ortools-clone/ortools/sat/variable_expand.cc

// 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/variable_expand.h"

#include <cstdint>
#include <cstdlib>
#include <limits>
#include <optional>
#include <string>
#include <utility>
#include <vector>

#include "absl/algorithm/container.h"
#include "absl/container/btree_map.h"
#include "absl/log/check.h"
#include "absl/log/log.h"
#include "absl/strings/str_cat.h"
#include "ortools/base/stl_util.h"
#include "ortools/sat/cp_model_utils.h"
#include "ortools/sat/presolve_context.h"
#include "ortools/sat/solution_crush.h"
#include "ortools/util/saturated_arithmetic.h"
#include "ortools/util/sorted_interval_list.h"

namespace operations_research {
namespace sat {

namespace {
enum class EncodingLinear1Type {
  kVarEqValue = 0,
  kVarNeValue,
  kVarGeValue,
  kVarLeValue,
  kVarInDomain,
};
const int kNumEncodingLinear1Types = 5;

template <typename Sink>
void AbslStringify(Sink& sink, EncodingLinear1Type type) {
  switch (type) {
    case EncodingLinear1Type::kVarEqValue:
      sink.Append("kVarEqValue");
      return;
    case EncodingLinear1Type::kVarNeValue:
      sink.Append("kVarNeValue");
      return;
    case EncodingLinear1Type::kVarGeValue:
      sink.Append("kVarGeValue");
      return;
    case EncodingLinear1Type::kVarLeValue:
      sink.Append("kVarLeValue");
      return;
    case EncodingLinear1Type::kVarInDomain:
      sink.Append("kVarInDomain");
      return;
  }
}

enum class EncodingLinear1Status {
  kOk = 0,
  kIgnore,
  kUnsat,
  kAbort,
};

struct EncodingLinear1 {
  EncodingLinear1Type type;
  int64_t value = std::numeric_limits<int64_t>::min();
  Domain rhs;  // Only used for kVarInDomain.
  int enforcement_literal;
  int constraint_index;

  std::string ToString() const {
    return absl::StrCat("EncodingLinear1(type: ", type, ", value: ", value,
                        ", rhs: ", rhs.ToString(),
                        ", enforcement_literal: ", enforcement_literal,
                        ", constraint_index: ", constraint_index, ")");
  }
};

std::pair<std::optional<EncodingLinear1>, EncodingLinear1Status> ProcessLinear1(
    PresolveContext* context, int constraint_index, const Domain& var_domain) {
  const ConstraintProto& ct =
      context->working_model->constraints(constraint_index);
  const Domain rhs = ReadDomainFromProto(ct.linear())
                         .InverseMultiplicationBy(ct.linear().coeffs(0))
                         .IntersectionWith(var_domain);
  EncodingLinear1 lin;
  lin.enforcement_literal = ct.enforcement_literal(0);
  lin.constraint_index = constraint_index;

  if (rhs.IsEmpty()) {
    if (!context->SetLiteralToFalse(lin.enforcement_literal)) {
      return {std::nullopt, EncodingLinear1Status::kUnsat};
    } else {
      return {std::nullopt, EncodingLinear1Status::kIgnore};
    }
  } else if (rhs.IsFixed()) {
    if (!var_domain.Contains(rhs.FixedValue())) {
      if (!context->SetLiteralToFalse(lin.enforcement_literal)) {
        return {std::nullopt, EncodingLinear1Status::kUnsat};
      }
      return {std::nullopt, EncodingLinear1Status::kIgnore};
    } else {
      lin.type = EncodingLinear1Type::kVarEqValue;
      lin.value = rhs.FixedValue();
    }
  } else {
    const Domain complement = var_domain.IntersectionWith(rhs.Complement());
    if (complement.IsEmpty()) {
      return {std::nullopt, EncodingLinear1Status::kIgnore};
    } else if (complement.IsFixed()) {
      DCHECK(var_domain.Contains(complement.FixedValue()));
      lin.type = EncodingLinear1Type::kVarNeValue;
      lin.value = complement.FixedValue();
    } else if (rhs.Min() > complement.Max()) {
      lin.type = EncodingLinear1Type::kVarGeValue;
      lin.value = rhs.Min();
    } else if (rhs.Max() < complement.Min()) {
      lin.type = EncodingLinear1Type::kVarLeValue;
      lin.value = rhs.Max();
    } else {
      lin.type = EncodingLinear1Type::kVarInDomain;
      lin.rhs = rhs;
    }
  }
  return {lin, EncodingLinear1Status::kOk};
}

template <typename Sink>
void AbslStringify(Sink& sink, const EncodingLinear1& lin) {
  sink.Append(lin.ToString());
}

