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ortools-clone/ortools/sat/cp_model_loader.cc

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// Copyright 2010-2021 Google LLC
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "ortools/sat/cp_model_loader.h"
#include <algorithm>
#include <cstdint>
#include <limits>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "absl/container/btree_set.h"
#include "absl/container/flat_hash_map.h"
#include "absl/container/flat_hash_set.h"
#include "absl/meta/type_traits.h"
#include "absl/strings/str_cat.h"
#include "absl/types/span.h"
#include "ortools/algorithms/sparse_permutation.h"
#include "ortools/base/logging.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/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_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/sat_base.h"
#include "ortools/sat/sat_parameters.pb.h"
#include "ortools/sat/sat_solver.h"
#include "ortools/sat/symmetry.h"
#include "ortools/util/logging.h"
#include "ortools/util/sorted_interval_list.h"
#include "ortools/util/strong_integers.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());
}
void ComputeLinearBounds(const LinearConstraintProto& proto,
CpModelMapping* mapping, IntegerTrail* integer_trail,
int64_t* sum_min, int64_t* sum_max) {
*sum_min = 0;
*sum_max = 0;
for (int i = 0; i < proto.vars_size(); ++i) {
const int64_t coeff = proto.coeffs(i);
const IntegerVariable var = mapping->Integer(proto.vars(i));
const int64_t lb = integer_trail->LowerBound(var).value();
const int64_t ub = integer_trail->UpperBound(var).value();
if (coeff >= 0) {
(*sum_min) += coeff * lb;
(*sum_max) += coeff * ub;
} else {
(*sum_min) += coeff * ub;
(*sum_max) += coeff * lb;
}
}
}
// 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) {
int64_t sum_min = 0;
int64_t sum_max = 0;
ComputeLinearBounds(proto, mapping, integer_trail, &sum_min, &sum_max);
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;
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));
}
}
// 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);
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
integer_trail->ReserveSpaceForNumVariables(
var_to_instantiate_as_integer.size());
mapping->reverse_integer_map_.resize(2 * 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_LT(mapping->integers_[i], mapping->reverse_integer_map_.size());
mapping->reverse_integer_map_[mapping->integers_[i]] = 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;
}
// Tricky: Moreover, some constraint will causes extra Boolean to be created
// and linked with the Boolean in the constraints. We can't use any of the
// symmetry that touch these since we potentially miss the component that will
// map these extra Booleans between each other.
//
// TODO(user): We could add these extra Boolean during expansion/presolve so
// that we have the symmetry involing them. Or maybe comes up with a different
// solution.
const int num_constraints = model_proto.constraints().size();
for (int c = 0; c < num_constraints; ++c) {
const ConstraintProto& ct = model_proto.constraints(c);
if (ct.constraint_case() != ConstraintProto::kLinear) continue;
if (ct.linear().domain().size() <= 2) continue;
// A linear with a complex domain might need extra Booleans to be loaded.
// Note that it should be fine for the Boolean(s) in enforcement_literal
// though.
for (const int ref : ct.linear().vars()) {
can_be_used_in_symmetry[PositiveRef(ref)] = 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();
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* integer_trail = m->GetOrCreate<IntegerTrail>();
auto* sat_solver = m->GetOrCreate<SatSolver>();
// TODO(user): Debug what makes it unsat at this point.
if (sat_solver->IsModelUnsat()) 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>();
// 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()) {
var_to_equalities[var].push_back(
{&ct, enforcement_literal, inter.Min(), true});
if (domain.Contains(inter.Min())) {
mapping->variables_to_encoded_values_[var].insert(inter.Min());
}
}
}
{
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});
if (domain.Contains(inter.Min())) {
mapping->variables_to_encoded_values_[var].insert(inter.Min());
}
}
}
}
// 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->IsModelUnsat()) return;
++num_half_inequalities;
mapping->already_loaded_ct_.insert(inequality.ct);
mapping->is_half_encoding_ct_.insert(inequality.ct);
}
if (!inequalities.empty()) {
VLOG(1) << 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_constraints = 0;
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;
num_constraints += encoding.size();
absl::flat_hash_set<int64_t> values;
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,
mapping->integers_[i],
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->IsModelUnsat()) 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;
const class Literal eq = encoder->GetOrCreateLiteralAssociatedToEquality(
mapping->integers_[i], IntegerValue(equality.value));
if (equality.is_equality) {
m->Add(Implication(equality.literal, eq));
} else {
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 (VLOG_IS_ON(1)) {
if (encoder->VariableIsFullyEncoded(mapping->integers_[i])) {
++num_fully_encoded;
} else {
++num_partially_encoded;
}
}
}
if (num_constraints > 0) {
VLOG(1) << num_equalities << " literals associated to VAR == value, and "
<< num_half_equalities << " half-associations.";
}
if (num_fully_encoded > 0) {
VLOG(1) << "num_fully_encoded_variables: " << num_fully_encoded;
}
if (num_partially_encoded > 0) {
VLOG(1) << "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* implied_bounds = m->GetOrCreate<ImpliedBounds>();
// Scan all exactly_one constraints and look for literal => var == value to
// detect element encodings.
