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ortools-clone/ortools/sat/cp_model_loader.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_loader.h"
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <memory>
#include <numeric>
#include <string>
#include <utility>
#include <vector>
#include "absl/container/btree_map.h"
#include "absl/container/btree_set.h"
#include "absl/container/flat_hash_map.h"
#include "absl/container/flat_hash_set.h"
#include "absl/log/check.h"
#include "absl/log/log.h"
#include "absl/log/vlog_is_on.h"
#include "absl/strings/str_cat.h"
#include "absl/types/span.h"
#include "ortools/algorithms/sparse_permutation.h"
#include "ortools/base/stl_util.h"
#include "ortools/base/strong_vector.h"
#include "ortools/sat/all_different.h"
#include "ortools/sat/circuit.h"
#include "ortools/sat/clause.h"
#include "ortools/sat/cp_constraints.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/cumulative.h"
#include "ortools/sat/diffn.h"
#include "ortools/sat/disjunctive.h"
#include "ortools/sat/implied_bounds.h"
#include "ortools/sat/integer.h"
#include "ortools/sat/integer_base.h"
#include "ortools/sat/integer_expr.h"
#include "ortools/sat/intervals.h"
#include "ortools/sat/linear_constraint.h"
#include "ortools/sat/model.h"
#include "ortools/sat/pb_constraint.h"
#include "ortools/sat/precedences.h"
#include "ortools/sat/sat_base.h"
#include "ortools/sat/sat_parameters.pb.h"
#include "ortools/sat/sat_solver.h"
#include "ortools/sat/symmetry.h"
#include "ortools/sat/timetable.h"
#include "ortools/sat/util.h"
#include "ortools/util/logging.h"
#include "ortools/util/sorted_interval_list.h"
#include "ortools/util/strong_integers.h"
#include "ortools/util/time_limit.h"
namespace operations_research {
namespace sat {
namespace {
template <typename Values>
std::vector<int64_t> ValuesFromProto(const Values& values) {
return std::vector<int64_t>(values.begin(), values.end());
}
// We check if the constraint is a sum(ax * xi) == value.
bool ConstraintIsEq(const LinearConstraintProto& proto) {
return proto.domain_size() == 2 && proto.domain(0) == proto.domain(1);
}
// We check if the constraint is a sum(ax * xi) != value.
bool ConstraintIsNEq(const LinearConstraintProto& proto,
CpModelMapping* mapping, IntegerTrail* integer_trail,
int64_t* single_value) {
const auto [sum_min, sum_max] =
mapping->ComputeMinMaxActivity(proto, integer_trail);
const Domain complement =
Domain(sum_min, sum_max)
.IntersectionWith(ReadDomainFromProto(proto).Complement());
if (complement.IsEmpty()) return false;
const int64_t value = complement.Min();
if (complement.Size() == 1) {
if (single_value != nullptr) {
*single_value = value;
}
return true;
}
return false;
}
} // namespace
void LoadVariables(const CpModelProto& model_proto,
bool view_all_booleans_as_integers, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
const int num_proto_variables = model_proto.variables_size();
// All [0, 1] variables always have a corresponding Boolean, even if it is
// fixed to 0 (domain == [0,0]) or fixed to 1 (domain == [1,1]).
{
auto* sat_solver = m->GetOrCreate<SatSolver>();
CHECK_EQ(sat_solver->NumVariables(), 0);
BooleanVariable new_var(0);
std::vector<BooleanVariable> false_variables;
std::vector<BooleanVariable> true_variables;
mapping->booleans_.resize(num_proto_variables, kNoBooleanVariable);
mapping->reverse_boolean_map_.resize(num_proto_variables, -1);
for (int i = 0; i < num_proto_variables; ++i) {
const auto& domain = model_proto.variables(i).domain();
if (domain.size() != 2) continue;
if (domain[0] >= 0 && domain[1] <= 1) {
mapping->booleans_[i] = new_var;
mapping->reverse_boolean_map_[new_var] = i;
if (domain[1] == 0) {
false_variables.push_back(new_var);
} else if (domain[0] == 1) {
true_variables.push_back(new_var);
}
++new_var;
}
}
sat_solver->SetNumVariables(new_var.value());
for (const BooleanVariable var : true_variables) {
m->Add(ClauseConstraint({sat::Literal(var, true)}));
}
for (const BooleanVariable var : false_variables) {
m->Add(ClauseConstraint({sat::Literal(var, false)}));
}
}
// Compute the list of positive variable reference for which we need to
// create an IntegerVariable.
std::vector<int> var_to_instantiate_as_integer;
if (view_all_booleans_as_integers) {
var_to_instantiate_as_integer.resize(num_proto_variables);
for (int i = 0; i < num_proto_variables; ++i) {
var_to_instantiate_as_integer[i] = i;
}
} else {
// Compute the integer variable references used by the model.
absl::flat_hash_set<int> used_variables;
const bool some_linerization =
m->GetOrCreate<SatParameters>()->linearization_level() > 0;
IndexReferences refs;
for (int c = 0; c < model_proto.constraints_size(); ++c) {
const ConstraintProto& ct = model_proto.constraints(c);
refs = GetReferencesUsedByConstraint(ct);
for (const int ref : refs.variables) {
used_variables.insert(PositiveRef(ref));
}
// We always add a linear relaxation for circuit/route except for
// linearization level zero.
if (some_linerization) {
if (ct.constraint_case() == ConstraintProto::kCircuit) {
for (const int ref : ct.circuit().literals()) {
used_variables.insert(PositiveRef(ref));
}
} else if (ct.constraint_case() == ConstraintProto::kRoutes) {
for (const int ref : ct.routes().literals()) {
used_variables.insert(PositiveRef(ref));
}
}
}
}
// Add the objectives variables that needs to be referenceable as integer
// even if they are only used as Booleans.
if (model_proto.has_objective()) {
for (const int obj_var : model_proto.objective().vars()) {
used_variables.insert(PositiveRef(obj_var));
}
}
// Make sure any unused variable, that is not already a Boolean is
// considered "used".
for (int i = 0; i < num_proto_variables; ++i) {
if (mapping->booleans_[i] == kNoBooleanVariable) {
used_variables.insert(i);
}
}
// We want the variable in the problem order.
var_to_instantiate_as_integer.assign(used_variables.begin(),
used_variables.end());
gtl::STLSortAndRemoveDuplicates(&var_to_instantiate_as_integer);
}
mapping->integers_.resize(num_proto_variables, kNoIntegerVariable);
// It is important for memory usage to reserve tight vector has we have many
// indexed by IntegerVariable. Unfortunately, we create intermediate
// IntegerVariable while loading large linear constraint, or when we have
// disjoint LP component. So this is a best effort at a tight upper bound.
int reservation_size = var_to_instantiate_as_integer.size();
for (const ConstraintProto& ct : model_proto.constraints()) {
if (ct.constraint_case() != ConstraintProto::kLinear) continue;
const int ct_size = ct.linear().vars().size();
if (ct_size > 100) {
reservation_size += static_cast<int>(std::round(std::sqrt(ct_size)));
}
}
if (model_proto.has_objective()) {
reservation_size += 1; // Objective var.
const int ct_size = model_proto.objective().vars().size() + 1;
if (ct_size > 100) {
reservation_size += static_cast<int>(std::round(std::sqrt(ct_size)));
}
}
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
integer_trail->ReserveSpaceForNumVariables(reservation_size);
m->GetOrCreate<GenericLiteralWatcher>()->ReserveSpaceForNumVariables(
reservation_size);
mapping->reverse_integer_map_.resize(var_to_instantiate_as_integer.size(),
-1);
for (const int i : var_to_instantiate_as_integer) {
const auto& var_proto = model_proto.variables(i);
mapping->integers_[i] =
integer_trail->AddIntegerVariable(ReadDomainFromProto(var_proto));
DCHECK(VariableIsPositive(mapping->integers_[i]));
const PositiveOnlyIndex index = GetPositiveOnlyIndex(mapping->integers_[i]);
DCHECK_LT(index, mapping->reverse_integer_map_.size());
mapping->reverse_integer_map_[index] = i;
}
auto* encoder = m->GetOrCreate<IntegerEncoder>();
auto* intervals_repository = m->GetOrCreate<IntervalsRepository>();
// Link any variable that has both views.