}  // namespace

ValueEncoding::ValueEncoding(int var, PresolveContext* context)
    : var_(var), var_domain_(context->DomainOf(var)), context_(context) {}

void ValueEncoding::AddValueToEncode(int64_t value) {
  DCHECK(!is_closed_);
  encoded_values_.push_back(value);
}

void ValueEncoding::CanonicalizeEncodedValuesAndAddEscapeValue(
    bool var_in_objective, bool var_has_positive_objective_coefficient) {
  if (!is_closed_) {
    gtl::STLSortAndRemoveDuplicates(&encoded_values_);
    // Add an escape value to the existing encoded values when the encoding is
    // not complete. This depends on the presence of an objective and its
    // direction.
    //
    // TODO(user): actually if the encoding is not mandatory (lit => var ==
    // value instead of lit <=> var == value), then the escape value can be the
    // the min of var_domain instead of the min of the residual domain (in case
    // we are minimizing var in the objective).
    if (encoded_values_.size() < var_domain_.Size()) {
      const Domain residual = var_domain_.IntersectionWith(
          Domain::FromValues(encoded_values_).Complement());
      const int64_t escape_value =
          !var_in_objective
              ? residual.SmallestValue()
              : (var_has_positive_objective_coefficient ? residual.Min()
                                                        : residual.Max());
      encoded_values_.push_back(escape_value);
    }
    is_closed_ = true;
    is_fully_encoded_ = encoded_values_.size() == var_domain_.Size();
  }
}

const std::vector<int64_t>& ValueEncoding::encoded_values() const {
  DCHECK(is_closed_);
  return encoded_values_;
}

bool ValueEncoding::empty() const {
  DCHECK(is_closed_);
  return encoded_values_.empty();
}

bool ValueEncoding::is_fully_encoded() const {
  DCHECK(is_closed_);
  return is_fully_encoded_;
}

void ValueEncoding::ForceFullEncoding() {
  encoded_values_.clear();
  encoded_values_.reserve(var_domain_.Size());
  for (const int64_t v : var_domain_.Values()) {
    encoded_values_.push_back(v);
  }
  is_closed_ = true;
  is_fully_encoded_ = true;
}

void ValueEncoding::CreateAllValueEncodingLiterals() {
  DCHECK(is_closed_);
  for (const int64_t value : encoded_values_) {
    encoding_[value] = context_->GetOrCreateVarValueEncoding(var_, value);
  }
}

int ValueEncoding::literal(int64_t value) const {
  DCHECK(is_closed_);
  const auto it = encoding_.find(value);
  DCHECK(it != encoding_.end());
  return it->second;
}

const absl::btree_map<int64_t, int>& ValueEncoding::encoding() const {
  DCHECK(is_closed_);
  return encoding_;
}

OrderEncoding::OrderEncoding(int var, PresolveContext* context,
                             SolutionCrush& solution_crush)
    : var_(var),
      var_domain_(context->DomainOf(var)),
      context_(context),
      solution_crush_(solution_crush) {}

void OrderEncoding::InsertLeLiteral(int64_t value, int literal) {
  if (!tmp_le_to_literals_[value].insert(literal).second) return;
  DCHECK_LT(value, var_domain_.Max());
  const int64_t next_value = var_domain_.ValueAtOrAfter(value + 1);
  if (tmp_ge_to_literals_[next_value].contains(NegatedRef(literal))) {
    const auto [it, inserted] = encoded_le_literal_.insert({value, literal});
    if (!inserted) {
      VLOG(2) << "Duplicate var_le_value literal: " << literal
              << " for value: " << value << " previous: " << it->second;
    }
  }
}

void OrderEncoding::InsertGeLiteral(int64_t value, int literal) {
  if (!tmp_ge_to_literals_[value].insert(literal).second) return;
  DCHECK_GT(value, var_domain_.Min());
  const int64_t previous_value = var_domain_.ValueAtOrBefore(value - 1);
  if (tmp_le_to_literals_[previous_value].contains(NegatedRef(literal))) {
    const auto [it, inserted] =
        encoded_le_literal_.insert({previous_value, NegatedRef(literal)});
    if (!inserted) {
      VLOG(2) << "Duplicate var_le_value literal: " << NegatedRef(literal)
              << " for value: " << previous_value
              << " previous: " << it->second;
    }
  }
}

