kpeter@593: /* -*- mode: C++; indent-tabs-mode: nil; -*- kpeter@593: * kpeter@593: * This file is a part of LEMON, a generic C++ optimization library. kpeter@593: * kpeter@593: * Copyright (C) 2003-2009 kpeter@593: * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport kpeter@593: * (Egervary Research Group on Combinatorial Optimization, EGRES). kpeter@593: * kpeter@593: * Permission to use, modify and distribute this software is granted kpeter@593: * provided that this copyright notice appears in all copies. For kpeter@593: * precise terms see the accompanying LICENSE file. kpeter@593: * kpeter@593: * This software is provided "AS IS" with no warranty of any kind, kpeter@593: * express or implied, and with no claim as to its suitability for any kpeter@593: * purpose. kpeter@593: * kpeter@593: */ kpeter@593: kpeter@593: #ifndef LEMON_NETWORK_SIMPLEX_H kpeter@593: #define LEMON_NETWORK_SIMPLEX_H kpeter@593: kpeter@593: /// \ingroup min_cost_flow kpeter@593: /// kpeter@593: /// \file kpeter@593: /// \brief Network simplex algorithm for finding a minimum cost flow. kpeter@593: kpeter@593: #include kpeter@593: #include kpeter@593: #include kpeter@593: kpeter@593: #include kpeter@593: kpeter@593: namespace lemon { kpeter@593: kpeter@593: /// \addtogroup min_cost_flow kpeter@593: /// @{ kpeter@593: kpeter@593: /// \brief Implementation of the primal network simplex algorithm kpeter@593: /// for finding a \ref min_cost_flow "minimum cost flow". kpeter@593: /// kpeter@593: /// \ref NetworkSimplex implements the primal network simplex algorithm kpeter@593: /// for finding a \ref min_cost_flow "minimum cost flow". kpeter@593: /// kpeter@593: /// \tparam Digraph The digraph type the algorithm runs on. kpeter@593: /// \tparam LowerMap The type of the lower bound map. kpeter@593: /// \tparam CapacityMap The type of the capacity (upper bound) map. kpeter@593: /// \tparam CostMap The type of the cost (length) map. kpeter@593: /// \tparam SupplyMap The type of the supply map. kpeter@593: /// kpeter@593: /// \warning kpeter@593: /// - Arc capacities and costs should be \e non-negative \e integers. kpeter@593: /// - Supply values should be \e signed \e integers. kpeter@593: /// - The value types of the maps should be convertible to each other. kpeter@593: /// - \c CostMap::Value must be signed type. kpeter@593: /// kpeter@593: /// \note \ref NetworkSimplex provides five different pivot rule kpeter@593: /// implementations that significantly affect the efficiency of the kpeter@593: /// algorithm. kpeter@593: /// By default "Block Search" pivot rule is used, which proved to be kpeter@593: /// by far the most efficient according to our benchmark tests. kpeter@593: /// However another pivot rule can be selected using \ref run() kpeter@593: /// function with the proper parameter. kpeter@593: #ifdef DOXYGEN kpeter@593: template < typename Digraph, kpeter@593: typename LowerMap, kpeter@593: typename CapacityMap, kpeter@593: typename CostMap, kpeter@593: typename SupplyMap > kpeter@593: kpeter@593: #else kpeter@593: template < typename Digraph, kpeter@593: typename LowerMap = typename Digraph::template ArcMap, kpeter@593: typename CapacityMap = typename Digraph::template ArcMap, kpeter@593: typename CostMap = typename Digraph::template ArcMap, kpeter@593: typename SupplyMap = typename Digraph::template NodeMap > kpeter@593: #endif kpeter@593: class NetworkSimplex kpeter@593: { kpeter@593: TEMPLATE_DIGRAPH_TYPEDEFS(Digraph); kpeter@593: kpeter@593: typedef typename CapacityMap::Value Capacity; kpeter@593: typedef typename CostMap::Value Cost; kpeter@593: typedef typename SupplyMap::Value Supply; kpeter@593: kpeter@593: typedef std::vector ArcVector; kpeter@593: typedef std::vector NodeVector; kpeter@593: typedef std::vector IntVector; kpeter@593: typedef std::vector BoolVector; kpeter@593: typedef std::vector CapacityVector; kpeter@593: typedef std::vector CostVector; kpeter@593: typedef std::vector SupplyVector; kpeter@593: kpeter@593: public: kpeter@593: kpeter@593: /// The type of the flow map kpeter@593: typedef typename Digraph::template ArcMap FlowMap; kpeter@593: /// The type of