for (int i = 0; i < num_proto_variables; ++i) {
if (mapping->integers_[i] == kNoIntegerVariable) continue;
if (mapping->booleans_[i] == kNoBooleanVariable) continue;
// Associate with corresponding integer variable.
encoder->AssociateToIntegerEqualValue(
sat::Literal(mapping->booleans_[i], true), mapping->integers_[i],
IntegerValue(1));
}
// Create the interval variables.
mapping->intervals_.resize(model_proto.constraints_size(),
kNoIntervalVariable);
for (int c = 0; c < model_proto.constraints_size(); ++c) {
const ConstraintProto& ct = model_proto.constraints(c);
if (ct.constraint_case() != ConstraintProto::ConstraintCase::kInterval) {
continue;
}
if (HasEnforcementLiteral(ct)) {
const sat::Literal enforcement_literal =
mapping->Literal(ct.enforcement_literal(0));
// TODO(user): Fix the constant variable situation. An optional interval
// with constant start/end or size cannot share the same constant
// variable if it is used in non-optional situation.
mapping->intervals_[c] = intervals_repository->CreateInterval(
mapping->Affine(ct.interval().start()),
mapping->Affine(ct.interval().end()),
mapping->Affine(ct.interval().size()), enforcement_literal.Index(),
/*add_linear_relation=*/false);
} else {
mapping->intervals_[c] = intervals_repository->CreateInterval(
mapping->Affine(ct.interval().start()),
mapping->Affine(ct.interval().end()),
mapping->Affine(ct.interval().size()), kNoLiteralIndex,
/*add_linear_relation=*/false);
}
mapping->already_loaded_ct_.insert(&ct);
}
}
void LoadBooleanSymmetries(const CpModelProto& model_proto, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
const SymmetryProto& symmetry = model_proto.symmetry();
if (symmetry.permutations().empty()) return;
// We currently can only use symmetry that touch a subset of variables.
const int num_vars = model_proto.variables().size();
std::vector<bool> can_be_used_in_symmetry(num_vars, true);
// First, we currently only support loading symmetry between Booleans.
for (int v = 0; v < num_vars; ++v) {
if (!mapping->IsBoolean(v)) can_be_used_in_symmetry[v] = false;
}
auto* sat_solver = m->GetOrCreate<SatSolver>();
auto* symmetry_handler = m->GetOrCreate<SymmetryPropagator>();
sat_solver->AddPropagator(symmetry_handler);
const int num_literals = 2 * sat_solver->NumVariables();
symmetry_handler->SetNumLiterals(num_literals);
for (const SparsePermutationProto& perm : symmetry.permutations()) {
bool can_be_used = true;
for (const int var : perm.support()) {
if (!can_be_used_in_symmetry[var]) {
can_be_used = false;
break;
}
}
if (!can_be_used) continue;
// Convert the variable symmetry to a "literal" one.
auto literal_permutation =
std::make_unique<SparsePermutation>(num_literals);
int support_index = 0;
const int num_cycle = perm.cycle_sizes().size();
for (int i = 0; i < num_cycle; ++i) {
const int size = perm.cycle_sizes(i);
const int saved_support_index = support_index;
for (int j = 0; j < size; ++j) {
const int var = perm.support(support_index++);
literal_permutation->AddToCurrentCycle(
mapping->Literal(var).Index().value());
}
literal_permutation->CloseCurrentCycle();
// Note that we also need to add the corresponding cycle for the negated
// literals.
support_index = saved_support_index;
for (int j = 0; j < size; ++j) {
const int var = perm.support(support_index++);
literal_permutation->AddToCurrentCycle(
mapping->Literal(var).NegatedIndex().value());
}
literal_permutation->CloseCurrentCycle();
}
symmetry_handler->AddSymmetry(std::move(literal_permutation));
}
SOLVER_LOG(m->GetOrCreate<SolverLogger>(), "Added ",
symmetry_handler->num_permutations(),
" symmetry to the SAT solver.");
}
// The logic assumes that the linear constraints have been presolved, so that
// equality with a domain bound have been converted to <= or >= and so that we
// never have any trivial inequalities.
//
// TODO(user): Regroup/presolve two encoding like b => x > 2 and the same
// Boolean b => x > 5. These shouldn't happen if we merge linear constraints.
void ExtractEncoding(const CpModelProto& model_proto, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
auto* encoder = m->GetOrCreate<IntegerEncoder>();
auto* logger = m->GetOrCreate<SolverLogger>();
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
auto* sat_solver = m->GetOrCreate<SatSolver>();
auto* time_limit = m->GetOrCreate<TimeLimit>();
// TODO(user): Debug what makes it unsat at this point.
if (sat_solver->ModelIsUnsat()) return;
// Detection of literal equivalent to (i_var == value). We collect all the
// half-reified constraint lit => equality or lit => inequality for a given
// variable, and we will later sort them to detect equivalence.
struct EqualityDetectionHelper {
const ConstraintProto* ct;
sat::Literal literal;
int64_t value;
bool is_equality; // false if != instead.
bool operator<(const EqualityDetectionHelper& o) const {
if (literal.Variable() == o.literal.Variable()) {
if (value == o.value) return is_equality && !o.is_equality;
return value < o.value;
}
return literal.Variable() < o.literal.Variable();
}
};
std::vector<std::vector<EqualityDetectionHelper>> var_to_equalities(
model_proto.variables_size());
// TODO(user): We will re-add the same implied bounds during probing, so
// it might not be necessary to do that here. Also, it might be too early
// if some of the literal view used in the LP are created later, but that
// should be fixable via calls to implied_bounds->NotifyNewIntegerView().
auto* implied_bounds = m->GetOrCreate<ImpliedBounds>();
auto* detector = m->GetOrCreate<ProductDetector>();
// Detection of literal equivalent to (i_var >= bound). We also collect
// all the half-refied part and we will sort the vector for detection of the
// equivalence.
struct InequalityDetectionHelper {
const ConstraintProto* ct;
sat::Literal literal;
IntegerLiteral i_lit;
bool operator<(const InequalityDetectionHelper& o) const {
if (literal.Variable() == o.literal.Variable()) {
return i_lit.var < o.i_lit.var;
}
return literal.Variable() < o.literal.Variable();
}
};
std::vector<InequalityDetectionHelper> inequalities;
// Loop over all constraints and fill var_to_equalities and inequalities.
for (const ConstraintProto& ct : model_proto.constraints()) {
if (ct.constraint_case() != ConstraintProto::ConstraintCase::kLinear) {
continue;
}
if (ct.enforcement_literal().size() != 1) continue;
if (ct.linear().vars_size() != 1) continue;
// ct is a linear constraint with one term and one enforcement literal.
const sat::Literal enforcement_literal =
mapping->Literal(ct.enforcement_literal(0));
if (sat_solver->Assignment().LiteralIsFalse(enforcement_literal)) continue;
const int ref = ct.linear().vars(0);
const int var = PositiveRef(ref);
const Domain domain = ReadDomainFromProto(model_proto.variables(var));
const Domain domain_if_enforced =
ReadDomainFromProto(ct.linear())
.InverseMultiplicationBy(ct.linear().coeffs(0) *
(RefIsPositive(ref) ? 1 : -1));
if (domain_if_enforced.IsEmpty()) {
if (!sat_solver->AddUnitClause(enforcement_literal.Negated())) return;
continue;
}
// Detect enforcement_literal => (var >= value or var <= value).
if (domain_if_enforced.NumIntervals() == 1) {
if (domain_if_enforced.Max() >= domain.Max() &&
domain_if_enforced.Min() > domain.Min()) {
inequalities.push_back({&ct, enforcement_literal,
IntegerLiteral::GreaterOrEqual(
mapping->Integer(var),
IntegerValue(domain_if_enforced.Min()))});
} else if (domain_if_enforced.Min() <= domain.Min() &&
domain_if_enforced.Max() < domain.Max()) {
inequalities.push_back({&ct, enforcement_literal,
IntegerLiteral::LowerOrEqual(
mapping->Integer(var),
IntegerValue(domain_if_enforced.Max()))});
}
}
// Detect implied bounds. The test is less strict than the above
// test.
if (domain_if_enforced.Min() > domain.Min()) {
implied_bounds->Add(
enforcement_literal,
IntegerLiteral::GreaterOrEqual(
mapping->Integer(var), IntegerValue(domain_if_enforced.Min())));
}
if (domain_if_enforced.Max() < domain.Max()) {
implied_bounds->Add(
enforcement_literal,
IntegerLiteral::LowerOrEqual(mapping->Integer(var),
IntegerValue(domain_if_enforced.Max())));
}
// Detect enforcement_literal => (var == value or var != value).
