/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ /* */ /* 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 sepa_minor.h * @ingroup SEPARATORS * @brief principal minor separator * @author Benjamin Mueller * * This separator detects all principal minors of the matrix \f$ xx' \f$ for which all auxiliary variables \f$ X \f$ * exist, i.e., two indices \f$ i \neq j \f$ such that \f$ X_{ii} \f$, \f$ X_{jj} \f$, and \f$ X_{ij} \f$ exist. Because * \f$ X - xx' \f$ is required to be positive semi-definite, it follows that the matrix * * \f[ * A(x,X) = \begin{bmatrix} 1 & x_i & x_j \\ x_i & X_{ii} & X_{ij} \\ x_j & X_{ij} & X_{jj} \end{bmatrix} * \f] * * is also required to be positive semi-definite. Let \f$ v \f$ be a negative eigenvector for \f$ A(x^*,X^*) \f$ in a * point \f$ (x^*,X^*) \f$, which implies that \f$ v' A(x^*,X^*) v < 0 \f$. To cut off \f$ (x^*,X^*) \f$, the separator * computes the globally valid linear inequality \f$ v' A(x,X) v \ge 0 \f$. * * * To identify which entries of the matrix X exist, we (the separator) iterate over the available nonlinear constraints. * For each constraint, we explore its expression and collect all nodes (expressions) of the form * - \f$x^2\f$ * - \f$y \cdot z\f$ * * Then, we go through the found bilinear terms \f$(yz)\f$ and if the corresponding \f$y^2\f$ and \f$z^2\f$ exist, then we have found * a minor. * * For circle packing instances, the minor cuts are not really helpful (see [Packing circles in a square: a theoretical * comparison of various convexification techniques](http://www.optimization-online.org/DB_HTML/2017/03/5911.html)). * Furthermore, the performance was negatively affected, thus circle packing constraint are identified and ignored in * the above algorithm. This behavior is controlled with the parameter "separating/minor/ignorepackingconss". */ /*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/ #ifndef __SCIP_SEPA_MINOR_H__ #define __SCIP_SEPA_MINOR_H__ #include "scip/scip.h" #ifdef __cplusplus extern "C" { #endif /** creates the minor separator and includes it in SCIP * * @ingroup SeparatorIncludes */ SCIP_EXPORT SCIP_RETCODE SCIPincludeSepaMinor( SCIP* scip /**< SCIP data structure */ ); /**@addtogroup SEPARATORS * * @{ */ /** @} */ #ifdef __cplusplus } #endif #endif