// In the following examples, x has 5 values: 0, 1, 2, 3, 4, and some order
// encoding literals.
//      0       1      2       3       4
//   x_le_0  x_le_1          x_le_3
//           x_ge_1          x_ge_3  x_ge_4
//
// x_le_0 => not(x == 1) && x_le_1
// x_le_1 => not(x == 2) && not(x == 3) && x_le_3
//
// x_ge_1 => not(x == 0)
// x_ge_3 => not(x == 1) && not(x == 2) && x_ge_1
// x_ge_4 => not(x == 3) && x_ge_3
void OrderEncoding::CreateAllOrderEncodingLiterals(
    const ValueEncoding& values) {
  CollectAllOrderEncodingValues();
  for (const auto& [value, literal] : encoded_le_literal_) {
    DCHECK(values.encoding().contains(value));
    DCHECK(values.encoding().contains(var_domain_.ValueAtOrAfter(value + 1)))
        << "Cannot find " << var_domain_.ValueAtOrAfter(value + 1)
        << " for var <= " << value;
  }
  if (!encoded_le_literal_.empty()) {
    const int64_t max_ge_value =
        var_domain_.ValueAtOrAfter(encoded_le_literal_.rbegin()->first + 1);
    ConstraintProto* not_le = nullptr;
    ConstraintProto* not_ge = context_->working_model->add_constraints();
    for (const auto [value, eq_literal] : values.encoding()) {
      const int ne_literal = NegatedRef(eq_literal);

      // Lower or equal.
      if (not_le != nullptr) {
        not_le->mutable_bool_and()->add_literals(ne_literal);
      }
      const auto it_le = encoded_le_literal_.find(value);
      if (it_le != encoded_le_literal_.end()) {
        const int le_literal = it_le->second;
        if (not_le != nullptr) {
          not_le->mutable_bool_and()->add_literals(le_literal);
        }
        not_le = context_->AddEnforcedConstraint({le_literal});
      }

      // Greater or equal.
      if (value > var_domain_.Min()) {  // var >= min is not created..
        const auto it_ge =
            encoded_le_literal_.find(var_domain_.ValueAtOrBefore(value - 1));
        if (it_ge != encoded_le_literal_.end()) {
          const int ge_literal = NegatedRef(it_ge->second);
          DCHECK(not_ge != nullptr);
          not_ge->add_enforcement_literal(ge_literal);
          if (value != max_ge_value) {
            not_ge = context_->working_model->add_constraints();
            not_ge->mutable_bool_and()->add_literals(ge_literal);
          } else {
            not_ge = nullptr;
          }
        }
      }
      if (not_ge != nullptr) {
        not_ge->mutable_bool_and()->add_literals(ne_literal);
      }
    }
  }
}

int OrderEncoding::ge_literal(int64_t value) const {
  DCHECK_GT(value, var_domain_.Min());
  const auto it =
      encoded_le_literal_.find(var_domain_.ValueAtOrBefore(value - 1));
  DCHECK(it != encoded_le_literal_.end());
  return NegatedRef(it->second);
}

int OrderEncoding::le_literal(int64_t value) const {
  const auto it = encoded_le_literal_.find(value);
  DCHECK(it != encoded_le_literal_.end());
  return it->second;
}

int OrderEncoding::num_encoded_values() const {
  return encoded_le_literal_.size();
}

void OrderEncoding::CollectAllOrderEncodingValues() {
  for (const auto& [value, lits] : tmp_le_to_literals_) {
    const auto it = encoded_le_literal_.find(value);
    if (it != encoded_le_literal_.end()) continue;
    const int le_literal = context_->NewBoolVar("order encoding");
    solution_crush_.MaybeSetLiteralToOrderEncoding(le_literal, var_, value,
                                                   /*is_le=*/true);
    encoded_le_literal_[value] = le_literal;
  }