//
// Note that for domain with 2 values like [0, 1], we will detect both ==
// 0 and != 1. Similarly, for a domain in [min, max], we should both
// detect (== min) and (<= min), and both detect (== max) and (>= max).
{
const Domain inter = domain.IntersectionWith(domain_if_enforced);
if (!inter.IsEmpty() && inter.Min() == inter.Max()) {
if (inter.Min() == 0) {
detector->ProcessConditionalZero(enforcement_literal,
mapping->Integer(var));
}
var_to_equalities[var].push_back(
{&ct, enforcement_literal, inter.Min(), true});
}
}
{
const Domain inter =
domain.IntersectionWith(domain_if_enforced.Complement());
if (!inter.IsEmpty() && inter.Min() == inter.Max()) {
var_to_equalities[var].push_back(
{&ct, enforcement_literal, inter.Min(), false});
}
}
}
// Detect Literal <=> X >= value
int num_inequalities = 0;
std::sort(inequalities.begin(), inequalities.end());
for (int i = 0; i + 1 < inequalities.size(); i++) {
if (inequalities[i].literal != inequalities[i + 1].literal.Negated()) {
continue;
}
// TODO(user): In these cases, we could fix the enforcement literal right
// away or ignore the constraint. Note that it will be done later anyway
// though.
if (integer_trail->IntegerLiteralIsTrue(inequalities[i].i_lit) ||
integer_trail->IntegerLiteralIsFalse(inequalities[i].i_lit)) {
continue;
}
if (integer_trail->IntegerLiteralIsTrue(inequalities[i + 1].i_lit) ||
integer_trail->IntegerLiteralIsFalse(inequalities[i + 1].i_lit)) {
continue;
}
const auto pair_a = encoder->Canonicalize(inequalities[i].i_lit);
const auto pair_b = encoder->Canonicalize(inequalities[i + 1].i_lit);
if (pair_a.first == pair_b.second) {
++num_inequalities;
encoder->AssociateToIntegerLiteral(inequalities[i].literal,
inequalities[i].i_lit);
mapping->already_loaded_ct_.insert(inequalities[i].ct);
mapping->already_loaded_ct_.insert(inequalities[i + 1].ct);
}
}
// Encode the half-inequalities.
int num_half_inequalities = 0;
for (const auto inequality : inequalities) {
if (mapping->ConstraintIsAlreadyLoaded(inequality.ct)) continue;
m->Add(
Implication(inequality.literal,
encoder->GetOrCreateAssociatedLiteral(inequality.i_lit)));
if (sat_solver->ModelIsUnsat()) return;
++num_half_inequalities;
mapping->already_loaded_ct_.insert(inequality.ct);
mapping->is_half_encoding_ct_.insert(inequality.ct);
}
if (!inequalities.empty()) {
SOLVER_LOG(logger, "[Encoding] ", num_inequalities,
" literals associated to VAR >= value, and ",
num_half_inequalities, " half-associations.");
}
// Detect Literal <=> X == value and associate them in the IntegerEncoder.
//
// TODO(user): Fully encode variable that are almost fully encoded?
int num_equalities = 0;
int num_half_equalities = 0;
int num_fully_encoded = 0;
int num_partially_encoded = 0;
for (int i = 0; i < var_to_equalities.size(); ++i) {
std::vector<EqualityDetectionHelper>& encoding = var_to_equalities[i];
std::sort(encoding.begin(), encoding.end());
if (encoding.empty()) continue;
absl::flat_hash_set<int64_t> values;
const IntegerVariable var = mapping->integers_[i];
for (int j = 0; j + 1 < encoding.size(); j++) {
if ((encoding[j].value != encoding[j + 1].value) ||
(encoding[j].literal != encoding[j + 1].literal.Negated()) ||
(encoding[j].is_equality != true) ||
(encoding[j + 1].is_equality != false)) {
continue;
}
++num_equalities;
encoder->AssociateToIntegerEqualValue(encoding[j].literal, var,
IntegerValue(encoding[j].value));
mapping->already_loaded_ct_.insert(encoding[j].ct);
mapping->already_loaded_ct_.insert(encoding[j + 1].ct);
values.insert(encoding[j].value);
}
// TODO(user): Try to remove it. Normally we caught UNSAT above, but
// tests are very flaky (it only happens in parallel). Keeping it there for
// the time being.
if (sat_solver->ModelIsUnsat()) return;
if (time_limit->LimitReached()) return;
// Encode the half-equalities.
//
// TODO(user): delay this after PropagateEncodingFromEquivalenceRelations()?
// Otherwise we might create new Boolean variables for no reason. Note
// however, that in the presolve, we should only use the "representative" in
// linear constraints, so we should be fine.
for (const auto equality : encoding) {
if (mapping->ConstraintIsAlreadyLoaded(equality.ct)) continue;
if (equality.is_equality) {
// If we have just an half-equality, lets not create the <=> literal
// but just add two implications. If we don't create hole, we don't
// really need the reverse literal. This way it is also possible for
// the ExtractElementEncoding() to detect later that actually this
// literal is <=> to var == value, and this way we create one less
// Boolean for the same result.
//
// TODO(user): It is not 100% clear what is the best encoding and if
// we should create equivalent literal or rely on propagator instead
// to push bounds.
m->Add(Implication(
equality.literal,
encoder->GetOrCreateAssociatedLiteral(
IntegerLiteral::GreaterOrEqual(var, equality.value))));
m->Add(Implication(
equality.literal,
encoder->GetOrCreateAssociatedLiteral(
IntegerLiteral::LowerOrEqual(var, equality.value))));
} else {
const Literal eq = encoder->GetOrCreateLiteralAssociatedToEquality(
var, equality.value);
m->Add(Implication(equality.literal, eq.Negated()));
}
++num_half_equalities;
mapping->already_loaded_ct_.insert(equality.ct);
mapping->is_half_encoding_ct_.insert(equality.ct);
}
// Update stats.
if (encoder->VariableIsFullyEncoded(var)) {
++num_fully_encoded;
} else {
++num_partially_encoded;
}
}
if (num_equalities > 0 || num_half_equalities > 0) {
SOLVER_LOG(logger, "[Encoding] ", num_equalities,
" literals associated to VAR == value, and ",
num_half_equalities, " half-associations.");
}
if (num_fully_encoded > 0) {
SOLVER_LOG(logger,
"[Encoding] num_fully_encoded_variables:", num_fully_encoded);
}
if (num_partially_encoded > 0) {
SOLVER_LOG(logger, "[Encoding] num_partially_encoded_variables:",
num_partially_encoded);
}
}
void ExtractElementEncoding(const CpModelProto& model_proto, Model* m) {
int num_element_encoded = 0;
auto* mapping = m->GetOrCreate<CpModelMapping>();
auto* logger = m->GetOrCreate<SolverLogger>();
auto* encoder = m->GetOrCreate<IntegerEncoder>();
auto* sat_solver = m->GetOrCreate<SatSolver>();
auto* implied_bounds = m->GetOrCreate<ImpliedBounds>();
auto* element_encodings = m->GetOrCreate<ElementEncodings>();
// Scan all exactly_one constraints and look for literal => var == value to
// detect element encodings.
int num_support_clauses = 0;
int num_dedicated_propagator = 0;
std::vector<Literal> clause;
std::vector<Literal> selectors;
std::vector<AffineExpression> exprs;
std::vector<AffineExpression> negated_exprs;
for (int c = 0; c < model_proto.constraints_size(); ++c) {
const ConstraintProto& ct = model_proto.constraints(c);
if (ct.constraint_case() != ConstraintProto::kExactlyOne) continue;
// Project the implied values onto each integer variable.
absl::btree_map<IntegerVariable, std::vector<ValueLiteralPair>>
var_to_value_literal_list;
for (const int l : ct.exactly_one().literals()) {
const Literal literal = mapping->Literal(l);
for (const auto& var_value : implied_bounds->GetImpliedValues(literal)) {
var_to_value_literal_list[var_value.first].push_back(
{var_value.second, literal});
}
}
// Used for logging only.
std::vector<IntegerVariable> encoded_variables;
std::string encoded_variables_str;
// Search for variable fully covered by the literals of the exactly_one.
for (auto& [var, encoding] : var_to_value_literal_list) {
if (encoding.size() < ct.exactly_one().literals_size()) {
VLOG(2) << "X" << var.value() << " has " << encoding.size()
<< " implied values, and a domain of size "
<< m->GetOrCreate<IntegerTrail>()
->InitialVariableDomain(var)
.Size();
continue;
}
// We use the order of literals of the exactly_one.