the potential map kpeter@593: typedef typename Digraph::template NodeMap PotentialMap; kpeter@593: kpeter@593: public: kpeter@593: kpeter@593: /// Enum type for selecting the pivot rule used by \ref run() kpeter@593: enum PivotRuleEnum { kpeter@593: FIRST_ELIGIBLE_PIVOT, kpeter@593: BEST_ELIGIBLE_PIVOT, kpeter@593: BLOCK_SEARCH_PIVOT, kpeter@593: CANDIDATE_LIST_PIVOT, kpeter@593: ALTERING_LIST_PIVOT kpeter@593: }; kpeter@593: kpeter@593: private: kpeter@593: kpeter@593: // State constants for arcs kpeter@593: enum ArcStateEnum { kpeter@593: STATE_UPPER = -1, kpeter@593: STATE_TREE = 0, kpeter@593: STATE_LOWER = 1 kpeter@593: }; kpeter@593: kpeter@593: private: kpeter@593: kpeter@593: // References for the original data kpeter@593: const Digraph &_orig_graph; kpeter@593: const LowerMap *_orig_lower; kpeter@593: const CapacityMap &_orig_cap; kpeter@593: const CostMap &_orig_cost; kpeter@593: const SupplyMap *_orig_supply; kpeter@593: Node _orig_source; kpeter@593: Node _orig_target; kpeter@593: Capacity _orig_flow_value; kpeter@593: kpeter@593: // Result maps kpeter@593: FlowMap *_flow_result; kpeter@593: PotentialMap *_potential_result; kpeter@593: bool _local_flow; kpeter@593: bool _local_potential; kpeter@593: kpeter@593: // Data structures for storing the graph kpeter@593: ArcVector _arc; kpeter@593: NodeVector _node; kpeter@593: IntNodeMap _node_id; kpeter@593: IntVector _source; kpeter@593: IntVector _target; kpeter@593: kpeter@593: // The number of nodes and arcs in the original graph kpeter@593: int _node_num; kpeter@593: int _arc_num; kpeter@593: kpeter@593: // Node and arc maps kpeter@593: CapacityVector _cap; kpeter@593: CostVector _cost; kpeter@593: CostVector _supply; kpeter@593: CapacityVector _flow; kpeter@593: CostVector _pi; kpeter@593: kpeter@593: // Node and arc maps for the spanning tree structure kpeter@593: IntVector _depth; kpeter@593: IntVector _parent; kpeter@593: IntVector _pred; kpeter@593: IntVector _thread; kpeter@593: BoolVector _forward; kpeter@593: IntVector _state; kpeter@593: kpeter@593: // The root node kpeter@593: int _root; kpeter@593: kpeter@593: // The entering arc in the current pivot iteration kpeter@593: int _in_arc; kpeter@593: kpeter@593: // Temporary data used in the current pivot iteration kpeter@593: int join, u_in, v_in, u_out, v_out; kpeter@593: int right, first, second, last; kpeter@593: int stem, par_stem, new_stem; kpeter@593: Capacity delta; kpeter@593: kpeter@593: private: kpeter@593: kpeter@593: /// \brief Implementation of the "First Eligible" pivot rule for the kpeter@593: /// \ref NetworkSimplex "network simplex" algorithm. kpeter@593: /// kpeter@593: /// This class implements the "First Eligible" pivot rule kpeter@593: /// for the \ref NetworkSimplex "network simplex" algorithm. kpeter@593: /// kpeter@593: /// For more information see \ref NetworkSimplex::run(). kpeter@593: class FirstEligiblePivotRule kpeter@593: { kpeter@593: private: kpeter@593: kpeter@593: // References to the NetworkSimplex class kpeter@593: const ArcVector &_arc; kpeter@593: const IntVector &_source; kpeter@593: const IntVector &_target; kpeter@593: const CostVector &_cost; kpeter@593: const IntVector &_state; kpeter@593: const CostVector &_pi; kpeter@593: int &_in_arc; kpeter@593: int _arc_num; kpeter@593: kpeter@593: // Pivot rule data kpeter@593: int _next_arc; kpeter@593: kpeter@593: public: kpeter@593: kpeter@593: /// Constructor kpeter@593: FirstEligiblePivotRule(NetworkSimplex &ns) : kpeter@593: _arc(ns._arc), _source(ns._source), _target(ns._target), kpeter@593: _cost(ns._cost), _state(ns._state), _pi(ns._pi), kpeter@593: _in_arc(ns._in_arc), _arc_num(ns._arc_num), _next_arc(0) kpeter@593: {} kpeter@593: kpeter@593: /// Find next entering arc kpeter@593: bool findEnteringArc() { kpeter@593: Cost c; kpeter@593: for (int e = _next_arc; e < _arc_num; ++e) { kpeter@593: c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); kpeter@593: if (c < 0) { kpeter@593: _in_arc = e; kpeter@593: _next_arc = e + 1; kpeter@593: return true; kpeter@593: } kpeter@593: } kpeter@593: for (int e = 0; e < _next_arc; ++e) { kpeter@593: c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); kpeter@593: if (c < 0) { kpeter@593: _in_arc = e; kpeter@593: _next_arc = e + 1; kpeter@593: return true; kpeter@593: } kpeter@593: } kpeter@593: return false; kpeter@593: } kpeter@593: kpeter@593: }; //class FirstEligiblePivotRule kpeter@593: kpeter@593: kpeter@593: /// \brief Implementation of the "Best Eligible" pivot rule for the kpeter@593: /// \ref NetworkSimplex "network simplex" algorithm. kpeter@593: /// kpeter@593: /// This class implements the "Best Eligible" pivot rule kpeter@593: /// for the \ref NetworkSimplex "network simplex" algorithm. kpeter@593: /// kpeter@593: /// For more information see \ref NetworkSimplex::run(). kpeter@593: class BestEligiblePivotRule kpeter@593: { kpeter@593: private: kpeter@593: kpeter@593: // References to the NetworkSimplex class kpeter@593: const ArcVector &_arc; kpeter@593: const IntVector &_source; kpeter@593: const IntVector &_target; kpeter@593: const CostVector &_cost; kpeter@593: const IntVector &_state; kpeter@593: const CostVector &_pi; kpeter@593: int &_in_arc; kpeter@593: int _arc_num; kpeter@593: kpeter@593: public: kpeter@593: kpeter@593: /// Constructor kpeter@593: BestEligiblePivotRule(NetworkSimplex &ns) : kpeter@593: _arc(ns._arc), _source(ns._source), _target(ns._target), kpeter@593: _cost(ns._cost), _state(ns._state), _pi(ns._pi), kpeter@593: _in_arc(ns._in_arc), _arc_num(ns._arc_num) kpeter@593: {} kpeter@593: kpeter@593: /// Find next entering arc kpeter@593: bool findEnteringArc() { kpeter@593: Cost c, min = 0; kpeter@593: for (int e = 0; e < _arc_num; ++e) { kpeter@593: c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); kpeter@593: if (c < min) { kpeter@593: min = c; kpeter@593: _in_arc = e; kpeter@593: } kpeter@593: } kpeter@593: return min < 0; kpeter@593: } kpeter@593: kpeter@593: }; //class BestEligiblePivotRule kpeter@593: kpeter@593: kpeter@593: /// \brief Implementation of the "Block Search" pivot rule for the kpeter@593: /// \ref NetworkSimplex "network simplex" algorithm. kpeter@593: /// kpeter@593: /// This class implements the "Block Search" pivot rule kpeter@593: /// for the \ref NetworkSimplex "network simplex" algorithm. kpeter@593: /// kpeter@593: /// For more information see \ref NetworkSimplex::run(). kpeter@593: class BlockSearchPivotRule kpeter@593: { kpeter@593: private: kpeter@593: kpeter@593: // References to the NetworkSimplex class kpeter@593: const ArcVector &_arc; kpeter@593: const IntVector &_source; kpeter@593: const IntVector &_target; kpeter@593: const CostVector &_cost; kpeter@593: const IntVector &_state; kpeter@593: const CostVector &_pi; kpeter@593: int &_in_arc; kpeter@593: int _arc_num; kpeter@593: kpeter@593: // Pivot rule data kpeter@593: int _block_size; kpeter@593: int _next_arc; kpeter@593: kpeter@593: public: kpeter@593: kpeter@593: /// Constructor kpeter@593: BlockSearchPivotRule(NetworkSimplex &ns) : kpeter@593: _arc(ns._arc), _source(ns._source), _target(ns._target), kpeter@593: _cost(ns._cost), _state(ns._state), _pi(ns._pi), kpeter@593: _in_arc(ns._in_arc), _arc_num(ns._arc_num), _next_arc(0) kpeter@593: { kpeter@593: // The main parameters of the pivot rule kpeter@593: const double BLOCK_SIZE_FACTOR = 2.0; kpeter@593: const int MIN_BLOCK_SIZE = 10; kpeter@593: kpeter@593: _block_size = std::max( int(BLOCK_SIZE_FACTOR * sqrt(_arc_num)), kpeter@593: MIN_BLOCK_SIZE ); kpeter@593: } kpeter@593: kpeter@593: /// Find next entering arc kpeter@593: bool findEnteringArc() { kpeter@593: Cost c, min = 0; kpeter@593: int cnt = _block_size; kpeter@593: int e, min_arc = _next_arc; kpeter@593: for (e = _next_arc; e < _arc_num; ++e) { kpeter@593: c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); kpeter@593: if (c < min) { kpeter@593: min = c; kpeter@593: min_arc = e; kpeter@593: } kpeter@593: if (--cnt == 0) { kpeter@593: if (min < 0) break; kpeter@593: cnt = _block_size; kpeter@593: } kpeter@593: } kpeter@593: if (min == 0 || cnt > 0) { kpeter@593: for (e = 0; e < _next_arc; ++e) { kpeter@593: c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); kpeter@593: if (c < min) { kpeter@593: min = c; kpeter@593: min_arc = e; kpeter@593: } kpeter@593: if (--cnt == 0) { kpeter@593: if (min < 0) break; kpeter@593: cnt = _block_size; kpeter@593: } kpeter@593: } kpeter@593: } kpeter@593: if (min >= 0) return false; kpeter@593: _in_arc = min_arc; kpeter@593: _next_arc = e; kpeter@593: return true; kpeter@593: } kpeter@593: kpeter@593: }; //class BlockSearchPivotRule kpeter@593: kpeter@593: kpeter@593: /// \brief Implementation of the "Candidate List" pivot rule for the kpeter@593: /// \ref NetworkSimplex "network simplex" algorithm. kpeter@593: /// kpeter@593: /// This class implements the "Candidate List" pivot rule kpeter@593: /// for the \ref NetworkSimplex "network simplex" algorithm. kpeter@593: /// kpeter@593: /// For more information see \ref NetworkSimplex::run(). kpeter@593: class CandidateListPivotRule kpeter@593: { kpeter@593: private: kpeter@593: kpeter@593: // References to the NetworkSimplex class kpeter@593: const ArcVector &_arc; kpeter@593: const IntVector &_source; kpeter@593: const IntVector &_target; kpeter@593: const CostVector &_cost; kpeter@593: const IntVector &_state; kpeter@593: const CostVector &_pi; kpeter@593: int &_in_arc; kpeter@593: int _arc_num; kpeter@593: kpeter@593: // Pivot rule data kpeter@593: IntVector _candidates; kpeter@593: int _list_length, _minor_limit; kpeter@593: int _curr_length, _minor_count; kpeter@593: int _next_arc; kpeter@593: kpeter@593: public: kpeter@593: kpeter@593: /// Constructor kpeter@593: CandidateListPivotRule(NetworkSimplex &ns) : kpeter@593: _arc(ns._arc), _source(ns._source), _target(ns._target), kpeter@593: _cost(ns._cost), _state(ns._state), _pi(ns._pi), kpeter@593: _in_arc(ns._in_arc), _arc_num(ns._arc_num), _next_arc(0) kpeter@593: { kpeter@593: // The main parameters of the pivot rule kpeter@593: const double LIST_LENGTH_FACTOR = 1.0; kpeter@593: const int MIN_LIST_LENGTH = 10; kpeter@593: const double MINOR_LIMIT_FACTOR = 0.1; kpeter@593: const int MIN_MINOR_LIMIT = 3; kpeter@593: kpeter@593: _list_length = std::max( int(LIST_LENGTH_FACTOR * sqrt(_arc_num)), kpeter@593: MIN_LIST_LENGTH ); kpeter@593: _minor_limit = std::max( int(MINOR_LIMIT_FACTOR * _list_length), kpeter@593: MIN_MINOR_LIMIT ); kpeter@593: _curr_length = _minor_count = 0; kpeter@593: _candidates.resize(_list_length); kpeter@593: } kpeter@593: kpeter@593: /// Find next entering arc kpeter@593: bool findEnteringArc() { kpeter@593: Cost min, c; kpeter@593: int e, min_arc = _next_arc; kpeter@593: if (_curr_length > 0 && _minor_count < _minor_limit) { kpeter@593: // Minor iteration: select the best eligible arc from the kpeter@593: // current candidate list kpeter@593: ++_minor_count; kpeter@593: min = 0; kpeter@593: for (int i = 0; i < _curr_length; ++i) { kpeter@593: e = _candidates[i]; kpeter@593: c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); kpeter@593: if (c < min) { kpeter@593: min = c; kpeter@593: min_arc = e; kpeter@593: } kpeter@593: if (c >= 0) { kpeter@593: _candidates[i--] = _candidates[--_curr_length]; kpeter@593: } kpeter@593: } kpeter@593: if (min < 0) { kpeter@593: _in_arc = min_arc; kpeter@593: return true; kpeter@593: } kpeter@593: } kpeter@593: kpeter@593: // Major iteration: build a new candidate list kpeter@593: min = 0; kpeter@593: _curr_length = 0; kpeter@593: for (e = _next_arc; e < _arc_num; ++e) { kpeter@593: c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); kpeter@593: if (c < 0) { kpeter@593: _candidates[_curr_length++] = e; kpeter@593: if (c < min) { kpeter@593: min = c; kpeter@593: min_arc = e; kpeter@593: } kpeter@593: if (_curr_length == _list_length) break; kpeter@593: } kpeter@593: } kpeter@593: if (_curr_length < _list_length) { kpeter@593: for (e = 0; e < _next_arc; ++e) { kpeter@593: c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); kpeter@593: if (c < 0) { kpeter@593: _candidates[_curr_length++] = e; kpeter@593: if (c < min) { kpeter@593: min = c; kpeter@593: min_arc = e; kpeter@593: } kpeter@593: if (_curr_length == _list_length) break; kpeter@593: } kpeter@593: } kpeter@593: } kpeter@593: if (_curr_length == 0) return false; kpeter@593: _minor_count = 1; kpeter@593: _in_arc = min_arc; kpeter@593: _next_arc = e; kpeter@593: return true; kpeter@593: } kpeter@593: kpeter@593: }; //class CandidateListPivotRule kpeter@593: kpeter@593: kpeter@593: /// \brief Implementation of the "Altering Candidate List" pivot rule kpeter@593: /// for the \ref NetworkSimplex "network simplex" algorithm. kpeter@593: /// kpeter@593: /// This class implements the "Altering Candidate List" pivot rule kpeter@593: /// for the \ref NetworkSimplex "network simplex" algorithm. kpeter@593: /// kpeter@593: /// For more information see \ref NetworkSimplex::run(). kpeter@593: class AlteringListPivotRule kpeter@593: { kpeter@593: private: kpeter@593: kpeter@593: // References to the NetworkSimplex class kpeter@593: const ArcVector &_arc; kpeter@593: const IntVector &_source; kpeter@593: const IntVector &_target; kpeter@593: const CostVector &_cost; kpeter@593: const IntVector &_state; kpeter@593: const CostVector &_pi; kpeter@593: int &_in_arc; kpeter@593: int _arc_num; kpeter@593: kpeter@593: // Pivot rule data kpeter@593: int _block_size, _head_length, _curr_length; kpeter@593: int _next_arc; kpeter@593: IntVector _candidates; kpeter@593: CostVector _cand_cost; kpeter@593: kpeter@593: // Functor class to compare arcs during sort of the candidate list kpeter@593: class SortFunc kpeter@593: { kpeter@593: private: kpeter@593: const CostVector &_map; kpeter@593: public: kpeter@593: SortFunc(const CostVector &map) : _map(map) {} kpeter@593: bool operator()(int left, int right) { kpeter@593: return _map[left] > _map[right]; kpeter@593: } kpeter@593: }; kpeter@593: kpeter@593: SortFunc _sort_func; kpeter@593: kpeter@593: public: kpeter@593: kpeter@593: /// Constructor kpeter@593: AlteringListPivotRule(NetworkSimplex &ns) : kpeter@593: _arc(ns._arc), _source(ns._source), _target(ns._target), kpeter@593: _cost(ns._cost), _state(ns._state), _pi(ns._pi), kpeter@593: _in_arc(ns._in_arc), _arc_num(ns._arc_num), kpeter@593: _next_arc(0), _cand_cost(ns._arc_num), _sort_func(_cand_cost) kpeter@593: { kpeter@593: // The main parameters of the pivot rule kpeter@593: const double BLOCK_SIZE_FACTOR = 1.5; kpeter@593: const int MIN_BLOCK_SIZE = 10; kpeter@593: const double HEAD_LENGTH_FACTOR = 0.1; kpeter@593: const int MIN_HEAD_LENGTH = 3; kpeter@593: kpeter@593: _block_size = std::max( int(BLOCK_SIZE_FACTOR * sqrt(_arc_num)), kpeter@593: MIN_BLOCK_SIZE ); kpeter@593: _head_length = std::max( int(HEAD_LENGTH_FACTOR * _block_size), kpeter@593: MIN_HEAD_LENGTH ); kpeter@593: _candidates.resize(_head_length + _block_size); kpeter@593: _curr_length = 0; kpeter@593: } kpeter@593: kpeter@593: /// Find next entering arc kpeter@593: bool findEnteringArc() { kpeter@593: // Check the current candidate list kpeter@593: int e; kpeter@593: for (int i = 0; i < _curr_length; ++i) { kpeter@593: e = _candidates[i]; kpeter@593: _cand_cost[e] = _state[e] * kpeter@593: (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); kpeter@593: if (_cand_cost[e] >= 0) { kpeter@593: _candidates[i--] = _candidates[--_curr_length]; kpeter@593: } kpeter@593: } kpeter@593: kpeter@593: // Extend the list kpeter@593: int cnt = _block_size; kpeter@593: int last_edge = 0; kpeter@593: int limit = _head_length; kpeter@593: kpeter@593: for (int e = _next_arc; e < _arc_num; ++e) { kpeter@593: _cand_cost[e] = _state[e] * kpeter@593: (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); kpeter@593: if (_cand_cost[e] < 0) { kpeter@593: _candidates[_curr_length++] = e; kpeter@593: last_edge = e; kpeter@593: } kpeter@593: if (--cnt == 0) { kpeter@593: if (_curr_length > limit) break; kpeter@593: limit = 0; kpeter@593: cnt = _block_size; kpeter@593: } kpeter@593: } kpeter@593: if (_curr_length <= limit) { kpeter@593: for (int e = 0; e < _next_arc; ++e) { kpeter@593: _cand_cost[e] = _state[e] * kpeter@593: (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); kpeter@593: if (_cand_cost[e] < 0) { kpeter@593: _candidates[_curr_length++] = e; kpeter@593: last_edge = e; kpeter@593: } kpeter@593: if (--cnt == 0) { kpeter@593: if (_curr_length > limit) break; kpeter@593: limit = 0; kpeter@593: cnt = _block_size; kpeter@593: } kpeter@593: } kpeter@593: } kpeter@593: if (_curr_length == 0) return false; kpeter@593: _next_arc = last_edge + 1; kpeter@593: kpeter@593: // Make heap of the candidate list (approximating a partial sort) kpeter@593: make_heap( _candidates.begin(), _candidates.begin() + _curr_length, kpeter@593: _sort_func ); kpeter@593: kpeter@593: // Pop the first element of the heap kpeter@593: _in_arc = _candidates[0]; kpeter@593: pop_heap( _candidates.begin(), _candidates.begin() + _curr_length, kpeter@593: _sort_func ); kpeter@593: _curr_length = std::min(_head_length, _curr_length - 1); kpeter@593: return true; kpeter@593: } kpeter@593: kpeter@593: }; //class AlteringListPivotRule kpeter@593: kpeter@593: public: kpeter@593: kpeter@593: /// \brief General constructor (with lower bounds). kpeter@593: /// kpeter@593: /// General constructor (with lower bounds). kpeter@593: /// kpeter@593: /// \param digraph The digraph the algorithm runs on. kpeter@593: /// \param lower The lower bounds of the arcs. kpeter@593: /// \param capacity The capacities (upper bounds) of the arcs. kpeter@593: /// \param cost The cost (length) values of the arcs. kpeter@593: /// \param supply The supply values of the nodes (signed). kpeter@593: NetworkSimplex( const Digraph &digraph, kpeter@593: const LowerMap &lower, kpeter@593: const CapacityMap &capacity, kpeter@593: const CostMap &cost, kpeter@593: const SupplyMap &supply ) : kpeter@593: _orig_graph(digraph), _orig_lower(&lower), _orig_cap(capacity), kpeter@593: _orig_cost(cost), _orig_supply(&supply), kpeter@593: _flow_result(NULL), _potential_result(NULL), kpeter@593: _local_flow(false), _local_potential(false), kpeter@593: _node_id(digraph) kpeter@593: {} kpeter@593: kpeter@593: /// \brief General constructor (without lower bounds). kpeter@593: /// kpeter@593: /// General constructor (without lower bounds). kpeter@593: /// kpeter@593: /// \param digraph The digraph the algorithm runs on. kpeter@593: /// \param capacity The capacities (upper bounds) of the arcs. kpeter@593: /// \param cost The cost (length) values of the arcs. kpeter@593: /// \param supply The supply values of the nodes (signed). kpeter@593: NetworkSimplex( const Digraph &digraph, kpeter@593: const CapacityMap &capacity, kpeter@593: const CostMap &cost, kpeter@593: const SupplyMap &supply ) : kpeter@593: _orig_graph(digraph), _orig_lower(NULL), _orig_cap(capacity), kpeter@593: _orig_cost(cost), _orig_supply(&supply), kpeter@593: _flow_result(NULL), _potential_result(NULL), kpeter@593: _local_flow(false), _local_potential(false), kpeter@593: _node_id(digraph) kpeter@593: {} kpeter@593: kpeter@593: /// \brief Simple constructor (with lower bounds). kpeter@593: /// kpeter@593: /// Simple constructor (with lower bounds). kpeter@593: /// kpeter@593: /// \param digraph The digraph the algorithm runs on. kpeter@593: /// \param lower The lower bounds of the arcs. kpeter@593: /// \param capacity The capacities (upper bounds) of the arcs. kpeter@593: /// \param cost The cost (length) values of the arcs. kpeter@593: /// \param s The source node. kpeter@593: /// \param t The target node. kpeter@593: /// \param flow_value The required amount of flow from node \c s kpeter@593: /// to node \c t (i.e. the supply of \c s and the demand of \c t). kpeter@593: NetworkSimplex( const Digraph &digraph, kpeter@593: const LowerMap &lower, kpeter@593: const CapacityMap &capacity, kpeter@593: const CostMap &cost, kpeter@593: Node s, Node t, kpeter@593: Capacity flow_value ) : kpeter@593: _orig_graph(digraph), _orig_lower(&lower), _orig_cap(capacity), kpeter@593: _orig_cost(cost), _orig_supply(NULL), kpeter@593: _orig_source(s), _orig_target(t), _orig_flow_value(flow_value), kpeter@593: _flow_result(NULL), _potential_result(NULL), kpeter@593: _local_flow(false), _local_potential(false), kpeter@593: _node_id(digraph) kpeter@593: {} kpeter@593: kpeter@593: /// \brief Simple constructor (without lower bounds). kpeter@593: /// kpeter@593: /// Simple constructor (without lower bounds). kpeter@593: /// kpeter@593: /// \param digraph The digraph the algorithm runs on. kpeter@593: /// \param capacity The capacities (upper