values(i)));
}
return FollowHint(vars, values, model);
}
void ConstructFixedSearchStrategy(SearchHeuristics* h, Model* model) {
// We start by the user specified heuristic.
std::vector<std::function<BooleanOrIntegerLiteral()>> heuristics;
if (h->user_search != nullptr) {
heuristics.push_back(h->user_search);
}
if (h->heuristic_search != nullptr) {
heuristics.push_back(h->heuristic_search);
} else {
heuristics.push_back(PseudoCost(model));
}
if (h->integer_completion_search != nullptr) {
heuristics.push_back(h->integer_completion_search);
}
h->fixed_search = SequentialSearch(std::move(heuristics));
}
std::function<BooleanOrIntegerLiteral()> InstrumentSearchStrategy(
const CpModelProto& cp_model_proto,
absl::Span<const IntegerVariable> variable_mapping,
std::function<BooleanOrIntegerLiteral()> instrumented_strategy,
Model* model) {
std::vector<int> ref_to_display;
for (int i = 0; i < cp_model_proto.variables_size(); ++i) {
if (variable_mapping[i] == kNoIntegerVariable) continue;
if (cp_model_proto.variables(i).name().empty()) continue;
ref_to_display.push_back(i);
}
std::sort(ref_to_display.begin(), ref_to_display.end(), [&](int i, int j) {
return cp_model_proto.variables(i).name() <
cp_model_proto.variables(j).name();
});
std::vector<std::pair<int64_t, int64_t>> old_domains(variable_mapping.size());
return [instrumented_strategy, model, variable_mapping, &cp_model_proto,
old_domains, ref_to_display]() mutable {
const BooleanOrIntegerLiteral decision = instrumented_strategy();
if (!decision.HasValue()) return decision;
if (decision.boolean_literal_index != kNoLiteralIndex) {
const Literal l = Literal(decision.boolean_literal_index);
LOG(INFO) << "Boolean decision " << l;
const auto& encoder = model->Get<IntegerEncoder>();
for (const IntegerLiteral i_lit : encoder->GetIntegerLiterals(l)) {
LOG(INFO) << " - associated with " << i_lit;
}
for (const auto [var, value] : encoder->GetEqualityLiterals(l)) {
LOG(INFO) << " - associated with " << var << " == " << value;
}
} else {
LOG(INFO) << "Integer decision " << decision.integer_literal;
}
const int level = model->Get<Trail>()->CurrentDecisionLevel();
std::string to_display =
absl::StrCat("Diff since last call, level=", level, "\n");
IntegerTrail* integer_trail = model->GetOrCreate<IntegerTrail>();
for (const int ref : ref_to_display) {
const IntegerVariable var = variable_mapping[ref];
const std::pair<int64_t, int64_t> new_domain(
integer_trail->LowerBound(var).value(),
integer_trail->UpperBound(var).value());
if (new_domain != old_domains[ref]) {
absl::StrAppend(&to_display, cp_model_proto.variables(ref).name(), " [",
old_domains[ref].first, ",", old_domains[ref].second,
"] -> [", new_domain.first, ",", new_domain.second,
"]\n");
old_domains[ref] = new_domain;
}
}
LOG(INFO) << to_display;
return decision;
};
}
absl::flat_hash_map<std::string, SatParameters> GetNamedParameters(
SatParameters base_params) {
absl::flat_hash_map<std::string, SatParameters> strategies;
// By default we disable the logging when we generate a set of parameter. It
// is possible to force it by setting it in the corresponding named parameter
// via the subsolver_params field.
base_params.set_log_search_progress(false);
// The "default" name can be used for the base_params unchanged.
strategies["default"] = base_params;
// Lp variations only.
{
SatParameters new_params = base_params;
new_params.set_linearization_level(0);
strategies["no_lp"] = new_params;
new_params.set_linearization_level(1);
strategies["default_lp"] = new_params;
new_params.set_linearization_level(2);
new_params.set_add_lp_constraints_lazily(false);
strategies["max_lp"] = new_params;
new_params.set_use_symmetry_in_lp(true);
strategies["max_lp_sym"] = new_params;
}
// Core. Note that we disable the lp here because it is faster on the minizinc
// benchmark.
//
// TODO(user): Do more experiments, the LP with core could be useful, but we
// probably need to incorporate the newly created integer variables from the
// core algorithm into the LP.