++num_element_encoded;
element_encodings->Add(var, encoding, c);
if (VLOG_IS_ON(1)) {
encoded_variables.push_back(var);
absl::StrAppend(&encoded_variables_str, " X", var.value());
}
// Encode the holes propagation (but we don't create extra literal if they
// are not already there). If there are non-encoded values we also add the
// direct min/max propagation.
bool need_extra_propagation = false;
std::sort(encoding.begin(), encoding.end(),
ValueLiteralPair::CompareByValue());
for (int i = 0, j = 0; i < encoding.size(); i = j) {
j = i + 1;
const IntegerValue value = encoding[i].value;
while (j < encoding.size() && encoding[j].value == value) ++j;
if (j - i == 1) {
// Lets not create var >= value or var <= value if they do not exist.
if (!encoder->IsFixedOrHasAssociatedLiteral(
IntegerLiteral::GreaterOrEqual(var, value)) ||
!encoder->IsFixedOrHasAssociatedLiteral(
IntegerLiteral::LowerOrEqual(var, value))) {
need_extra_propagation = true;
continue;
}
encoder->AssociateToIntegerEqualValue(encoding[i].literal, var,
value);
} else {
// We do not create an extra literal if it doesn't exist.
if (encoder->GetAssociatedEqualityLiteral(var, value) ==
kNoLiteralIndex) {
need_extra_propagation = true;
continue;
}
// If all literal supporting a value are false, then the value must be
// false. Note that such a clause is only useful if there are more
// than one literal supporting the value, otherwise we should already
// have detected the equivalence.
++num_support_clauses;
clause.clear();
for (int k = i; k < j; ++k) clause.push_back(encoding[k].literal);
const Literal eq_lit =
encoder->GetOrCreateLiteralAssociatedToEquality(var, value);
clause.push_back(eq_lit.Negated());
// TODO(user): It should be safe otherwise the exactly_one will have
// duplicate literal, but I am not sure that if presolve is off we can
// assume that.
sat_solver->AddProblemClause(clause);
}
}
if (need_extra_propagation) {
++num_dedicated_propagator;
selectors.clear();
exprs.clear();
negated_exprs.clear();
for (const auto [value, literal] : encoding) {
selectors.push_back(literal);
exprs.push_back(AffineExpression(value));
negated_exprs.push_back(AffineExpression(-value));
}
auto* constraint =
new GreaterThanAtLeastOneOfPropagator(var, exprs, selectors, {}, m);
constraint->RegisterWith(m->GetOrCreate<GenericLiteralWatcher>());
m->TakeOwnership(constraint);
// And the <= side.
constraint = new GreaterThanAtLeastOneOfPropagator(
NegationOf(var), negated_exprs, selectors, {}, m);
constraint->RegisterWith(m->GetOrCreate<GenericLiteralWatcher>());
m->TakeOwnership(constraint);
}
}
if (encoded_variables.size() > 1 && VLOG_IS_ON(1)) {
VLOG(1) << "exactly_one(" << c << ") encodes " << encoded_variables.size()
<< " variables at the same time: " << encoded_variables_str;
}
}
if (num_element_encoded > 0) {
SOLVER_LOG(logger,
"[Encoding] num_element_encoding: ", num_element_encoded);
}
if (num_support_clauses > 0) {
SOLVER_LOG(logger, "[Encoding] Added ", num_support_clauses,
" element support clauses, and ", num_dedicated_propagator,
" dedicated propagators.");
}
}
void PropagateEncodingFromEquivalenceRelations(const CpModelProto& model_proto,
Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
auto* encoder = m->GetOrCreate<IntegerEncoder>();
auto* sat_solver = m->GetOrCreate<SatSolver>();
// Loop over all constraints and find affine ones.
int64_t num_associations = 0;
int64_t num_set_to_false = 0;
for (const ConstraintProto& ct : model_proto.constraints()) {
if (!ct.enforcement_literal().empty()) continue;
if (ct.constraint_case() != ConstraintProto::kLinear) continue;
if (ct.linear().vars_size() != 2) continue;
if (!ConstraintIsEq(ct.linear())) continue;
const IntegerValue rhs(ct.linear().domain(0));
// Make sure the coefficient are positive.
IntegerVariable var1 = mapping->Integer(ct.linear().vars(0));
IntegerVariable var2 = mapping->Integer(ct.linear().vars(1));
IntegerValue coeff1(ct.linear().coeffs(0));
IntegerValue coeff2(ct.linear().coeffs(1));
if (coeff1 < 0) {
var1 = NegationOf(var1);
coeff1 = -coeff1;
}
if (coeff2 < 0) {
var2 = NegationOf(var2);
coeff2 = -coeff2;
}
// TODO(user): This is not supposed to happen, but apparently it did on
// once on routing_GCM_0001_sat.fzn. Investigate and fix.
if (coeff1 == 0 || coeff2 == 0) continue;
// We first map the >= literals.
// It is important to do that first, since otherwise mapping a == literal
// might creates the underlying >= and <= literals.
for (int i = 0; i < 2; ++i) {
for (const auto [value1, literal1] :
encoder->PartialGreaterThanEncoding(var1)) {
const IntegerValue bound2 = FloorRatio(rhs - value1 * coeff1, coeff2);
++num_associations;
encoder->AssociateToIntegerLiteral(
literal1, IntegerLiteral::LowerOrEqual(var2, bound2));
}
std::swap(var1, var2);
std::swap(coeff1, coeff2);
}
// Same for the == literals.
//
// TODO(user): This is similar to LoadEquivalenceAC() for unreified
// constraints, but when the later is called, more encoding might have taken
// place.
for (int i = 0; i < 2; ++i) {
const auto copy = encoder->PartialDomainEncoding(var1);
for (const auto value_literal : copy) {
const IntegerValue value1 = value_literal.value;
const IntegerValue intermediate = rhs - value1 * coeff1;
if (intermediate % coeff2 != 0) {
// Using this function deals properly with UNSAT.
++num_set_to_false;
if (!sat_solver->AddUnitClause(value_literal.literal.Negated())) {
return;
}
continue;
}
++num_associations;
encoder->AssociateToIntegerEqualValue(value_literal.literal, var2,
intermediate / coeff2);
}
std::swap(var1, var2);
std::swap(coeff1, coeff2);
}
}
if (num_associations > 0) {
VLOG(1) << "Num associations from equivalences = " << num_associations;
}
if (num_set_to_false > 0) {
VLOG(1) << "Num literals set to false from equivalences = "
<< num_set_to_false;
}
}
void DetectOptionalVariables(const CpModelProto& model_proto, Model* m) {
const SatParameters& parameters = *(m->GetOrCreate<SatParameters>());
if (!parameters.use_optional_variables()) return;
if (parameters.enumerate_all_solutions()) return;
// The variables from the objective cannot be marked as optional!
const int num_proto_variables = model_proto.variables_size();
std::vector<bool> already_seen(num_proto_variables, false);
if (model_proto.has_objective()) {
for (const int ref : model_proto.objective().vars()) {
already_seen[PositiveRef(ref)] = true;
}
}
// Compute for each variables the intersection of the enforcement literals
// of the constraints in which they appear.
