1 /* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ 2 /* */ 3 /* This file is part of the program and library */ 4 /* SCIP --- Solving Constraint Integer Programs */ 5 /* */ 6 /* Copyright (c) 2002-2023 Zuse Institute Berlin (ZIB) */ 7 /* */ 8 /* Licensed under the Apache License, Version 2.0 (the "License"); */ 9 /* you may not use this file except in compliance with the License. */ 10 /* You may obtain a copy of the License at */ 11 /* */ 12 /* http://www.apache.org/licenses/LICENSE-2.0 */ 13 /* */ 14 /* Unless required by applicable law or agreed to in writing, software */ 15 /* distributed under the License is distributed on an "AS IS" BASIS, */ 16 /* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. */ 17 /* See the License for the specific language governing permissions and */ 18 /* limitations under the License. */ 19 /* */ 20 /* You should have received a copy of the Apache-2.0 license */ 21 /* along with SCIP; see the file LICENSE. If not visit scipopt.org. */ 22 /* */ 23 /* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ 24 25 /**@file heur_gins.h 26 * @ingroup PRIMALHEURISTICS 27 * @brief LNS heuristic that tries to delimit the search region to a neighborhood in the constraint graph 28 * @author Gregor Hendel 29 * 30 * 31 * Graph Induced Neighborhood Search (GINS) is a Large Neighborhood Search Heuristic that attempts to improve 32 * an incumbent solution by fixing a suitable percentage of integer variables to the incumbent and 33 * solving the resulting, smaller and presumably easier sub-MIP. 34 * 35 * Its search neighborhoods are based on distances in a bipartite graph \f$G\f$ with the variables and constraints as nodes and 36 * an edge between a variable and a constraint, if the variable is part of the constraint. 37 * Given an integer \f$k\f$, the \f$k\f$-neighborhood of a variable \f$v\f$ in \f$G\f$ is the set of variables, whose nodes 38 * are connected to \f$v\f$ by a path not longer than \f$2 \cdot k\f$. Intuitively, a judiciously chosen neighborhood size 39 * allows to consider a local portion of the overall problem. 40 * 41 * An initial variable selection is made by randomly sampling different neighborhoods across the whole main problem. 42 * The neighborhood that offers the largest potential for improvement is selected to become the local search neighborhood, 43 * while all variables outside the neighborhood are fixed to their incumbent solution values. 44 * 45 * GINS also supports a rolling horizon approach, during which several local neighborhoods are considered 46 * with increasing distance to the variable selected for the initial sub-problem. The rolling horizon approach ends 47 * if no improvement could be found or a sufficient part of the problem component variables has been part of 48 * at least one neighborhood. 49 */ 50 51 /*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/ 52 53 #ifndef __SCIP_HEUR_GINS_H__ 54 #define __SCIP_HEUR_GINS_H__ 55 56 #include "scip/def.h" 57 #include "scip/type_retcode.h" 58 #include "scip/type_scip.h" 59 60 #ifdef __cplusplus 61 extern "C" { 62 #endif 63 64 /** creates the gins primal heuristic and includes it in SCIP 65 * 66 * @ingroup PrimalHeuristicIncludes 67 */ 68 SCIP_EXPORT 69 SCIP_RETCODE SCIPincludeHeurGins( 70 SCIP* scip /**< SCIP data structure */ 71 ); 72 73 #ifdef __cplusplus 74 } 75 #endif 76 77 #endif 78