{
SatParameters new_params = base_params;
new_params.set_search_branching(SatParameters::AUTOMATIC_SEARCH);
new_params.set_optimize_with_core(true);
new_params.set_linearization_level(0);
strategies["core"] = new_params;
}
// It can be interesting to try core and lp.
{
SatParameters new_params = base_params;
new_params.set_search_branching(SatParameters::AUTOMATIC_SEARCH);
new_params.set_optimize_with_core(true);
new_params.set_linearization_level(1);
strategies["core_default_lp"] = new_params;
}
{
SatParameters new_params = base_params;
new_params.set_search_branching(SatParameters::AUTOMATIC_SEARCH);
new_params.set_optimize_with_core(true);
new_params.set_linearization_level(2);
strategies["core_max_lp"] = new_params;
}
{
SatParameters new_params = base_params;
new_params.set_search_branching(SatParameters::AUTOMATIC_SEARCH);
new_params.set_optimize_with_core(true);
new_params.set_optimize_with_max_hs(true);
strategies["max_hs"] = new_params;
}
{
SatParameters new_params = base_params;
new_params.set_optimize_with_lb_tree_search(true);
// We do not want to change the objective_var lb from outside as it gives
// better result to only use locally derived reason in that algo.
new_params.set_share_objective_bounds(false);
new_params.set_linearization_level(0);
strategies["lb_tree_search_no_lp"] = new_params;
new_params.set_linearization_level(2);
if (base_params.use_dual_scheduling_heuristics()) {
AddExtraSchedulingPropagators(new_params);
}
// We want to spend more time on the LP here.
new_params.set_add_lp_constraints_lazily(false);
new_params.set_root_lp_iterations(100'000);
strategies["lb_tree_search"] = new_params;
}
{
SatParameters new_params = base_params;
new_params.set_use_objective_lb_search(true);
new_params.set_linearization_level(0);
strategies["objective_lb_search_no_lp"] = new_params;
new_params.set_linearization_level(1);
strategies["objective_lb_search"] = new_params;
if (base_params.use_dual_scheduling_heuristics()) {
AddExtraSchedulingPropagators(new_params);
}
new_params.set_linearization_level(2);
strategies["objective_lb_search_max_lp"] = new_params;
}
{
SatParameters new_params = base_params;
new_params.set_use_objective_shaving_search(true);
new_params.set_cp_model_presolve(true);
new_params.set_cp_model_probing_level(0);
new_params.set_symmetry_level(0);
if (base_params.use_dual_scheduling_heuristics()) {
AddExtraSchedulingPropagators(new_params);
}
strategies["objective_shaving"] = new_params;
new_params.set_linearization_level(0);
strategies["objective_shaving_no_lp"] = new_params;
new_params.set_linearization_level(2);
strategies["objective_shaving_max_lp"] = new_params;
}
{
SatParameters new_params = base_params;
new_params.set_cp_model_presolve(true);
new_params.set_cp_model_probing_level(0);
new_params.set_symmetry_level(0);
new_params.set_share_objective_bounds(false);
new_params.set_share_level_zero_bounds(false);
new_params.set_no_overlap_2d_boolean_relations_limit(40);
strategies["variables_shaving"] = new_params;
new_params.set_linearization_level(0);
strategies["variables_shaving_no_lp"] = new_params;
if (base_params.use_dual_scheduling_heuristics()) {
AddExtraSchedulingPropagators(new_params);
}
new_params.set_linearization_level(2);
strategies["variables_shaving_max_lp"] = new_params;
}
{
SatParameters new_params = base_params;
new_params.set_search_branching(SatParameters::AUTOMATIC_SEARCH);
new_params.set_use_probing_search(true);
new_params.set_at_most_one_max_expansion_size(2);
// Use a small deterministic time to avoid spending too much time on
// shaving by default. The probing workers will increase it as needed.
new_params.set_shaving_search_deterministic_time(0.001);
if (base_params.use_dual_scheduling_heuristics()) {
AddExtraSchedulingPropagators(new_params);
}
strategies["probing"] = new_params;
new_params.set_linearization_level(0);
strategies["probing_no_lp"] = new_params;
// We want to spend more time on the LP here.
new_params.set_linearization_level(2);
new_params.set_add_lp_constraints_lazily(false);
new_params.set_root_lp_iterations(100'000);
strategies["probing_max_lp"] = new_params;
}
// Search variation.