//
// TODO(user): This deals with the simplest cases, but we could try to
// detect literals that implies all the constraints in which a variable
// appear to false. This can be done with a LCA computation in the tree of
// Boolean implication (once the presolve remove cycles). Not sure if we can
// properly exploit that afterwards though. Do some research!
std::vector<std::vector<int>> enforcement_intersection(num_proto_variables);
absl::btree_set<int> literals_set;
for (int c = 0; c < model_proto.constraints_size(); ++c) {
const ConstraintProto& ct = model_proto.constraints(c);
if (ct.enforcement_literal().empty()) {
for (const int var : UsedVariables(ct)) {
already_seen[var] = true;
enforcement_intersection[var].clear();
}
} else {
literals_set.clear();
literals_set.insert(ct.enforcement_literal().begin(),
ct.enforcement_literal().end());
for (const int var : UsedVariables(ct)) {
if (!already_seen[var]) {
enforcement_intersection[var].assign(ct.enforcement_literal().begin(),
ct.enforcement_literal().end());
} else {
// Take the intersection.
std::vector<int>& vector_ref = enforcement_intersection[var];
int new_size = 0;
for (const int literal : vector_ref) {
if (literals_set.contains(literal)) {
vector_ref[new_size++] = literal;
}
}
vector_ref.resize(new_size);
}
already_seen[var] = true;
}
}
}
// Auto-detect optional variables.
int num_optionals = 0;
for (int var = 0; var < num_proto_variables; ++var) {
const IntegerVariableProto& var_proto = model_proto.variables(var);
const int64_t min = var_proto.domain(0);
const int64_t max = var_proto.domain(var_proto.domain().size() - 1);
if (min == max) continue;
if (min == 0 && max == 1) continue;
if (enforcement_intersection[var].empty()) continue;
++num_optionals;
}
if (num_optionals > 0) {
SOLVER_LOG(m->GetOrCreate<SolverLogger>(), "Auto-detected ", num_optionals,
" optional variables. Note that for now we DO NOT do anything "
"with this information.");
}
}
void AddFullEncodingFromSearchBranching(const CpModelProto& model_proto,
Model* m) {
if (model_proto.search_strategy().empty()) return;
auto* mapping = m->GetOrCreate<CpModelMapping>();
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
for (const DecisionStrategyProto& strategy : model_proto.search_strategy()) {
if (strategy.domain_reduction_strategy() ==
DecisionStrategyProto::SELECT_MEDIAN_VALUE) {
for (const LinearExpressionProto& expr : strategy.exprs()) {
const int var = expr.vars(0);
if (!mapping->IsInteger(var)) continue;
const IntegerVariable variable = mapping->Integer(var);
if (!integer_trail->IsFixed(variable)) {
m->Add(FullyEncodeVariable(variable));
}
}
}
}
}
// ============================================================================
// Constraint loading functions.
// ============================================================================
void LoadBoolOrConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
auto* sat_solver = m->GetOrCreate<SatSolver>();
std::vector<Literal> literals = mapping->Literals(ct.bool_or().literals());
for (const int ref : ct.enforcement_literal()) {
literals.push_back(mapping->Literal(ref).Negated());
}
sat_solver->AddProblemClause(literals);
if (literals.size() == 3) {
m->GetOrCreate<ProductDetector>()->ProcessTernaryClause(literals);
}
}
void LoadBoolAndConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
std::vector<Literal> literals;
for (const int ref : ct.enforcement_literal()) {
literals.push_back(mapping->Literal(ref).Negated());
}
auto* sat_solver = m->GetOrCreate<SatSolver>();
for (const Literal literal : mapping->Literals(ct.bool_and().literals())) {
literals.push_back(literal);
sat_solver->AddProblemClause(literals);
literals.pop_back();
}
}
void LoadAtMostOneConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
auto* implications = m->GetOrCreate<BinaryImplicationGraph>();
CHECK(!HasEnforcementLiteral(ct)) << "Not supported.";
if (!implications->AddAtMostOne(
mapping->Literals(ct.at_most_one().literals()))) {
m->GetOrCreate<SatSolver>()->NotifyThatModelIsUnsat();
}
}
void LoadExactlyOneConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
CHECK(!HasEnforcementLiteral(ct)) << "Not supported.";
const auto& literals = mapping->Literals(ct.exactly_one().literals());
m->Add(ExactlyOneConstraint(literals));
if (literals.size() == 3) {
m->GetOrCreate<ProductDetector>()->ProcessTernaryExactlyOne(literals);
}
}
void LoadBoolXorConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
m->Add(LiteralXorIs(mapping->Literals(ct.enforcement_literal()),
mapping->Literals(ct.bool_xor().literals()), true));
}
namespace {
// Boolean encoding of:
// enforcement_literal => coeff1 * var1 + coeff2 * var2 == rhs;
void LoadEquivalenceAC(const std::vector<Literal> enforcement_literal,
IntegerValue coeff1, IntegerVariable var1,
IntegerValue coeff2, IntegerVariable var2,
const IntegerValue rhs, Model* m) {
auto* encoder = m->GetOrCreate<IntegerEncoder>();
CHECK(encoder->VariableIsFullyEncoded(var1));
CHECK(encoder->VariableIsFullyEncoded(var2));
absl::flat_hash_map<IntegerValue, Literal> term1_value_to_literal;
for (const auto value_literal : encoder->FullDomainEncoding(var1)) {
term1_value_to_literal[coeff1 * value_literal.value] =
value_literal.literal;
}
const auto copy = encoder->FullDomainEncoding(var2);
for (const auto value_literal : copy) {
const IntegerValue target = rhs - value_literal.value * coeff2;
if (!term1_value_to_literal.contains(target)) {
m->Add(EnforcedClause(enforcement_literal,
{value_literal.literal.Negated()}));
} else {
const Literal target_literal = term1_value_to_literal[target];
m->Add(EnforcedClause(enforcement_literal,
{value_literal.literal.Negated(), target_literal}));
m->Add(EnforcedClause(enforcement_literal,
{value_literal.literal, target_literal.Negated()}));
// This "target" can never be reached again, so it is safe to remove it.
// We do that so we know the term1 values that are never reached.
term1_value_to_literal.erase(target);
}
}
// Exclude the values that can never be "matched" by coeff2 * var2.
// We need the std::sort() to be deterministic!
std::vector<Literal> implied_false;
for (const auto entry : term1_value_to_literal) {
implied_false.push_back(entry.second);
}
std::sort(implied_false.begin(), implied_false.end());
for (const Literal l : implied_false) {
m->Add(EnforcedClause(enforcement_literal, {l.Negated()}));
}
}
// Boolean encoding of:
// enforcement_literal => coeff1 * var1 + coeff2 * var2 != rhs;
void LoadEquivalenceNeqAC(const std::vector<Literal> enforcement_literal,
IntegerValue coeff1, IntegerVariable var1,
IntegerValue coeff2, IntegerVariable var2,
const IntegerValue rhs, Model* m) {
auto* encoder = m->GetOrCreate<IntegerEncoder>();
CHECK(encoder->VariableIsFullyEncoded(var1));
CHECK(encoder->VariableIsFullyEncoded(var2));
absl::flat_hash_map<IntegerValue, Literal> term1_value_to_literal;
for (const auto value_literal : encoder->FullDomainEncoding(var1)) {
term1_value_to_literal[coeff1 * value_literal.value] =
value_literal.literal;
}
const auto copy = encoder->FullDomainEncoding(var2);
for (const auto value_literal : copy) {
const IntegerValue target_value = rhs - value_literal.value * coeff2;
const auto& it = term1_value_to_literal.find(target_value);
if (it != term1_value_to_literal.end()) {
const Literal target_literal = it->second;
m->Add(EnforcedClause(
enforcement_literal,
{value_literal.literal.Negated(), target_literal.Negated()}));
}
}
}
bool IsPartOfProductEncoding(const ConstraintProto& ct) {
if (ct.enforcement_literal().size() != 1) return false;
if (ct.linear().vars().size() > 2) return false;
if (ct.linear().domain().size() != 2) return false;
if (ct.linear().domain(0) != 0) return false;
if (ct.linear().domain(1) != 0) return false;
for (const int64_t coeff : ct.linear().coeffs()) {
if (std::abs(coeff) != 1) return false;
}
return true;
}
} // namespace
void SplitAndLoadIntermediateConstraints(bool lb_required, bool ub_required,
std::vector<IntegerVariable>* vars,
std::vector<int64_t>* coeffs,
Model* m) {
// If we enumerate all solutions, then we want intermediate variables to be
// tight independently of what side is required.
if (m->GetOrCreate<SatParameters>()->enumerate_all_solutions()) {
lb_required = true;
ub_required = true;
}
// We sort by absolute value of coefficients. The separate - from +, and then
// by variable order, usually variable with the same "meaning" are defined
// together in a model.