  for (const auto& [value, lits] : tmp_ge_to_literals_) {
    const int64_t previous_value = var_domain_.ValueAtOrBefore(value - 1);
    const auto it = encoded_le_literal_.find(previous_value);
    if (it != encoded_le_literal_.end()) continue;
    const int ge_literal = context_->NewBoolVar("order encoding");
    solution_crush_.MaybeSetLiteralToOrderEncoding(ge_literal, var_, value,
                                                   /*is_le=*/false);
    encoded_le_literal_[previous_value] = NegatedRef(ge_literal);
  }
}

bool ProcessEncodingConstraints(
    int var, PresolveContext* context, ValueEncoding& values,
    OrderEncoding& order,
    std::vector<std::vector<EncodingLinear1>>& linear_ones_by_type,
    std::vector<int>& constraint_indices, bool& var_in_objective,
    bool& var_has_positive_objective_coefficient) {
  const Domain& var_domain = context->DomainOf(var);
  constraint_indices.clear();
  var_in_objective = false;
  var_has_positive_objective_coefficient = false;
  for (const int c : context->VarToConstraints(var)) {
    if (c == kObjectiveConstraint) {
      const int64_t obj_coeff = context->ObjectiveMap().at(var);
      var_in_objective = true;
      var_has_positive_objective_coefficient = obj_coeff > 0;
      continue;
    }
    if (c < 0) continue;
    constraint_indices.push_back(c);
  }

  // Sort the constraint indices to make the encoding deterministic.
  absl::c_sort(constraint_indices);
  for (const int c : constraint_indices) {
    const ConstraintProto& ct = context->working_model->constraints(c);
    DCHECK_EQ(ct.constraint_case(), ConstraintProto::kLinear);
    DCHECK_EQ(ct.linear().vars().size(), 1);
    DCHECK(RefIsPositive(ct.linear().vars(0)));
    DCHECK_EQ(ct.linear().vars(0), var);
    if (ct.enforcement_literal().size() != 1) {
      context->UpdateRuleStats(
          "TODO variables: linear1 with multiple enforcement literals");
      return false;
    }

    const auto& [opt_lin, status] = ProcessLinear1(context, c, var_domain);
    if (!opt_lin.has_value()) {
      if (status == EncodingLinear1Status::kUnsat) return false;
      if (status == EncodingLinear1Status::kIgnore) continue;
      if (status == EncodingLinear1Status::kAbort) {
        context->UpdateRuleStats(
            "TODO variables: only used in complex linear1");
        return false;
      }
    }

    const EncodingLinear1& lin = opt_lin.value();
    VLOG(3) << "ProcessVariableOnlyUsedInEncoding(): var(" << var
            << ") domain: " << var_domain << " linear1: " << lin;

    switch (lin.type) {
      case EncodingLinear1Type::kVarEqValue:
        values.AddValueToEncode(lin.value);
        break;
      case EncodingLinear1Type::kVarNeValue:
        values.AddValueToEncode(lin.value);
        break;
      case EncodingLinear1Type::kVarGeValue: {
        values.AddValueToEncode(lin.value);
        values.AddValueToEncode(var_domain.ValueAtOrBefore(lin.value - 1));
        order.InsertGeLiteral(lin.value, lin.enforcement_literal);
        break;
      }
      case EncodingLinear1Type::kVarLeValue: {
        values.AddValueToEncode(lin.value);
        values.AddValueToEncode(var_domain.ValueAtOrAfter(lin.value + 1));
        order.InsertLeLiteral(lin.value, lin.enforcement_literal);
        break;
      }
      case EncodingLinear1Type::kVarInDomain: {
        // TODO(user): fine grained management of the domains.
        break;
      }
    }

    linear_ones_by_type[static_cast<int>(lin.type)].push_back(lin);
  }
  values.CanonicalizeEncodedValuesAndAddEscapeValue(
      var_in_objective, var_has_positive_objective_coefficient);
  return true;
}

void TryToReplaceVariableByItsEncoding(int var, int& new_exo_to_presolve_index,
                                       PresolveContext* context,
                                       SolutionCrush& solution_crush) {
  const Domain var_domain = context->DomainOf(var);
  std::vector<std::vector<EncodingLinear1>> linear_ones_by_type(
      kNumEncodingLinear1Types);
  ValueEncoding values(var, context);
  OrderEncoding order(var, context, solution_crush);
  bool var_in_objective = false;
  bool var_has_positive_objective_coefficient = false;
  std::vector<int> constraint_indices;
  if (!ProcessEncodingConstraints(
          var, context, values, order, linear_ones_by_type, constraint_indices,
          var_in_objective, var_has_positive_objective_coefficient)) {
    return;
  }