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::flat_hash_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});
}
}
// VLOG info.
std::vector<IntegerVariable> encoded_variables;
std::string encoded_variables_str;
// Search for variable fully covered by the literals of the exactly_one.
for (const auto& [var, literal_value_list] : var_to_value_literal_list) {
if (literal_value_list.size() < ct.exactly_one().literals_size()) {
VLOG(2) << "X" << var.value() << " has " << literal_value_list.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.
implied_bounds->AddElementEncoding(var, literal_value_list, c);
if (VLOG_IS_ON(1)) {
encoded_variables.push_back(var);
absl::StrAppend(&encoded_variables_str, " X", var.value());
num_element_encoded++;
}
}
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) {
VLOG(1) << "num_element_encoded: " << num_element_encoded;
}
}
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 value_literal :
encoder->PartialGreaterThanEncoding(var1)) {
const IntegerValue value1 = value_literal.first;
const IntegerValue bound2 = FloorRatio(rhs - value1 * coeff1, coeff2);
++num_associations;
encoder->AssociateToIntegerLiteral(
value_literal.second, 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) {
for (const auto value_literal : encoder->PartialDomainEncoding(var1)) {
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;
sat_solver->AddUnitClause(value_literal.literal.Negated());
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) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
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;
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
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;
integer_trail->MarkIntegerVariableAsOptional(
mapping->Integer(var),
mapping->Literal(enforcement_intersection[var].front()));
}
if (num_optionals > 0) {
SOLVER_LOG(m->GetOrCreate<SolverLogger>(), "Auto-detected ", num_optionals,
" optional variables.");
}
}
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 int ref : strategy.variables()) {
if (!mapping->IsInteger(ref)) return;
const IntegerVariable variable = mapping->Integer(PositiveRef(ref));
if (!integer_trail->IsFixed(variable)) {
m->Add(FullyEncodeVariable(variable));
}
}
}
}
}
// ============================================================================
// Constraint loading functions.
// ============================================================================
void LoadBoolOrConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
std::vector<Literal> literals = mapping->Literals(ct.bool_or().literals());
for (const int ref : ct.enforcement_literal()) {
literals.push_back(mapping->Literal(ref).Negated());
}
m->Add(ClauseConstraint(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>();
CHECK(!HasEnforcementLiteral(ct)) << "Not supported.";
m->Add(AtMostOneConstraint(mapping->Literals(ct.at_most_one().literals())));
}
void LoadExactlyOneConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
CHECK(!HasEnforcementLiteral(ct)) << "Not supported.";
m->Add(ExactlyOneConstraint(mapping->Literals(ct.exactly_one().literals())));
}
void LoadBoolXorConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
CHECK(!HasEnforcementLiteral(ct)) << "Not supported.";
m->Add(LiteralXorIs(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;
}
for (const auto value_literal : encoder->FullDomainEncoding(var2)) {
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;
}
for (const auto value_literal : encoder->FullDomainEncoding(var2)) {
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()}));
}
}
}
} // namespace
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.DebugString();
m->GetOrCreate<SatSolver>()->NotifyThatModelIsUnsat();
}
return;
}
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
const std::vector<IntegerVariable> vars =
mapping->Integers(ct.linear().vars());
const 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);
}
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.DebugString() << ", 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.DebugString()
<< ", 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);
}
}
if (ct.linear().domain_size() == 2) {
int64_t lb = ct.linear().domain(0);
int64_t ub = ct.linear().domain(1);
if (min_sum >= lb) lb = std::numeric_limits<int64_t>::min();
if (max_sum <= ub) ub = std::numeric_limits<int64_t>::max();
if (!HasEnforcementLiteral(ct)) {
if (all_booleans) {
// TODO(user): we should probably also implement an
// half-reified version of this constraint.