const int num_terms = vars->size();
std::vector<std::pair<IntegerVariable, int64_t>> terms;
{
terms.reserve(num_terms);
for (int i = 0; i < num_terms; ++i) {
terms.push_back({(*vars)[i], (*coeffs)[i]});
}
std::sort(terms.begin(), terms.end(),
[](const std::pair<IntegerVariable, int64_t> a,
const std::pair<IntegerVariable, int64_t> b) {
const int64_t abs_coeff_a = std::abs(a.second);
const int64_t abs_coeff_b = std::abs(b.second);
if (abs_coeff_a != abs_coeff_b) {
return abs_coeff_a < abs_coeff_b;
}
if (a.second != b.second) {
return a.second < b.second;
}
return a.first < b.first;
});
}
std::vector<int64_t> sorted_coeffs;
sorted_coeffs.resize(num_terms);
for (int i = 0; i < num_terms; ++i) {
sorted_coeffs[i] = terms[i].second;
}
const std::vector<std::pair<int, int>> buckets =
HeuristicallySplitLongLinear(sorted_coeffs);
std::vector<IntegerVariable> bucket_sum_vars;
std::vector<int64_t> bucket_sum_coeffs;
std::vector<IntegerVariable> local_vars;
std::vector<int64_t> local_coeffs;
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
for (const auto [start, size] : buckets) {
// Just keep the same variable if the size of that bucket is one.
if (size == 1) {
const auto [var, coeff] = terms[start];
bucket_sum_vars.push_back(var);
bucket_sum_coeffs.push_back(coeff);
continue;
}
local_vars.clear();
local_coeffs.clear();
int64_t bucket_lb = 0;
int64_t bucket_ub = 0;
int64_t gcd = 0;
for (int i = 0; i < size; ++i) {
const auto [var, coeff] = terms[start + i];
gcd = std::gcd(gcd, std::abs(coeff));
local_vars.push_back(var);
local_coeffs.push_back(coeff);
const int64_t term1 = coeff * integer_trail->LowerBound(var).value();
const int64_t term2 = coeff * integer_trail->UpperBound(var).value();
bucket_lb += std::min(term1, term2);
bucket_ub += std::max(term1, term2);
}
if (gcd == 0) continue;
if (gcd > 1) {
// Everything should be exactly divisible!
for (int64_t& ref : local_coeffs) ref /= gcd;
bucket_lb /= gcd;
bucket_ub /= gcd;
}
const IntegerVariable bucket_sum =
integer_trail->AddIntegerVariable(bucket_lb, bucket_ub);
bucket_sum_vars.push_back(bucket_sum);
bucket_sum_coeffs.push_back(gcd);
local_vars.push_back(bucket_sum);
local_coeffs.push_back(-1);
if (lb_required) {
// We have sum bucket_var >= lb, so we need local_vars >= bucket_var.
m->Add(WeightedSumGreaterOrEqual(local_vars, local_coeffs, 0));
}
if (ub_required) {
// Similarly, bucket_var <= ub, so we need local_vars <= bucket_var
m->Add(WeightedSumLowerOrEqual(local_vars, local_coeffs, 0));
}
}
// Rewrite the constraint.
*vars = bucket_sum_vars;
*coeffs = bucket_sum_coeffs;
}
void LoadLinearConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
if (ct.linear().vars().empty()) {
const Domain rhs = ReadDomainFromProto(ct.linear());
if (rhs.Contains(0)) return;
if (HasEnforcementLiteral(ct)) {
std::vector<Literal> clause;
for (const int ref : ct.enforcement_literal()) {
clause.push_back(mapping->Literal(ref).Negated());
}
m->Add(ClauseConstraint(clause));
} else {
VLOG(1) << "Trivially UNSAT constraint: " << ct;
m->GetOrCreate<SatSolver>()->NotifyThatModelIsUnsat();
}
return;
}
if (IsPartOfProductEncoding(ct)) {
const Literal l = mapping->Literal(ct.enforcement_literal(0));
auto* detector = m->GetOrCreate<ProductDetector>();
if (ct.linear().vars().size() == 1) {
// TODO(user): Actually this should never be called since we process
// linear1 in ExtractEncoding().
detector->ProcessConditionalZero(l,
mapping->Integer(ct.linear().vars(0)));
} else if (ct.linear().vars().size() == 2) {
const IntegerVariable x = mapping->Integer(ct.linear().vars(0));
const IntegerVariable y = mapping->Integer(ct.linear().vars(1));
detector->ProcessConditionalEquality(
l, x,
ct.linear().coeffs(0) == ct.linear().coeffs(1) ? NegationOf(y) : y);
}
}
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
std::vector<IntegerVariable> vars = mapping->Integers(ct.linear().vars());
std::vector<int64_t> coeffs = ValuesFromProto(ct.linear().coeffs());
// Compute the min/max to relax the bounds if needed.
//
// TODO(user): Reuse ComputeLinearBounds()? but then we need another loop
// to detect if we only have Booleans.
IntegerValue min_sum(0);
IntegerValue max_sum(0);
IntegerValue max_domain_size(0);
bool all_booleans = true;
for (int i = 0; i < vars.size(); ++i) {
if (all_booleans && !mapping->IsBoolean(ct.linear().vars(i))) {
all_booleans = false;
}
const IntegerValue lb = integer_trail->LowerBound(vars[i]);
const IntegerValue ub = integer_trail->UpperBound(vars[i]);
max_domain_size = std::max(max_domain_size, ub - lb + 1);
const IntegerValue term_a = coeffs[i] * lb;
const IntegerValue term_b = coeffs[i] * ub;
min_sum += std::min(term_a, term_b);
max_sum += std::max(term_a, term_b);
}
// Load precedences.
if (!HasEnforcementLiteral(ct)) {
auto* root_level_lin2_bounds = m->GetOrCreate<RootLevelLinear2Bounds>();
// To avoid overflow in the code below, we tighten the bounds.
// Note that we detect and do not add trivial relation.
int64_t rhs_min = ct.linear().domain(0);
int64_t rhs_max = ct.linear().domain(ct.linear().domain().size() - 1);
rhs_min = std::max(rhs_min, min_sum.value());
rhs_max = std::min(rhs_max, max_sum.value());
if (vars.size() == 2) {
LinearExpression2 expr(vars[0], vars[1], coeffs[0], coeffs[1]);
root_level_lin2_bounds->Add(expr, rhs_min, rhs_max);
} else if (vars.size() == 3) {
// TODO(user): This is a weaker duplication of the logic of
// BinaryRelationsMaps, but is is useful for the transitive closure in
// PrecedenceRelations::Build(). Replace this by getting the
// BinaryRelationsMaps affine bounds at level zero.
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
if (i == j) continue;
const int other = 3 - i - j; // i + j + other = 0 + 1 + 2.
const int64_t coeff = coeffs[other];
const int64_t other_lb =
coeff > 0
? coeff * integer_trail->LowerBound(vars[other]).value()
: coeff * integer_trail->UpperBound(vars[other]).value();
const int64_t other_ub =
coeff > 0
? coeff * integer_trail->UpperBound(vars[other]).value()
: coeff * integer_trail->LowerBound(vars[other]).value();
LinearExpression2 expr(vars[i], vars[j], coeffs[i], coeffs[j]);
root_level_lin2_bounds->Add(expr, rhs_min - other_ub,
rhs_max - other_lb);
}
}
}
}
const SatParameters& params = *m->GetOrCreate<SatParameters>();
const IntegerValue domain_size_limit(
params.max_domain_size_when_encoding_eq_neq_constraints());
if (ct.linear().vars_size() == 2 && !integer_trail->IsFixed(vars[0]) &&
!integer_trail->IsFixed(vars[1]) &&
max_domain_size <= domain_size_limit) {
auto* encoder = m->GetOrCreate<IntegerEncoder>();
if (params.boolean_encoding_level() > 0 && ConstraintIsEq(ct.linear()) &&
ct.linear().domain(0) != min_sum && ct.linear().domain(0) != max_sum &&
encoder->VariableIsFullyEncoded(vars[0]) &&
encoder->VariableIsFullyEncoded(vars[1])) {
VLOG(3) << "Load AC version of " << ct << ", var0 domain = "
<< integer_trail->InitialVariableDomain(vars[0])
<< ", var1 domain = "
<< integer_trail->InitialVariableDomain(vars[1]);
return LoadEquivalenceAC(mapping->Literals(ct.enforcement_literal()),
IntegerValue(coeffs[0]), vars[0],
IntegerValue(coeffs[1]), vars[1],
IntegerValue(ct.linear().domain(0)), m);
}
int64_t single_value = 0;
if (params.boolean_encoding_level() > 0 &&
ConstraintIsNEq(ct.linear(), mapping, integer_trail, &single_value) &&
single_value != min_sum && single_value != max_sum &&
encoder->VariableIsFullyEncoded(vars[0]) &&
encoder->VariableIsFullyEncoded(vars[1])) {
VLOG(3) << "Load NAC version of " << ct << ", var0 domain = "
<< integer_trail->InitialVariableDomain(vars[0])
<< ", var1 domain = "
<< integer_trail->InitialVariableDomain(vars[1])
<< ", value = " << single_value;
return LoadEquivalenceNeqAC(mapping->Literals(ct.enforcement_literal()),
IntegerValue(coeffs[0]), vars[0],
IntegerValue(coeffs[1]), vars[1],
IntegerValue(single_value), m);
}
}
// Note that the domain/enforcement of the main constraint do not change.