bounds) of the arcs. kpeter@593: /// \param cost The cost (length) values of the arcs. kpeter@593: /// \param s The source node. kpeter@593: /// \param t The target node. kpeter@593: /// \param flow_value The required amount of flow from node \c s kpeter@593: /// to node \c t (i.e. the supply of \c s and the demand of \c t). kpeter@593: NetworkSimplex( const Digraph &digraph, kpeter@593: const CapacityMap &capacity, kpeter@593: const CostMap &cost, kpeter@593: Node s, Node t, kpeter@593: Capacity flow_value ) : kpeter@593: _orig_graph(digraph), _orig_lower(NULL), _orig_cap(capacity), kpeter@593: _orig_cost(cost), _orig_supply(NULL), kpeter@593: _orig_source(s), _orig_target(t), _orig_flow_value(flow_value), kpeter@593: _flow_result(NULL), _potential_result(NULL), kpeter@593: _local_flow(false), _local_potential(false), kpeter@593: _node_id(digraph) kpeter@593: {} kpeter@593: kpeter@593: /// Destructor. kpeter@593: ~NetworkSimplex() { kpeter@593: if (_local_flow) delete _flow_result; kpeter@593: if (_local_potential) delete _potential_result; kpeter@593: } kpeter@593: kpeter@593: /// \brief Set the flow map. kpeter@593: /// kpeter@593: /// This function sets the flow map. kpeter@593: /// kpeter@593: /// \return (*this) kpeter@593: NetworkSimplex& flowMap(FlowMap &map) { kpeter@593: if (_local_flow) { kpeter@593: delete _flow_result; kpeter@593: _local_flow = false; kpeter@593: } kpeter@593: _flow_result = ↦ kpeter@593: return *this; kpeter@593: } kpeter@593: kpeter@593: /// \brief Set the potential map. kpeter@593: /// kpeter@593: /// This function sets the potential map. kpeter@593: /// kpeter@593: /// \return (*this) kpeter@593: NetworkSimplex& potentialMap(PotentialMap &map) { kpeter@593: if (_local_potential) { kpeter@593: delete _potential_result; kpeter@593: _local_potential = false; kpeter@593: } kpeter@593: _potential_result = ↦ kpeter@593: return *this; kpeter@593: } kpeter@593: kpeter@593: /// \name Execution control kpeter@593: /// The algorithm can be executed using the kpeter@593: /// \ref lemon::NetworkSimplex::run() "run()" function. kpeter@593: /// @{ kpeter@593: kpeter@593: /// \brief Run the algorithm. kpeter@593: /// kpeter@593: /// This function runs the algorithm. kpeter@593: /// kpeter@593: /// \param pivot_rule The pivot rule that is used during the kpeter@593: /// algorithm. kpeter@593: /// kpeter@593: /// The available pivot rules: kpeter@593: /// kpeter@593: /// - FIRST_ELIGIBLE_PIVOT The next eligible arc is selected in kpeter@593: /// a wraparound fashion in every iteration kpeter@593: /// (\ref FirstEligiblePivotRule). kpeter@593: /// kpeter@593: /// - BEST_ELIGIBLE_PIVOT The best eligible arc is selected in kpeter@593: /// every iteration (\ref BestEligiblePivotRule). kpeter@593: /// kpeter@593: /// - BLOCK_SEARCH_PIVOT A specified number of arcs are examined in kpeter@593: /// every iteration in a wraparound fashion and the best eligible kpeter@593: /// arc is selected from this block (\ref BlockSearchPivotRule). kpeter@593: /// kpeter@593: /// - CANDIDATE_LIST_PIVOT In a major iteration a candidate list is kpeter@593: /// built from eligible arcs in a wraparound fashion and in the kpeter@593: /// following minor iterations the best eligible arc is selected kpeter@593: /// from this list (\ref CandidateListPivotRule). kpeter@593: /// kpeter@593: /// - ALTERING_LIST_PIVOT It is a modified version of the kpeter@593: /// "Candidate List" pivot rule. It keeps only the several best kpeter@593: /// eligible arcs from the former candidate list and extends this kpeter@593: /// list in every iteration (\ref AlteringListPivotRule). kpeter@593: /// kpeter@593: /// According to our comprehensive benchmark tests the "Block Search" kpeter@593: /// pivot rule proved to be the fastest and the most robust on kpeter@593: /// various test inputs. Thus it is the default option. kpeter@593: /// kpeter@593: /// \return \c true if a feasible flow can be found. kpeter@593: bool run(PivotRuleEnum pivot_rule = BLOCK_SEARCH_PIVOT) { kpeter@593: return init() && start(pivot_rule); kpeter@593: } kpeter@593: kpeter@593: /// @} kpeter@593: kpeter@593: /// \name Query Functions kpeter@593: /// The results of the algorithm can be obtained using these kpeter@593: /// functions.