{
SatParameters new_params = base_params;
new_params.set_search_branching(SatParameters::AUTOMATIC_SEARCH);
strategies["auto"] = new_params;
new_params.set_search_branching(SatParameters::FIXED_SEARCH);
new_params.set_use_dynamic_precedence_in_disjunctive(false);
new_params.set_use_dynamic_precedence_in_cumulative(false);
strategies["fixed"] = new_params;
new_params.set_linearization_level(0);
strategies["fixed_no_lp"] = new_params;
new_params.set_linearization_level(2);
new_params.set_add_lp_constraints_lazily(false);
new_params.set_root_lp_iterations(100'000);
strategies["fixed_max_lp"] = new_params;
}
// Portfolio search.
{
SatParameters new_params = base_params;
new_params.set_search_branching(SatParameters::PORTFOLIO_SEARCH);
strategies["portfolio"] = new_params;
new_params.set_linearization_level(0);
strategies["portfolio_no_lp"] = new_params;
new_params.set_linearization_level(2);
new_params.set_add_lp_constraints_lazily(false);
new_params.set_root_lp_iterations(100'000);
strategies["portfolio_max_lp"] = new_params;
}
// Quick restart.
{
// TODO(user): Experiment with search_random_variable_pool_size.
SatParameters new_params = base_params;
new_params.set_search_branching(
SatParameters::PORTFOLIO_WITH_QUICK_RESTART_SEARCH);
strategies["quick_restart"] = new_params;
new_params.set_linearization_level(0);
strategies["quick_restart_no_lp"] = new_params;
new_params.set_linearization_level(2);
strategies["quick_restart_max_lp"] = new_params;
}
{
SatParameters new_params = base_params;
new_params.set_linearization_level(2);
new_params.set_search_branching(SatParameters::LP_SEARCH);
if (base_params.use_dual_scheduling_heuristics()) {
AddExtraSchedulingPropagators(new_params);
}
strategies["reduced_costs"] = new_params;
}
{
// Note: no dual scheduling heuristics.
SatParameters new_params = base_params;
new_params.set_linearization_level(2);
new_params.set_search_branching(SatParameters::PSEUDO_COST_SEARCH);
new_params.set_exploit_best_solution(true);
strategies["pseudo_costs"] = new_params;
}
// Less encoding.
{
SatParameters new_params = base_params;
new_params.set_boolean_encoding_level(0);
strategies["less_encoding"] = new_params;
}
// Base parameters for shared tree worker.
{
SatParameters new_params = base_params;
new_params.set_use_shared_tree_search(true);
new_params.set_search_branching(SatParameters::AUTOMATIC_SEARCH);
// These settings don't make sense with shared tree search, turn them off as
// they can break things.
new_params.set_optimize_with_core(false);
new_params.set_optimize_with_lb_tree_search(false);
new_params.set_optimize_with_max_hs(false);
// Given that each workers work on a different part of the subtree, it might
// not be a good idea to try to work on a global shared solution.
//
// TODO(user): Experiments more here, in particular we could follow it if
// it falls into the current subtree.
new_params.set_polarity_exploit_ls_hints(false);
strategies["shared_tree"] = new_params;
}
// Base parameters for LNS worker.
{
SatParameters lns_params = base_params;
lns_params.set_stop_after_first_solution(false);
lns_params.set_cp_model_presolve(true);
// We disable costly presolve/inprocessing.
lns_params.set_use_sat_inprocessing(false);
lns_params.set_cp_model_probing_level(0);
lns_params.set_symmetry_level(0);
lns_params.set_find_big_linear_overlap(false);
lns_params.set_find_clauses_that_are_exactly_one(false);
lns_params.set_log_search_progress(false);
lns_params.set_debug_crash_on_bad_hint(false); // Can happen in lns.
lns_params.set_solution_pool_size(1); // Keep the best solution found.
lns_params.set_alternative_pool_size(0); // Disable.
strategies["lns"] = lns_params;
// Note that we only do this for the derived parameters. The strategy "lns"
// will be handled along with the other ones.