// Same for the min/sum and max_sum. The intermediate variables are always
// equal to the intermediate sum, independently of the enforcement.
const bool pseudo_boolean = ct.linear().domain_size() == 2 && all_booleans;
if (!pseudo_boolean &&
ct.linear().vars().size() > params.linear_split_size()) {
const auto& domain = ct.linear().domain();
SplitAndLoadIntermediateConstraints(
domain.size() > 2 || min_sum < domain[0],
domain.size() > 2 || max_sum > domain[1], &vars, &coeffs, m);
}
if (ct.linear().domain_size() == 2) {
const int64_t lb = ct.linear().domain(0);
const int64_t ub = ct.linear().domain(1);
std::vector<Literal> enforcement_literals =
mapping->Literals(ct.enforcement_literal());
if (all_booleans) {
std::vector<LiteralWithCoeff> cst;
for (int i = 0; i < vars.size(); ++i) {
const int ref = ct.linear().vars(i);
cst.push_back({mapping->Literal(ref), coeffs[i]});
}
m->GetOrCreate<SatSolver>()->AddLinearConstraint(
/*use_lower_bound=*/(min_sum < lb), lb,
/*use_upper_bound=*/(max_sum > ub), ub, &enforcement_literals, &cst);
} else {
if (min_sum < lb) {
AddWeightedSumGreaterOrEqual(enforcement_literals, vars, coeffs, lb, m);
}
if (max_sum > ub) {
AddWeightedSumLowerOrEqual(enforcement_literals, vars, coeffs, ub, m);
}
}
return;
}
// We have a linear with a complex Domain, we need to create extra Booleans.
// For enforcement => var \in domain, we can potentially reuse the encoding
// literal directly rather than creating new ones.
const bool is_linear1 = vars.size() == 1 && coeffs[0] == 1;
bool special_case = false;
std::vector<Literal> clause;
std::vector<Literal> for_enumeration;
auto* encoding = m->GetOrCreate<IntegerEncoder>();
const int domain_size = ct.linear().domain_size();
for (int i = 0; i < domain_size; i += 2) {
const int64_t lb = ct.linear().domain(i);
const int64_t ub = ct.linear().domain(i + 1);
// Skip non-reachable intervals.
if (min_sum > ub) continue;
if (max_sum < lb) continue;
// Skip trivial constraint. Note that when this happens, all the intervals
// before where non-reachable.
if (min_sum >= lb && max_sum <= ub) return;
if (is_linear1) {
if (lb == ub) {
clause.push_back(
encoding->GetOrCreateLiteralAssociatedToEquality(vars[0], lb));
continue;
} else if (min_sum >= lb) {
clause.push_back(encoding->GetOrCreateAssociatedLiteral(
IntegerLiteral::LowerOrEqual(vars[0], ub)));
continue;
} else if (max_sum <= ub) {
clause.push_back(encoding->GetOrCreateAssociatedLiteral(
IntegerLiteral::GreaterOrEqual(vars[0], lb)));
continue;
}
}
// If there is just two terms and no enforcement, we don't need to create an
// extra boolean as the second case can be controlled by the negation of the
// first.
if (ct.enforcement_literal().empty() && clause.size() == 1 &&
i + 1 == domain_size) {
special_case = true;
}
const Literal subdomain_literal(
special_case ? clause.back().Negated()
: Literal(m->Add(NewBooleanVariable()), true));
clause.push_back(subdomain_literal);
for_enumeration.push_back(subdomain_literal);
if (min_sum < lb) {
AddWeightedSumGreaterOrEqual({subdomain_literal}, vars, coeffs, lb, m);
}
if (max_sum > ub) {
AddWeightedSumLowerOrEqual({subdomain_literal}, vars, coeffs, ub, m);
}
}
const std::vector<Literal> enforcement_literals =
mapping->Literals(ct.enforcement_literal());
// Make sure all booleans are tights when enumerating all solutions.
if (params.enumerate_all_solutions() && !enforcement_literals.empty()) {
Literal linear_is_enforced;
if (enforcement_literals.size() == 1) {
linear_is_enforced = enforcement_literals[0];
} else {
linear_is_enforced = Literal(m->Add(NewBooleanVariable()), true);
std::vector<Literal> maintain_linear_is_enforced;
for (const Literal e_lit : enforcement_literals) {
m->Add(Implication(e_lit.Negated(), linear_is_enforced.Negated()));
maintain_linear_is_enforced.push_back(e_lit.Negated());
}
maintain_linear_is_enforced.push_back(linear_is_enforced);
m->Add(ClauseConstraint(maintain_linear_is_enforced));
}
for (const Literal lit : for_enumeration) {
m->Add(Implication(linear_is_enforced.Negated(), lit.Negated()));
if (special_case) break; // For the unique Boolean var to be false.
}
}
if (!special_case) {
for (const Literal e_lit : enforcement_literals) {
clause.push_back(e_lit.Negated());
}
m->Add(ClauseConstraint(clause));
}
}
void LoadAllDiffConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
const std::vector<Literal> enforcement_literals =
mapping->Literals(ct.enforcement_literal());
const std::vector<AffineExpression> expressions =
mapping->Affines(ct.all_diff().exprs());
m->Add(AllDifferentOnBounds(enforcement_literals, expressions));
}
void LoadAlwaysFalseConstraint(const ConstraintProto& ct, Model* m) {
if (ct.enforcement_literal().empty()) {
m->GetOrCreate<SatSolver>()->NotifyThatModelIsUnsat();
}
ConstraintProto new_ct = ct;
BoolArgumentProto& bool_or = *new_ct.mutable_bool_or();
for (const int literal : ct.enforcement_literal()) {
bool_or.add_literals(NegatedRef(literal));
}
LoadBoolOrConstraint(new_ct, m);
}
void LoadIntProdConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
const std::vector<Literal> enforcement_literals =
mapping->Literals(ct.enforcement_literal());
const AffineExpression prod = mapping->Affine(ct.int_prod().target());
std::vector<AffineExpression> terms;
for (const LinearExpressionProto& expr : ct.int_prod().exprs()) {
terms.push_back(mapping->Affine(expr));
}
switch (terms.size()) {
case 0: {
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
if (prod.IsConstant()) {
if (prod.constant.value() != 1) {
LoadAlwaysFalseConstraint(ct, m);
}
} else if (enforcement_literals.empty()) {
if (!integer_trail->Enqueue(prod.LowerOrEqual(1)) ||
!integer_trail->Enqueue(prod.GreaterOrEqual(1))) {
m->GetOrCreate<SatSolver>()->NotifyThatModelIsUnsat();
}
} else {
LinearConstraintBuilder builder(m, /*lb=*/1, /*ub=*/1);
builder.AddTerm(prod, 1);
LoadConditionalLinearConstraint(enforcement_literals, builder.Build(),
m);
}
break;
}
case 1: {
LinearConstraintBuilder builder(m, /*lb=*/0, /*ub=*/0);
builder.AddTerm(prod, 1);
builder.AddTerm(terms[0], -1);
LoadConditionalLinearConstraint(enforcement_literals, builder.Build(), m);
break;
}
case 2: {
m->Add(ProductConstraint(enforcement_literals, terms[0], terms[1], prod));
break;
}
default: {
LOG(FATAL) << "Loading int_prod with arity > 2, should not be here.";
break;
}
}
}
void LoadIntDivConstraint(const ConstraintProto& ct, Model* m) {
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
auto* mapping = m->GetOrCreate<CpModelMapping>();
const std::vector<Literal> enforcement_literals =
mapping->Literals(ct.enforcement_literal());
const AffineExpression div = mapping->Affine(ct.int_div().target());
const AffineExpression num = mapping->Affine(ct.int_div().exprs(0));
const AffineExpression denom = mapping->Affine(ct.int_div().exprs(1));
if (integer_trail->IsFixed(denom)) {
m->Add(FixedDivisionConstraint(enforcement_literals, num,
integer_trail->FixedValue(denom), div));
} else {
if (VLOG_IS_ON(1)) {
LinearConstraintBuilder builder(m);
if (m->GetOrCreate<ProductDecomposer>()->TryToLinearize(num, denom,
&builder)) {
VLOG(1) << "Division " << ct << " can be linearized";
}
}
m->Add(DivisionConstraint(enforcement_literals, num, denom, div));
}
}
void LoadIntModConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
const std::vector<Literal> enforcement_literals =
mapping->Literals(ct.enforcement_literal());
const AffineExpression target = mapping->Affine(ct.int_mod().target());
const AffineExpression expr = mapping->Affine(ct.int_mod().exprs(0));
const AffineExpression mod = mapping->Affine(ct.int_mod().exprs(1));
CHECK(integer_trail->IsFixed(mod));
const IntegerValue fixed_modulo = integer_trail->FixedValue(mod);
m->Add(
FixedModuloConstraint(enforcement_literals, expr, fixed_modulo, target));
}
void LoadLinMaxConstraint(const ConstraintProto& ct, Model* m) {
if (ct.lin_max().exprs().empty()) {
LoadAlwaysFalseConstraint(ct, m);
return;
}
auto* mapping = m->GetOrCreate<CpModelMapping>();
const LinearExpression max = mapping->GetExprFromProto(ct.lin_max().target());
std::vector<LinearExpression> negated_exprs;
negated_exprs.reserve(ct.lin_max().exprs_size());
for (int i = 0; i < ct.lin_max().exprs_size(); ++i) {
negated_exprs.push_back(
NegationOf(mapping->GetExprFromProto(ct.lin_max().exprs(i))));
}
// TODO(user): Consider replacing the min propagator by max.