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->Add(BooleanLinearConstraint(lb, ub, &cst));
} else {
if (lb != std::numeric_limits<int64_t>::min()) {
m->Add(WeightedSumGreaterOrEqual(vars, coeffs, lb));
}
if (ub != std::numeric_limits<int64_t>::max()) {
m->Add(WeightedSumLowerOrEqual(vars, coeffs, ub));
}
}
} else {
const std::vector<Literal> enforcement_literals =
mapping->Literals(ct.enforcement_literal());
if (lb != std::numeric_limits<int64_t>::min()) {
m->Add(ConditionalWeightedSumGreaterOrEqual(enforcement_literals, vars,
coeffs, lb));
}
if (ub != std::numeric_limits<int64_t>::max()) {
m->Add(ConditionalWeightedSumLowerOrEqual(enforcement_literals, vars,
coeffs, ub));
}
}
} else {
// In this case, we can create just one Boolean instead of two since one
// is the negation of the other.
const bool special_case =
ct.enforcement_literal().empty() && ct.linear().domain_size() == 4;
std::vector<Literal> clause;
for (int i = 0; i < ct.linear().domain_size(); i += 2) {
int64_t lb = ct.linear().domain(i);
int64_t ub = ct.linear().domain(i + 1);
if (min_sum >= lb) lb = std::numeric_limits<int64_t>::min();
if (max_sum <= ub) ub = std::numeric_limits<int64_t>::max();
const Literal subdomain_literal(
special_case && i > 0 ? clause.back().Negated()
: Literal(m->Add(NewBooleanVariable()), true));
clause.push_back(subdomain_literal);
if (lb != std::numeric_limits<int64_t>::min()) {
m->Add(ConditionalWeightedSumGreaterOrEqual({subdomain_literal}, vars,
coeffs, lb));
}
if (ub != std::numeric_limits<int64_t>::max()) {
m->Add(ConditionalWeightedSumLowerOrEqual({subdomain_literal}, vars,
coeffs, ub));
}
}
for (const int ref : ct.enforcement_literal()) {
clause.push_back(mapping->Literal(ref).Negated());
}
if (!special_case) m->Add(ClauseConstraint(clause));
}
}
void LoadAllDiffConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
const std::vector<AffineExpression> expressions =
mapping->Affines(ct.all_diff().exprs());
m->Add(AllDifferentOnBounds(expressions));
}
void LoadIntProdConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
const AffineExpression prod = mapping->Affine(ct.int_prod().target());
CHECK_EQ(ct.int_prod().exprs_size(), 2)
<< "General int_prod not supported yet.";
const AffineExpression expr0 = mapping->Affine(ct.int_prod().exprs(0));
const AffineExpression expr1 = mapping->Affine(ct.int_prod().exprs(1));
if (VLOG_IS_ON(1)) {
LinearConstraintBuilder builder(m);
if (DetectLinearEncodingOfProducts(expr0, expr1, m, &builder)) {
VLOG(1) << "Product " << ct.DebugString() << " can be linearized";
}
}
m->Add(ProductConstraint(expr0, expr1, prod));
}
void LoadIntDivConstraint(const ConstraintProto& ct, Model* m) {
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
auto* mapping = m->GetOrCreate<CpModelMapping>();
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(num, integer_trail->FixedValue(denom), div));
} else {
if (VLOG_IS_ON(1)) {
LinearConstraintBuilder builder(m);
if (DetectLinearEncodingOfProducts(num, denom, m, &builder)) {
VLOG(1) << "Division " << ct.DebugString() << " can be linearized";
}
}
m->Add(DivisionConstraint(num, denom, div));
}
}
void LoadIntModConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
auto* integer_trail = m->GetOrCreate<IntegerTrail>();
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(expr, fixed_modulo, target));
}
void LoadLinMaxConstraint(const ConstraintProto& ct, Model* m) {
if (ct.lin_max().exprs().empty()) {
m->GetOrCreate<SatSolver>()->NotifyThatModelIsUnsat();
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.
m->Add(IsEqualToMinOf(NegationOf(max), negated_exprs));
}
void LoadNoOverlapConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
m->Add(Disjunctive(mapping->Intervals(ct.no_overlap().intervals())));
}
void LoadNoOverlap2dConstraint(const ConstraintProto& ct, Model* m) {
if (ct.no_overlap_2d().x_intervals().empty()) return;
auto* mapping = m->GetOrCreate<CpModelMapping>();
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());
m->Add(NonOverlappingRectangles(
x_intervals, y_intervals,
!ct.no_overlap_2d().boxes_with_null_area_can_overlap()));
}
void LoadCumulativeConstraint(const ConstraintProto& ct, Model* m) {
auto* mapping = m->GetOrCreate<CpModelMapping>();
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(intervals, demands, capacity));
}
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());
std::vector<Literal> literals =
m->GetOrCreate<CpModelMapping>()->Literals(circuit.literals());
const int num_nodes = ReindexArcs(&tails, &heads);
m->Add(SubcircuitConstraint(num_nodes, tails, heads, literals));
}
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());
std::vector<Literal> literals =
m->GetOrCreate<CpModelMapping>()->Literals(routes.literals());
const int num_nodes = ReindexArcs(&tails, &heads);
m->Add(SubcircuitConstraint(num_nodes, tails, heads, literals,
/*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::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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