auto it = absl::c_find_if(
base_params.subsolver_params(),
[](const SatParameters& params) { return params.name() == "lns"; });
if (it != base_params.subsolver_params().end()) {
lns_params.MergeFrom(*it);
}
SatParameters lns_params_base = lns_params;
lns_params_base.set_linearization_level(0);
lns_params_base.set_search_branching(SatParameters::AUTOMATIC_SEARCH);
strategies["lns_base"] = lns_params_base;
SatParameters lns_params_stalling = lns_params;
lns_params_stalling.set_search_branching(SatParameters::PORTFOLIO_SEARCH);
lns_params_stalling.set_search_random_variable_pool_size(5);
strategies["lns_stalling"] = lns_params_stalling;
// For routing, the LP relaxation seems pretty important, so we prefer an
// high linearization level to solve LNS subproblems.
SatParameters lns_params_routing = lns_params;
lns_params_routing.set_linearization_level(2);
lns_params_routing.set_search_branching(SatParameters::AUTOMATIC_SEARCH);
strategies["lns_routing"] = lns_params_routing;
}
// Add user defined ones.
// Note that this might be merged to our default ones.
for (const SatParameters& params : base_params.subsolver_params()) {
auto it = strategies.find(params.name());
if (it != strategies.end()) {
it->second.MergeFrom(params);
} else {
// Merge the named parameters with the base parameters to create the new
// parameters.
SatParameters new_params = base_params;
new_params.MergeFrom(params);
strategies[params.name()] = new_params;
}
}
// Fix names (we don't set them above).
for (auto& [name, params] : strategies) {
params.set_name(name);
}
return strategies;
}
// Note: in flatzinc setting, we know we always have a fixed search defined.
//
// Things to try:
// - Specialize for purely boolean problems
// - Disable linearization_level options for non linear problems
// - Fast restart in randomized search
// - Different propatation levels for scheduling constraints
std::vector<SatParameters> GetFullWorkerParameters(
const SatParameters& base_params, const CpModelProto& cp_model,
int num_already_present, SubsolverNameFilter* filter) {
// Defines a set of named strategies so it is easier to read in one place
// the one that are used. See below.
const auto strategies = GetNamedParameters(base_params);
// We only use a "fixed search" worker if some strategy is specified or
// if we have a scheduling model.
//
// TODO(user): For scheduling, this is important to find good first solution
// but afterwards it is not really great and should probably be replaced by a
// LNS worker.
const bool use_fixed_strategy = !cp_model.search_strategy().empty() ||
ModelHasSchedulingConstraints(cp_model);
// Our current set of strategies
//
// TODO(user): Avoid launching two strategies if they are the same,
// like if there is no lp, or everything is already linearized at level 1.
std::vector<std::string> names;
// Starts by adding user specified ones.
for (const std::string& name : base_params.extra_subsolvers()) {
names.push_back(name);
}
// We use the default if empty.
if (base_params.subsolvers().empty()) {
// Note that the order is important as the list can be truncated.
names.push_back("default_lp");
names.push_back("fixed");
names.push_back("core");
names.push_back("no_lp");
if (cp_model.has_symmetry()) {
names.push_back("max_lp_sym");
} else {
// If there is no symmetry, max_lp_sym and max_lp are the same, but
// we prefer the less confusing name.
names.push_back("max_lp");
}
names.push_back("quick_restart");
names.push_back("reduced_costs");
names.push_back("quick_restart_no_lp");
names.push_back("pseudo_costs");
names.push_back("lb_tree_search");
names.push_back("probing");
names.push_back("objective_lb_search");
names.push_back("objective_shaving_no_lp");
names.push_back("objective_shaving_max_lp");
names.push_back("probing_max_lp");
names.push_back("probing_no_lp");
names.push_back("objective_lb_search_no_lp");
names.push_back("objective_lb_search_max_lp");
if (cp_model.has_symmetry()) {
names.push_back("max_lp");
}
} else {
for (const std::string& name : base_params.subsolvers()) {
// Hack for flatzinc. At the time of parameter setting, the objective is
// not expanded. So we do not know if core is applicable or not.
if (name == "core_or_no_lp") {
if (!cp_model.has_objective() ||
cp_model.objective().vars_size() <= 1) {
names.push_back("no_lp");
} else {
names.push_back("core");
}
} else {
names.push_back(name);
}
}
}
// Remove the names that should be ignored.
int new_size = 0;
for (const std::string& name : names) {
if (filter->Keep(name)) {
names[new_size++] = name;
}
}
names.resize(new_size);
// Creates the diverse set of parameters with names and seed.
std::vector<SatParameters> result;
for (const std::string& name : names) {
SatParameters params = strategies.at(name);
// Do some filtering.
if (!use_fixed_strategy &&
params.search_branching() == SatParameters::FIXED_SEARCH) {
continue;
}
// TODO(user): Enable probing_search in deterministic mode.
