/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ /* */ /* This file is part of the program and library */ /* SCIP --- Solving Constraint Integer Programs */ /* */ /* Copyright 2002-2022 Zuse Institute Berlin */ /* */ /* 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. */ /* */ /* You should have received a copy of the Apache-2.0 license */ /* along with SCIP; see the file LICENSE. If not visit scipopt.org. */ /* */ /* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ /**@file heuristics.h * @ingroup PUBLICCOREAPI * @brief methods commonly used by primal heuristics * @author Gregor Hendel */ /*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/ #ifndef __SCIP_HEURISTICS_H__ #define __SCIP_HEURISTICS_H__ #include "scip/def.h" #include "scip/type_scip.h" #include "scip/type_heur.h" #include "scip/type_misc.h" #include "scip/type_retcode.h" #include "scip/type_sol.h" #include "scip/type_var.h" #ifdef __cplusplus extern "C" { #endif /**@defgroup PublicSpecialHeuristicMethods Special Methods * @ingroup PublicHeuristicMethods * @brief methods commonly used by primal heuristics * * @{ */ /** performs a diving within the limits of the @p diveset parameters * * This method performs a diving according to the settings defined by the diving settings @p diveset; Contrary to the * name, SCIP enters probing mode (not diving mode) and dives along a path into the tree. Domain propagation * is applied at every node in the tree, whereas probing LPs might be solved less frequently. * * Starting from the current LP solution, the algorithm selects candidates which maximize the * score defined by the @p diveset and whose solution value has not yet been rendered infeasible by propagation, * and propagates the bound change on this candidate. * * The algorithm iteratively selects the the next (unfixed) candidate in the list, until either enough domain changes * or the resolve frequency of the LP trigger an LP resolve (and hence, the set of potential candidates changes), * or the last node is proven to be infeasible. It optionally backtracks and tries the * other branching direction. * * After the set of remaining candidates is empty or the targeted depth is reached, the node LP is * solved, and the old candidates are replaced by the new LP candidates. * * @see heur_guideddiving.c for an example implementation of a dive set controlling the diving algorithm. * * @note the node from where the algorithm is called is checked for a basic LP solution. If the solution * is non-basic, e.g., when barrier without crossover is used, the method returns without performing a dive. * * @note currently, when multiple diving heuristics call this method and solve an LP at the same node, only the first * call will be executed, @see SCIPgetLastDiveNode(). */ SCIP_EXPORT SCIP_RETCODE SCIPperformGenericDivingAlgorithm( SCIP* scip, /**< SCIP data structure */ SCIP_DIVESET* diveset, /**< settings for diving */ SCIP_SOL* worksol, /**< non-NULL working solution */ SCIP_HEUR* heur, /**< the calling primal heuristic */ SCIP_RESULT* result, /**< SCIP result pointer */ SCIP_Bool nodeinfeasible, /**< is the current node known to be infeasible? */ SCIP_Longint iterlim, /**< nonnegative iteration limit for the LP solves, or -1 for dynamic setting */ SCIP_DIVECONTEXT divecontext /**< context for diving statistics */ ); /** get a sub-SCIP copy of the transformed problem */ SCIP_EXPORT SCIP_RETCODE SCIPcopyLargeNeighborhoodSearch( SCIP* sourcescip, /**< source SCIP data structure */ SCIP* subscip, /**< sub-SCIP used by the heuristic */ SCIP_HASHMAP* varmap, /**< a hashmap to store the mapping of source variables to the corresponding * target variables */ const char* suffix, /**< suffix for the problem name */ SCIP_VAR** fixedvars, /**< source variables whose copies should be fixed in the target SCIP environment, or NULL */ SCIP_Real* fixedvals, /**< array of fixing values for target SCIP variables, or NULL */ int nfixedvars, /**< number of source variables whose copies should be fixed in the target SCIP environment, or NULL */ SCIP_Bool uselprows, /**< should the linear relaxation of the problem defined by LP rows be copied? */ SCIP_Bool copycuts, /**< should cuts be copied (only if uselprows == FALSE) */ SCIP_Bool* success, /**< was the copying successful? */ SCIP_Bool* valid /**< pointer to store whether the copying was valid, or NULL */ ); /** adds a trust region neighborhood constraint to the @p targetscip * * a trust region constraint measures the deviation from the current incumbent solution \f$x^*\f$ by an auxiliary * continuous variable \f$v \geq 0\f$: * \f[ * \sum\limits_{j\in B} |x_j^* - x_j| = v * \f] * Only binary variables are taken into account. The deviation is penalized in the objective function using * a positive \p violpenalty. * * @note: the trust region constraint creates an auxiliary variable to penalize the deviation from * the current incumbent solution. This variable can afterwards be accessed using SCIPfindVar() by its name * 'trustregion_violationvar' */ SCIP_EXPORT SCIP_RETCODE SCIPaddTrustregionNeighborhoodConstraint( SCIP* scip, /**< the SCIP data structure */ SCIP* subscip, /**< SCIP data structure of the subproblem */ SCIP_VAR** subvars, /**< variables of the subproblem, NULL entries are ignored */ SCIP_Real violpenalty /**< the penalty for violating the trust region */ ); /** @} */ #ifdef __cplusplus } #endif #endif