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linear_expr.h
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linear_expr.h
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// Copyright 2010-2018 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.
#ifndef OR_TOOLS_LINEAR_SOLVER_LINEAR_EXPR_H_
#define OR_TOOLS_LINEAR_SOLVER_LINEAR_EXPR_H_
/**
* \file
* This file allows you to write natural code (like a mathematical equation) to
* model optimization problems with MPSolver. It is syntatic sugar on top of
* the MPSolver API, it provides no additional functionality. Use of these APIs
* makes it much easier to write code that is both simple and big-O optimal for
* creating your model, at the cost of some additional constant factor
* overhead. If model creation is a bottleneck in your problem, consider using
* the MPSolver API directly instead.
*
* This file contains two classes:
* 1. LinearExpr: models offset + sum_{i in S} a_i*x_i for decision var x_i,
* 2. LinearRange: models lb <= sum_{i in S} a_i*x_i <= ub,
* and it provides various operator overloads to build up "LinearExpr"s and
* then convert them to "LinearRange"s.
*
* Recommended use (avoids dangerous code):
*
* \code
MPSolver solver = ...;
const LinearExpr x = solver.MakeVar(...); * Note: implicit conversion
const LinearExpr y = solver.MakeVar(...);
const LinearExpr z = solver.MakeVar(...);
const LinearExpr e1 = x + y;
const LinearExpr e2 = (e1 + 7.0 + z)/3.0;
const LinearRange r = e1 <= e2;
solver.MakeRowConstraint(r);
\endcode
*
* \b WARNING, AVOID THIS TRAP:
*
* \code
MPSolver solver = ...;
MPVariable* x = solver.MakeVar(...);
LinearExpr y = x + 5;
\endcode
*
* In evaluating "x+5" above, x is NOT converted to a LinearExpr before the
* addition, but rather is treated as a pointer, so x+5 gives a new pointer to
* garbage memory.
*
* For this reason, when using LinearExpr, it is best practice to:
* 1. use double literals instead of ints (e.g. "x + 5.0", not "x + 5"),
* 2. Immediately convert all MPVariable* to LinearExpr on creation, and only
* hold references to the "LinearExpr"s.
*
* Likewise, the following code is NOT recommended:
* \code
MPSolver solver = ...;
MPVariable* x = solver.MakeVar(...);
MPVariable* y = solver.MakeVar(...);
LinearExpr e1 = LinearExpr(x) + y + 5;
\endcode
*
* While it is correct, it violates the natural assumption that the + operator
* is associative. Thus you are setting a trap for future modifications of the
* code, as any of the following changes would lead to the above failure mode:
*
* * \code LinearExpr e1 = LinearExpr(x) + (y + 5); \endcode
* * \code LinearExpr e1 = y + 5 + LinearExpr(x); \endcode
*/
#include <ostream>
#include <string>
#include "absl/container/flat_hash_map.h"
namespace operations_research {
// NOTE(user): forward declaration is necessary due to cyclic dependencies,
// MPVariable is defined in linear_solver.h, which depends on LinearExpr.
class MPVariable;
/**
* LinearExpr models a quantity that is linear in the decision variables
* (MPVariable) of an optimization problem, i.e.
*
* offset + sum_{i in S} a_i*x_i,
*
* where the a_i and offset are constants and the x_i are MPVariables. You can
* use a LinearExpr "linear_expr" with an MPSolver "solver" to:
* * Set as the objective of your optimization problem, e.g.
*
* solver.MutableObjective()->MaximizeLinearExpr(linear_expr);
*
* * Create a constraint in your optimization, e.g.
*
* solver.MakeRowConstraint(linear_expr1 <= linear_expr2);
*
* * Get the value of the quantity after solving, e.g.
*
* solver.Solve();
* linear_expr.SolutionValue();
*
* LinearExpr is allowed to delete variables with coefficient zero from the map,
* but is not obligated to do so.
*/
class LinearExpr {
public:
LinearExpr();
/// Possible implicit conversions are intentional.
LinearExpr(double constant); // NOLINT
/***
* Possible implicit conversions are intentional.
*
* Warning: var is not owned.
*/
LinearExpr(const MPVariable* var); // NOLINT
/**
* Returns 1-var.
*
* NOTE(user): if var is binary variable, this corresponds to the logical
* negation of var.
* Passing by value is intentional, see the discussion on binary ops.
*/
static LinearExpr NotVar(LinearExpr var);
LinearExpr& operator+=(const LinearExpr& rhs);
LinearExpr& operator-=(const LinearExpr& rhs);
LinearExpr& operator*=(double rhs);
LinearExpr& operator/=(double rhs);
LinearExpr operator-() const;
double offset() const { return offset_; }
const absl::flat_hash_map<const MPVariable*, double>& terms() const {
return terms_;
}
/**
* Evaluates the value of this expression at the solution found.
*
* It must be called only after calling MPSolver::Solve.
*/
double SolutionValue() const;
/**
* A human readable representation of this. Variables will be printed in order
* of lowest index first.
*/
std::string ToString() const;
private:
double offset_;
absl::flat_hash_map<const MPVariable*, double> terms_;
};
std::ostream& operator<<(std::ostream& stream, const LinearExpr& linear_expr);
// NOTE(user): in the ops below, the non-"const LinearExpr&" are intentional.
// We need to create a new LinearExpr for the result, so we lose nothing by
// passing one argument by value, mutating it, and then returning it. In
// particular, this allows (with move semantics and RVO) an optimized
// evaluation of expressions such as
// a + b + c + d
// (see http://en.cppreference.com/w/cpp/language/operators).
LinearExpr operator+(LinearExpr lhs, const LinearExpr& rhs);
LinearExpr operator-(LinearExpr lhs, const LinearExpr& rhs);
LinearExpr operator*(LinearExpr lhs, double rhs);
LinearExpr operator/(LinearExpr lhs, double rhs);
LinearExpr operator*(double lhs, LinearExpr rhs);
/**
* An expression of the form:
*
* \code lower_bound <= sum_{i in S} a_i*x_i <= upper_bound. \endcode
* The sum is represented as a LinearExpr with offset 0.
*
* Must be added to model with
* \code
MPSolver::AddRowConstraint(const LinearRange& range,
const std::string& name);
\endcode
*/
class LinearRange {
public:
LinearRange() : lower_bound_(0), upper_bound_(0) {}
/**
* The bounds of the linear range are updated so that they include the offset
* from "linear_expr", i.e., we form the range:
* \code
lower_bound - offset <= linear_expr - offset <= upper_bound - offset.
\endcode
*/
LinearRange(double lower_bound, const LinearExpr& linear_expr,
double upper_bound);
double lower_bound() const { return lower_bound_; }
const LinearExpr& linear_expr() const { return linear_expr_; }
double upper_bound() const { return upper_bound_; }
private:
double lower_bound_;
// invariant: linear_expr_.offset() == 0.
LinearExpr linear_expr_;
double upper_bound_;
};
LinearRange operator<=(const LinearExpr& lhs, const LinearExpr& rhs);
LinearRange operator==(const LinearExpr& lhs, const LinearExpr& rhs);
LinearRange operator>=(const LinearExpr& lhs, const LinearExpr& rhs);
// TODO(user,user): explore defining more overloads to support:
// solver.AddRowConstraint(0.0 <= x + y + z <= 1.0);
} // namespace operations_research
#endif // OR_TOOLS_LINEAR_SOLVER_LINEAR_EXPR_H_