// Currently it timeouts on small problems as the deterministic time limit
// never hits the sharding limit.
if (params.use_probing_search() && params.interleave_search()) continue;
// TODO(user): Enable shaving search in interleave mode.
// Currently it do not respect ^C, and has no per chunk time limit.
if ((params.use_objective_shaving_search()) && params.interleave_search()) {
continue;
}
// In the corner case of empty variable, lets not schedule the probing as
// it currently just loop forever instead of returning right away.
if (params.use_probing_search() && cp_model.variables().empty()) continue;
if (cp_model.has_objective() && !cp_model.objective().vars().empty()) {
// Disable core search if there is only 1 term in the objective.
if (cp_model.objective().vars().size() == 1 &&
params.optimize_with_core()) {
continue;
}
if (name == "less_encoding") continue;
// Disable subsolvers that do not implement the deterministic mode.
//
// TODO(user): Enable lb_tree_search in deterministic mode.
if (params.interleave_search() &&
(params.optimize_with_lb_tree_search() ||
params.use_objective_lb_search())) {
continue;
}
} else {
// Remove subsolvers that require an objective.
if (params.optimize_with_lb_tree_search()) continue;
if (params.optimize_with_core()) continue;
if (params.use_objective_lb_search()) continue;
if (params.use_objective_shaving_search()) continue;
if (params.search_branching() == SatParameters::LP_SEARCH) continue;
if (params.search_branching() == SatParameters::PSEUDO_COST_SEARCH) {
continue;
}
}
// Add this strategy.
params.set_name(name);
params.set_random_seed(CombineSeed(
base_params.random_seed(), static_cast<int64_t>(result.size()) + 1));
result.push_back(params);
}
// In interleaved mode, we run all of them.
//
// TODO(user): Actually make sure the gap num_workers <-> num_heuristics is
// contained.
if (base_params.interleave_search()) return result;
// Apply the logic for how many we keep.
int num_to_keep = base_params.num_full_subsolvers();
if (num_to_keep == 0) {
// Derive some automatic number to leave room for LS/LNS and other
// strategies not taken into account here.
const int num_available =
std::max(0, base_params.num_workers() - num_already_present);
const auto heuristic_num_workers = [](int num_workers) {
DCHECK_GE(num_workers, 0);
if (num_workers == 1) return 1;
if (num_workers <= 4) return num_workers - 1;
if (num_workers <= 8) return num_workers - 2;
if (num_workers <= 16) return num_workers - (num_workers / 4 + 1);
return num_workers - (num_workers / 2 - 3);
};
num_to_keep = heuristic_num_workers(num_available);
}
if (result.size() > num_to_keep) {
result.resize(std::max(0, num_to_keep));
}
return result;
}
std::vector<SatParameters> GetFirstSolutionBaseParams(
const SatParameters& base_params) {
std::vector<SatParameters> result;
const auto get_base = [&result, &base_params](bool fj) {
SatParameters new_params = base_params;
new_params.set_log_search_progress(false);
new_params.set_use_feasibility_jump(fj);
const int base_seed = base_params.random_seed();
new_params.set_random_seed(CombineSeed(base_seed, result.size()));
return new_params;
};
// Add one feasibility jump.
if (base_params.use_feasibility_jump()) {
SatParameters new_params = get_base(true);
new_params.set_name("fj");
new_params.set_feasibility_jump_linearization_level(0);
result.push_back(new_params);
}
// Random search.
for (int i = 0; i < 2; ++i) {
SatParameters new_params = get_base(false);
new_params.set_search_random_variable_pool_size(5);
new_params.set_search_branching(SatParameters::RANDOMIZED_SEARCH);
if (i % 2 == 0) {
new_params.set_name("fs_random_no_lp");
new_params.set_linearization_level(0);
} else {
new_params.set_name("fs_random");
}
result.push_back(new_params);
}
// Add a second feasibility jump.
