/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ /* */ /* This file is part of the program and library */ /* SCIP --- Solving Constraint Integer Programs */ /* */ /* Copyright (C) 2002-2020 Konrad-Zuse-Zentrum */ /* fuer Informationstechnik Berlin */ /* */ /* SCIP is distributed under the terms of the ZIB Academic License. */ /* */ /* You should have received a copy of the ZIB Academic License */ /* along with SCIP; see the file COPYING. If not visit scipopt.org. */ /* */ /* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ /**@file nodesel_uct.h * @ingroup NODESELECTORS * @brief uct node selector which balances exploration and exploitation by considering node visits * @author Gregor Hendel * * the UCT node selection rule selects the next leaf according to a mixed score of the node's actual lower bound * and the number of times it has been visited so far compared to its parent node. * * The idea of UCT node selection for MIP appeared in: * Ashish Sabharwal and Horst Samulowitz * Guiding Combinatorial Optimization with UCT (2011) * * The authors adapted a game-tree exploration scheme called UCB to MIP trees. Starting from the root node as current node, * the algorithm selects the current node's child \f$N_i\f$ which maximizes the UCT score * * \f$ \mbox{score}(N_i) := -\mbox{estimate}_{N_i} + \mbox{weight} \cdot \frac{\mbox{visits}(\mbox{parent}(N_i))}{\mbox{visits}(N_i)} * \f$ * * where \f$\mbox{estimate}\f$ is the node's lower bound normalized by the root lower bound, and \f$\mbox{visits}\f$ * denotes the number of times a leaf in the subtree rooted at this node has been explored so far. * * The selected node in the sense of the SCIP node selection is the leaf reached by the above criterion. * * The authors suggest that this node selection rule is particularly useful at the beginning of the solving process, but * to switch to a different node selection after a number of nodes has been explored to reduce computational overhead. * Our implementation uses only information available from the original SCIP tree which does not support the * forward path mechanism needed for the most efficient node selection. Instead, the algorithm selects the next leaf * by looping over all leaves and comparing the best leaf found so far with the next one. Two leaves l_1, l_2 are compared * by following their paths back upwards until their deepest common ancestor \f$a\f$ is reached, together with the two * children of \f$a\f$ representing the two paths to l_1, l_2. The leaf represented by the child of \f$a\f$ * with higher UCT score is a candidate for the next selected leaf. * * The node selector features several parameters: * * the nodelimit delimits the number of explored nodes before UCT selection is turned off * the weight parameter changes the relevance of the visits quotient in the UCT score (see above score formula) * useestimate determines whether the node's estimate or lower bound is taken as estimate * * @note It should be avoided to switch to uct node selection after the branch and bound process has begun because * the central UCT score information how often a path was taken is not collected if UCT is inactive. A safe use of * UCT is to switch it on before SCIP starts optimization. */ /*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/ #ifndef __SCIP_NODESEL_UCT_H__ #define __SCIP_NODESEL_UCT_H__ #include "scip/def.h" #include "scip/type_retcode.h" #include "scip/type_scip.h" #ifdef __cplusplus extern "C" { #endif /** creates the uct node selector and includes it in SCIP * * @ingroup NodeSelectorIncludes */ SCIP_EXPORT SCIP_RETCODE SCIPincludeNodeselUct( SCIP* scip /**< SCIP data structure */ ); #ifdef __cplusplus } #endif #endif