  // Helpers to get the linear1 of each type for var.
  const auto& lin_eq =
      linear_ones_by_type[static_cast<int>(EncodingLinear1Type::kVarEqValue)];
  const auto& lin_ne =
      linear_ones_by_type[static_cast<int>(EncodingLinear1Type::kVarNeValue)];
  const auto& lin_ge =
      linear_ones_by_type[static_cast<int>(EncodingLinear1Type::kVarGeValue)];
  const auto& lin_le =
      linear_ones_by_type[static_cast<int>(EncodingLinear1Type::kVarLeValue)];
  const auto& lin_domain =
      linear_ones_by_type[static_cast<int>(EncodingLinear1Type::kVarInDomain)];

  // We force the full encoding if the variable is mostly encoded and some
  // linear1 involves domains that do not correspond to value or order
  // encodings.
  const bool full_encoding_is_not_too_expensive =
      context->IsMostlyFullyEncoded(var) || var_domain.Size() <= 32;
  const bool full_encoding_is_needed =
      (!lin_domain.empty() ||
       (var_in_objective && context->ObjectiveDomainIsConstraining()));
  if (!values.is_fully_encoded() && full_encoding_is_not_too_expensive &&
      full_encoding_is_needed) {
    VLOG(3) << "Forcing full encoding of var: " << var;
    values.ForceFullEncoding();
  }

  if (values.empty()) {
    // This variable has no value encoding. Either enforced_domains is
    // empty, and in that case, we will not do anything about it, or the
    // variable is not used anymore, and it will be removed later.
    return;
  }

  // Compute how many literals are implied by the complex domains.
  int num_implied_literals_in_complex_domains = 0;
  for (const EncodingLinear1& info : lin_domain) {
    num_implied_literals_in_complex_domains +=
        var_domain.Size() - info.rhs.Size();
  }

  VLOG(2) << "ProcessVariableOnlyUsedInEncoding(): var(" << var
          << "): " << var_domain << ", size: " << var_domain.Size()
          << ", #encoded_values: " << values.encoded_values().size()
          << ", #ordered_values: " << order.num_encoded_values()
          << ", #var_eq_value: " << lin_eq.size()
          << ", #var_ne_value: " << lin_ne.size()
          << ", #var_ge_value: " << lin_ge.size()
          << ", #var_le_value: " << lin_le.size()
          << ", #var_in_domain: " << lin_domain.size()
          << ", var_in_objective: " << var_in_objective
          << ", var_has_positive_objective_coefficient: "
          << var_has_positive_objective_coefficient
          << ", #implied_literals_in_complex_domains: "
          << num_implied_literals_in_complex_domains;
  if (full_encoding_is_needed &&
      (!values.is_fully_encoded() ||
       num_implied_literals_in_complex_domains > 2500)) {
    VLOG(2) << "Abort - fully_encode_var: " << values.is_fully_encoded()
            << ", num_implied_literals_in_complex_domains: "
            << num_implied_literals_in_complex_domains
            << ", full_encoding_is_not_too_expensive: "
            << full_encoding_is_not_too_expensive
            << ", full_encoding_is_needed: " << full_encoding_is_needed;
    if (var_in_objective) {
      context->UpdateRuleStats(
          "TODO variables: only used in constrained objective and in encoding");
    } else {
      context->UpdateRuleStats(
          "TODO variables: only used in large value encoding and order "
          "encoding.");
    }
    return;
  }

  values.CreateAllValueEncodingLiterals();
  // Fix the hinted value if needed.
  solution_crush.SetOrUpdateVarToDomain(
      var, Domain::FromValues(values.encoded_values()), values.encoding(),
      var_in_objective, var_has_positive_objective_coefficient);
  order.CreateAllOrderEncodingLiterals(values);

