Scippy

    SCIP

    Solving Constraint Integer Programs

    heur_trustregion.h
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    2/* */
    3/* This file is part of the program and library */
    4/* SCIP --- Solving Constraint Integer Programs */
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    6/* Copyright (c) 2002-2025 Zuse Institute Berlin (ZIB) */
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    24
    25/**@file heur_trustregion.h
    26 * @ingroup PRIMALHEURISTICS
    27 * @brief Large neighborhood search heuristic for Benders' decomposition based on trust region methods
    28 * @author Stephen J. Maher
    29 *
    30 * The Trust Region heuristic draws upon trust region methods for solving optimization problems, especially in the
    31 * context of Benders' decomposition. This heuristic has been developed to improve the heuristic performance of the
    32 * Benders' decomposition algorithm within SCIP.
    33 *
    34 * The Trust Region heuristic copies the original SCIP instance and adds a constraint to penalize changes from the
    35 * incumbent solution. Consider a problem that includes a set of binary variables \f$\mathcal{B}\f$. Given a feasible
    36 * solution \f$\hat{x}\f$ to the original problem, we define the set \f$\mathcal{B}^{+}\f$ as the index set for the
    37 * binary variables that are 1 in the input solution and \f$\mathcal{B}^{-}\f$ as the index set for binary variables
    38 * that are 0. The trust region constraint, which is added to the sub-SCIP, is given by
    39 *
    40 * \f[
    41 * \sum_{i \in \mathcal{B}^{+}}(1 - x_{i}) + \sum_{i \in \mathcal{B}^{-}}x_{i} \le \theta
    42 * \f]
    43 *
    44 * The variable \f$\theta\f$ measure the distance, in terms of the binary variables, of candidate solutions to the input
    45 * solution.
    46 *
    47 * In addition, an upper bounding constraint is explicitly added to enforce a minimum improvement from the heuristic,
    48 * given by \f$f(x) \le f(\hat{x}) - \epsilon\f$. The parameter \f$\epsilon \ge 0\f$ denotes the minimum improvement
    49 * that must be achieved by the heuristic.
    50 *
    51 * The objective function is then modified to \f$f(x) + M\theta\f$, where \f$M\f$ is a parameter for penalizing the
    52 * distance of solutions from the input solution \f$\hat{x}\f$.
    53 *
    54 * If a new incumbent solution is found by this heuristic, then the Trust Region heuristic is immediately
    55 * re-executed with this new incumbent solution.
    56 */
    57
    58/*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/
    59
    60#ifndef __SCIP_HEUR_TRUSTREGION_H__
    61#define __SCIP_HEUR_TRUSTREGION_H__
    62
    63#include "scip/def.h"
    64#include "scip/type_retcode.h"
    65#include "scip/type_scip.h"
    66
    67#ifdef __cplusplus
    68extern "C" {
    69#endif
    70
    71/** creates local branching primal heuristic and includes it in SCIP
    72 *
    73 * @ingroup PrimalHeuristicIncludes
    74 */
    75SCIP_EXPORT
    77 SCIP* scip /**< SCIP data structure */
    78 );
    79
    80#ifdef __cplusplus
    81}
    82#endif
    83
    84#endif
    common defines and data types used in all packages of SCIP
    SCIP_RETCODE SCIPincludeHeurTrustregion(SCIP *scip)
    type definitions for return codes for SCIP methods
    enum SCIP_Retcode SCIP_RETCODE
    Definition: type_retcode.h:63
    type definitions for SCIP's main datastructure