AddIsEqualToMinOf(mapping->Literals(ct.enforcement_literal()),
NegationOf(max), negated_exprs, m);
}
void LoadNoOverlapConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
AddDisjunctive(mapping->Literals(ct.enforcement_literal()),
mapping->Intervals(ct.no_overlap().intervals()), m);
}
void LoadNoOverlap2dConstraint(const ConstraintProto& ct, Model* m) {
if (ct.no_overlap_2d().x_intervals().empty()) return;
auto* mapping = m->GetOrCreate<CpModelMapping>();
const std::vector<Literal> enforcement_literals =
mapping->Literals(ct.enforcement_literal());
const std::vector<IntervalVariable> x_intervals =
mapping->Intervals(ct.no_overlap_2d().x_intervals());
const std::vector<IntervalVariable> y_intervals =
mapping->Intervals(ct.no_overlap_2d().y_intervals());
AddNonOverlappingRectangles(enforcement_literals, x_intervals, y_intervals,
m);
}
void LoadCumulativeConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
const std::vector<Literal> enforcement_literals =
mapping->Literals(ct.enforcement_literal());
const std::vector<IntervalVariable> intervals =
mapping->Intervals(ct.cumulative().intervals());
const AffineExpression capacity = mapping->Affine(ct.cumulative().capacity());
const std::vector<AffineExpression> demands =
mapping->Affines(ct.cumulative().demands());
m->Add(Cumulative(enforcement_literals, intervals, demands, capacity));
}
void LoadReservoirConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
auto* encoder = m->GetOrCreate<IntegerEncoder>();
const std::vector<Literal> enforcement_literals =
mapping->Literals(ct.enforcement_literal());
const std::vector<AffineExpression> times =
mapping->Affines(ct.reservoir().time_exprs());
const std::vector<AffineExpression> level_changes =
mapping->Affines(ct.reservoir().level_changes());
std::vector<Literal> presences;
const int size = ct.reservoir().time_exprs().size();
for (int i = 0; i < size; ++i) {
if (!ct.reservoir().active_literals().empty()) {
presences.push_back(mapping->Literal(ct.reservoir().active_literals(i)));
} else {
presences.push_back(encoder->GetTrueLiteral());
}
}
AddReservoirConstraint(enforcement_literals, times, level_changes, presences,
ct.reservoir().min_level(), ct.reservoir().max_level(),
m);
}
void LoadCircuitConstraint(const ConstraintProto& ct, Model* m) {
const auto& circuit = ct.circuit();
if (circuit.tails().empty()) return;
std::vector<int> tails(circuit.tails().begin(), circuit.tails().end());
std::vector<int> heads(circuit.heads().begin(), circuit.heads().end());
auto* mapping = m->GetOrCreate<CpModelMapping>();
const std::vector<Literal> enforcement_literals =
mapping->Literals(ct.enforcement_literal());
const std::vector<Literal> literals = mapping->Literals(circuit.literals());
const int num_nodes = ReindexArcs(&tails, &heads);
LoadSubcircuitConstraint(num_nodes, tails, heads, enforcement_literals,
literals, m);
}
void LoadRoutesConstraint(const ConstraintProto& ct, Model* m) {
const auto& routes = ct.routes();
if (routes.tails().empty()) return;
std::vector<int> tails(routes.tails().begin(), routes.tails().end());
std::vector<int> heads(routes.heads().begin(), routes.heads().end());
auto* mapping = m->GetOrCreate<CpModelMapping>();
const std::vector<Literal> enforcement_literals =
mapping->Literals(ct.enforcement_literal());
std::vector<Literal> literals = mapping->Literals(routes.literals());
const int num_nodes = ReindexArcs(&tails, &heads);
LoadSubcircuitConstraint(num_nodes, tails, heads, enforcement_literals,
literals, m,
/*multiple_subcircuit_through_zero=*/true);
}
bool LoadConstraint(const ConstraintProto& ct, Model* m) {
switch (ct.constraint_case()) {
case ConstraintProto::ConstraintCase::CONSTRAINT_NOT_SET:
return true;
case ConstraintProto::ConstraintCase::kBoolOr:
LoadBoolOrConstraint(ct, m);
return true;
case ConstraintProto::ConstraintCase::kBoolAnd:
LoadBoolAndConstraint(ct, m);
return true;
case ConstraintProto::ConstraintCase::kAtMostOne:
LoadAtMostOneConstraint(ct, m);
return true;
case ConstraintProto::ConstraintCase::kExactlyOne:
LoadExactlyOneConstraint(ct, m);
return true;
case ConstraintProto::ConstraintCase::kBoolXor:
LoadBoolXorConstraint(ct, m);
return true;
case ConstraintProto::ConstraintProto::kLinear:
LoadLinearConstraint(ct, m);
return true;
case ConstraintProto::ConstraintProto::kAllDiff:
LoadAllDiffConstraint(ct, m);
return true;
case ConstraintProto::ConstraintProto::kIntProd:
LoadIntProdConstraint(ct, m);
return true;
case ConstraintProto::ConstraintProto::kIntDiv:
LoadIntDivConstraint(ct, m);
return true;
case ConstraintProto::ConstraintProto::kIntMod:
LoadIntModConstraint(ct, m);
return true;
case ConstraintProto::ConstraintProto::kLinMax:
LoadLinMaxConstraint(ct, m);
return true;
case ConstraintProto::ConstraintProto::kInterval:
// Already dealt with.
return true;
case ConstraintProto::ConstraintProto::kNoOverlap:
LoadNoOverlapConstraint(ct, m);
return true;
case ConstraintProto::ConstraintProto::kNoOverlap2D:
LoadNoOverlap2dConstraint(ct, m);
return true;
case ConstraintProto::ConstraintProto::kCumulative:
LoadCumulativeConstraint(ct, m);
return true;
case ConstraintProto::ConstraintProto::kReservoir:
LoadReservoirConstraint(ct, m);
return true;
case ConstraintProto::ConstraintProto::kCircuit:
LoadCircuitConstraint(ct, m);
return true;
case ConstraintProto::ConstraintProto::kRoutes:
LoadRoutesConstraint(ct, m);
return true;
default:
return false;
}
}
} // namespace sat
} // namespace operations_research
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