  // Link all Boolean in our linear1 to the encoding literals.
  for (const EncodingLinear1& info_eq : lin_eq) {
    context->AddImplication(info_eq.enforcement_literal,
                            values.literal(info_eq.value));
  }

  for (const EncodingLinear1& info_ne : lin_ne) {
    context->AddImplication(info_ne.enforcement_literal,
                            NegatedRef(values.literal(info_ne.value)));
  }

  for (const EncodingLinear1& info_ge : lin_ge) {
    context->AddImplication(info_ge.enforcement_literal,
                            order.ge_literal(info_ge.value));
  }

  for (const EncodingLinear1& info_le : lin_le) {
    context->AddImplication(info_le.enforcement_literal,
                            order.le_literal(info_le.value));
  }

  for (const EncodingLinear1& info_in : lin_domain) {
    ConstraintProto* imply =
        context->AddEnforcedConstraint({info_in.enforcement_literal});
    const Domain implied_complement =
        var_domain.IntersectionWith(info_in.rhs.Complement());
    for (const int64_t v : implied_complement.Values()) {
      imply->mutable_bool_and()->add_literals(NegatedRef(values.literal(v)));
    }
  }

  // Detect implications between complex encodings.
  const auto add_incompatibility = [context](const EncodingLinear1& info_i,
                                             const EncodingLinear1& info_j) {
    const int e_i = info_i.enforcement_literal;
    const int e_j = info_j.enforcement_literal;
    if (e_i == NegatedRef(e_j)) return;
    BoolArgumentProto* incompatible =
        context->working_model->add_constraints()->mutable_bool_or();
    incompatible->add_literals(NegatedRef(e_i));
    incompatible->add_literals(NegatedRef(e_j));
    context->UpdateRuleStats(
        "variables: add at_most_one between incompatible complex encodings");
  };

  for (int i = 0; i < lin_domain.size(); ++i) {
    const EncodingLinear1& info_i = lin_domain[i];
    DCHECK_EQ(info_i.type, EncodingLinear1Type::kVarInDomain);

    // Incompatibilities between x in domain and x >= ge.
    for (const EncodingLinear1& info_j : lin_ge) {
      DCHECK_EQ(info_j.type, EncodingLinear1Type::kVarGeValue);
      if (info_i.rhs.Max() < info_j.value) {
        add_incompatibility(info_i, info_j);
      }
    }

    // Incompatibilities between x in domain and x <= value.
    for (const EncodingLinear1& info_j : lin_le) {
      DCHECK_EQ(info_j.type, EncodingLinear1Type::kVarLeValue);
      if (info_i.rhs.Min() > info_j.value) {
        add_incompatibility(info_i, info_j);
      }
    }

    // Incompatibilities between x in domain_i and x in domain_j.
    for (int j = i + 1; j < lin_domain.size(); ++j) {
      const EncodingLinear1& info_j = lin_domain[j];
      DCHECK_EQ(info_j.type, EncodingLinear1Type::kVarInDomain);
      if (!info_i.rhs.OverlapsWith(info_j.rhs)) {
        add_incompatibility(info_i, info_j);
      }
    }
  }
  context->UpdateNewConstraintsVariableUsage();

  // Update the objective if needed. Note that this operation can fail if
  // the new expression result in potential overflow.
  if (var_in_objective) {
    // We substract the min or the max of the variable from all
    // coefficients. This should reduce the objective size and helps with
    // the bounds.
    const int64_t base_value = var_has_positive_objective_coefficient
                                   ? var_domain.Min()
                                   : var_domain.Max();
    // Tricky: We cannot just choose an arbitrary value if the objective has
    // a restrictive domain!
    DCHECK(values.is_fully_encoded() ||
           !context->ObjectiveDomainIsConstraining());

    // Checks for overflow before trying to substitute the variable in the
    // objective.
    int64_t accumulated = std::abs(base_value);
    for (const int64_t value : values.encoded_values()) {
      accumulated = CapAdd(accumulated, std::abs(CapSub(value, base_value)));
      if (accumulated == std::numeric_limits<int64_t>::max()) {
        VLOG(2) << "Abort - overflow when converting linear1 to clauses";
        context->UpdateRuleStats(
            "TODO variables: overflow when converting linear1 to clauses");
        return;
      }
    }

