benderscut_int.h
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30 * The classical Benders' decomposition algorithm is only applicable to problems with continuous second stage variables.
31 * Laporte and Louveaux (1993) developed a method for generating cuts when Benders' decomposition is applied to problem
32 * with discrete second stage variables. However, these cuts are only applicable when the master problem is a pure
35 * The integer optimality cuts are a point-wise underestimator of the optimal subproblem objective function value.
36 * Similar to benderscuts_opt.c, an auxiliary variable, \f$\varphi\f$. is required in the master problem as a lower
39 * Consider the Benders' decomposition subproblem that takes the master problem solution \f$\bar{x}\f$ as input:
43 * If the subproblem is feasible, and \f$z(\bar{x}) > \varphi\f$ (indicating that the current underestimators are not
44 * optimal) then the Benders' decomposition integer optimality cut can be generated from the optimal solution of the
45 * subproblem. Let \f$S_{r}\f$ be the set of indicies for master problem variables that are 1 in \f$\bar{x}\f$ and
53 * Laporte, G. & Louveaux, F. V. The integer L-shaped method for stochastic integer programs with complete recourse
57 /*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/
SCIP_RETCODE SCIPincludeBenderscutInt(SCIP *scip, SCIP_BENDERS *benders)
Definition: benderscut_int.c:617
Definition: struct_scip.h:68
type definitions for return codes for SCIP methods
Definition: struct_benders.h:57
type definitions for SCIP's main datastructure
type definitions for Benders' decomposition methods
common defines and data types used in all packages of SCIP
Definition: objbenders.h:43