if (base_params.use_feasibility_jump()) {
SatParameters new_params = get_base(true);
new_params.set_name("fj");
new_params.set_feasibility_jump_linearization_level(0);
result.push_back(new_params);
}
// Random quick restart.
for (int i = 0; i < 2; ++i) {
SatParameters new_params = get_base(false);
new_params.set_search_random_variable_pool_size(5);
new_params.set_search_branching(
SatParameters::PORTFOLIO_WITH_QUICK_RESTART_SEARCH);
if (i % 2 == 0) {
new_params.set_name("fs_random_quick_restart_no_lp");
new_params.set_linearization_level(0);
} else {
new_params.set_name("fs_random_quick_restart");
}
result.push_back(new_params);
}
// Add a linear feasibility jump.
// This one seems to perform worse, so we add only 1 for 2 normal LS, and we
// add this late.
if (base_params.use_feasibility_jump()) {
SatParameters new_params = get_base(true);
new_params.set_name("fj_lin");
new_params.set_feasibility_jump_linearization_level(2);
result.push_back(new_params);
}
return result;
}
std::vector<SatParameters> RepeatParameters(
absl::Span<const SatParameters> base_params, int num_params_to_generate) {
// Return if we are done.
std::vector<SatParameters> result;
result.assign(base_params.begin(), base_params.end());
if (result.empty()) return result;
if (result.size() >= num_params_to_generate) {
result.resize(num_params_to_generate);
return result;
}
// Repeat parameters until we have enough.
int i = 0;
const int base_size = result.size();
while (result.size() < num_params_to_generate) {
result.push_back(result[i % base_size]);
result.back().set_random_seed(CombineSeed(result.back().random_seed(), i));
++i;
}
return result;
}
SubsolverNameFilter::SubsolverNameFilter(const SatParameters& params) {
for (const auto& pattern : params.filter_subsolvers()) {
filter_patterns_.push_back(pattern);
}
for (const auto& pattern : params.ignore_subsolvers()) {
ignore_patterns_.push_back(pattern);
}
// Hack for backward compatibility and easy of use.
if (params.use_ls_only()) {
filter_patterns_.push_back("ls*");
filter_patterns_.push_back("fj*");
}
if (params.use_lns_only()) {
// Still add first solution solvers.
filter_patterns_.push_back("fj*");
filter_patterns_.push_back("fs*");
filter_patterns_.push_back("*lns");
}
}
bool SubsolverNameFilter::Keep(absl::string_view name) {
last_name_ = name;
if (!filter_patterns_.empty()) {
bool keep = false;
for (const absl::string_view pattern : filter_patterns_) {
if (FNMatch(pattern, name)) {
keep = true;
break;
}
}
if (!keep) {
ignored_.emplace_back(name);
return false;
}
}
for (const absl::string_view pattern : ignore_patterns_) {
if (FNMatch(pattern, name)) {
ignored_.emplace_back(name);
return false;
}
}
return true;
}
bool SubsolverNameFilter::FNMatch(absl::string_view pattern,
absl::string_view str) {
bool in_wildcard_match = false;
while (true) {
if (pattern.empty()) {
return in_wildcard_match || str.empty();
}
if (str.empty()) {
return pattern.find_first_not_of('*') == pattern.npos;
}
switch (pattern.front()) {
case '*':
pattern.remove_prefix(1);
in_wildcard_match = true;
break;
case '?':
pattern.remove_prefix(1);
str.remove_prefix(1);
break;
default:
if (in_wildcard_match) {
absl::string_view fixed_portion = pattern;
const size_t end = fixed_portion.find_first_of("*?");
if (end != fixed_portion.npos) {
fixed_portion = fixed_portion.substr(0, end);
}
const size_t match = str.find(fixed_portion);
if (match == str.npos) {
return false;
}
pattern.remove_prefix(fixed_portion.size());
str.remove_prefix(match + fixed_portion.size());
in_wildcard_match = false;
} else {
if (pattern.front() != str.front()) {
return false;
}
pattern.remove_prefix(1);
str.remove_prefix(1);
}
break;
}
}
}
} // namespace sat
} // namespace operations_research
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