/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ /* */ /* 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 prop_obbt.h * @ingroup PROPAGATORS * @brief optimization-based bound tightening propagator * @author Stefan Weltge * * In Optimization-Based Bound Tightening (OBBT), we solve auxiliary LPs of the form * \f[ * \min / \max \, \{ x_i \mid x \in P' \}, * \f] * where \f$P'\f$ is the current LP relaxation restricted by the primal cutoff constraint \f$c^T x <= z\f$, \f$z\f$ the * current cutoff bound. Trivially, the optimal objective value of this LP provides a valid lower/upper bound on * variable \f$x_i\f$. * * Since solving LPs may be expensive, the propagator inspects solutions \f$x \in P'\f$ and does not run for variable * bounds which are tight at \f$x\f$: First, we check SCIP's last LP relaxation solution. Second, we solve a sequence of * filtering LP's \f$\min / \max \, \{ \sum w_i \, x_i \mid x \in P' \}\f$ in order to push several variables towards * one of their bounds in one LP solve. Third, we inspect all solutions of the auxiliary LPs solved along the way. * * By default, OBBT is only applied for nonbinary variables that occur in nonlinear constraints. * * After we learned a better variable bound the propagator tries to separate the solution of the current OBBT LP with * the refined outer approximation in order to strengthen the learned bound. Additionally, we trigger a * propagation round of SCIP after a fixed number of learned bound tightenings. * * Additionally, the propagator uses the dual solution of the auxiliary LPs to construct globally valid generalized * variable bounds which may be propagated during the branch-and-bound search. */ /*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/ #ifndef __SCIP_PROP_OBBT_H__ #define __SCIP_PROP_OBBT_H__ #include "scip/def.h" #include "scip/type_retcode.h" #include "scip/type_scip.h" #ifdef __cplusplus extern "C" { #endif /** creates the obbt propagator and includes it in SCIP * * @ingroup PropagatorIncludes */ SCIP_EXPORT SCIP_RETCODE SCIPincludePropObbt( SCIP* scip /**< SCIP data structure */ ); #ifdef __cplusplus } #endif #endif