    // TODO(user): we could also use a log encoding here if the domain is
    // large and the objective is not constraining.
    ConstraintProto encoding_ct;
    LinearConstraintProto* linear = encoding_ct.mutable_linear();
    const int64_t coeff_in_equality = -1;
    linear->add_vars(var);
    linear->add_coeffs(coeff_in_equality);
    int64_t rhs_value = -base_value;
    for (const auto& [value, literal] : values.encoding()) {
      const int64_t coeff = value - base_value;
      if (coeff == 0) continue;
      if (RefIsPositive(literal)) {
        linear->add_vars(literal);
        linear->add_coeffs(coeff);
      } else {
        // (1 - var) * coeff;
        rhs_value -= coeff;
        linear->add_vars(PositiveRef(literal));
        linear->add_coeffs(-coeff);
      }
    }
    linear->add_domain(rhs_value);
    linear->add_domain(rhs_value);
    if (!context->SubstituteVariableInObjective(var, coeff_in_equality,
                                                encoding_ct)) {
      context->UpdateRuleStats(
          "TODO variables: cannot substitute encoded variable in objective");
      return;
    }
    context->UpdateRuleStats(
        "variables: only used in objective and in encoding");
  } else {
    if ((!lin_eq.empty() || !lin_ne.empty()) && lin_domain.empty()) {
      context->UpdateRuleStats(
          "variables: only used in value and order encodings");
    } else if (!lin_domain.empty()) {
      context->UpdateRuleStats("variables: only used in complex encoding");
    } else {
      context->UpdateRuleStats("variables: only used in value encoding");
    }
  }
  if (!values.is_fully_encoded()) {
    VLOG(2) << "Reduce domain size: " << var_domain.Size() << " to  "
            << values.encoded_values().size() << ": " << var_domain << " -> "
            << Domain::FromValues(values.encoded_values());
    context->UpdateRuleStats("variables: reduce domain to encoded values");
  }

  // Clear all involved constraint. We do it in two passes to avoid
  // invalidating the iterator. We also use the constraint variable graph as
  // extra encodings (value, order) may have added new constraints.
  {
    std::vector<int> to_clear;
    for (const int c : context->VarToConstraints(var)) {
      if (c >= 0) to_clear.push_back(c);
    }
    absl::c_sort(to_clear);
    for (const int c : to_clear) {
      context->working_model->mutable_constraints(c)->Clear();
      context->UpdateConstraintVariableUsage(c);
    }
  }

  // This must be done after we removed all the constraint containing var.
  new_exo_to_presolve_index = context->working_model->constraints_size();
  ConstraintProto* exo = context->working_model->add_constraints();
  BoolArgumentProto* arg = exo->mutable_exactly_one();
  for (const auto& [value, literal] : values.encoding()) {
    arg->add_literals(literal);
  }
  context->UpdateNewConstraintsVariableUsage();
  if (context->ModelIsUnsat()) return;

  // To simplify the postsolve, we output a single constraint to infer X from
  // the bi:  X = sum bi * (Vi - min_value) + min_value
  const int64_t var_min = var_domain.Min();
  ConstraintProto* mapping_ct =
      context->NewMappingConstraint(__FILE__, __LINE__);
  mapping_ct->mutable_linear()->add_vars(var);
  mapping_ct->mutable_linear()->add_coeffs(1);
  int64_t offset = var_min;
  for (const auto& [value, literal] : values.encoding()) {
    const int64_t coeff = value - var_min;
    if (coeff == 0) continue;
    if (RefIsPositive(literal)) {
      mapping_ct->mutable_linear()->add_vars(literal);
      mapping_ct->mutable_linear()->add_coeffs(-coeff);
    } else {
      offset += coeff;
      mapping_ct->mutable_linear()->add_vars(PositiveRef(literal));
      mapping_ct->mutable_linear()->add_coeffs(coeff);
    }
  }
  mapping_ct->mutable_linear()->add_domain(offset);
  mapping_ct->mutable_linear()->add_domain(offset);

  context->MarkVariableAsRemoved(var);
}

}  // namespace sat
}  // namespace operations_research