\n kpeter@593: /// \ref lemon::NetworkSimplex::run() "run()" must be called before kpeter@593: /// using them. kpeter@593: /// @{ kpeter@593: kpeter@593: /// \brief Return a const reference to the flow map. kpeter@593: /// kpeter@593: /// This function returns a const reference to an arc map storing kpeter@593: /// the found flow. kpeter@593: /// kpeter@593: /// \pre \ref run() must be called before using this function. kpeter@593: const FlowMap& flowMap() const { kpeter@593: return *_flow_result; kpeter@593: } kpeter@593: kpeter@593: /// \brief Return a const reference to the potential map kpeter@593: /// (the dual solution). kpeter@593: /// kpeter@593: /// This function returns a const reference to a node map storing kpeter@593: /// the found potentials (the dual solution). kpeter@593: /// kpeter@593: /// \pre \ref run() must be called before using this function. kpeter@593: const PotentialMap& potentialMap() const { kpeter@593: return *_potential_result; kpeter@593: } kpeter@593: kpeter@593: /// \brief Return the flow on the given arc. kpeter@593: /// kpeter@593: /// This function returns the flow on the given arc. kpeter@593: /// kpeter@593: /// \pre \ref run() must be called before using this function. kpeter@593: Capacity flow(const Arc& arc) const { kpeter@593: return (*_flow_result)[arc]; kpeter@593: } kpeter@593: kpeter@593: /// \brief Return the potential of the given node. kpeter@593: /// kpeter@593: /// This function returns the potential of the given node. kpeter@593: /// kpeter@593: /// \pre \ref run() must be called before using this function. kpeter@593: Cost potential(const Node& node) const { kpeter@593: return (*_potential_result)[node]; kpeter@593: } kpeter@593: kpeter@593: /// \brief Return the total cost of the found flow. kpeter@593: /// kpeter@593: /// This function returns the total cost of the found flow. kpeter@593: /// The complexity of the function is \f$ O(e) \f$. kpeter@593: /// kpeter@593: /// \pre \ref run() must be called before using this function. kpeter@593: Cost totalCost() const { kpeter@593: Cost c = 0; kpeter@593: for (ArcIt e(_orig_graph); e != INVALID; ++e) kpeter@593: c += (*_flow_result)[e] * _orig_cost[e]; kpeter@593: return c; kpeter@593: } kpeter@593: kpeter@593: /// @} kpeter@593: kpeter@593: private: kpeter@593: kpeter@593: // Initialize internal data structures kpeter@593: bool init() { kpeter@593: // Initialize result maps kpeter@593: if (!_flow_result) { kpeter@593: _flow_result = new FlowMap(_orig_graph); kpeter@593: _local_flow = true; kpeter@593: } kpeter@593: if (!_potential_result) { kpeter@593: _potential_result = new PotentialMap(_orig_graph); kpeter@593: _local_potential = true; kpeter@593: } kpeter@593: kpeter@593: // Initialize vectors kpeter@593: _node_num = countNodes(_orig_graph); kpeter@593: _arc_num = countArcs(_orig_graph); kpeter@593: int all_node_num = _node_num + 1; kpeter@593: int all_edge_num = _arc_num + _node_num; kpeter@593: kpeter@593: _arc.resize(_arc_num); kpeter@593: _node.reserve(_node_num); kpeter@593: _source.resize(all_edge_num); kpeter@593: _target.resize(all_edge_num); kpeter@593: kpeter@593: _cap.resize(all_edge_num); kpeter@593: _cost.resize(all_edge_num); kpeter@593: _supply.resize(all_node_num); kpeter@593: _flow.resize(all_edge_num, 0); kpeter@593: _pi.resize(all_node_num, 0); kpeter@593: kpeter@593: _depth.resize(all_node_num); kpeter@593: _parent.resize(all_node_num); kpeter@593: _pred.resize(all_node_num); kpeter@593: _thread.resize(all_node_num); kpeter@593: _forward.resize(all_node_num); kpeter@593: _state.resize(all_edge_num, STATE_LOWER); kpeter@593: kpeter@593: // Initialize node related data kpeter@593: bool valid_supply = true; kpeter@593: if (_orig_supply) { kpeter@593: Supply sum = 0; kpeter@593: int i = 0; kpeter@593: for (NodeIt n(_orig_graph); n != INVALID; ++n, ++i) { kpeter@593: _node.push_back(n); kpeter@593: _node_id[n] = i; kpeter@593: _supply[i] = (*_orig_supply)[n]; kpeter@593: sum += _supply[i]; kpeter@593: } kpeter@593: valid_supply = (sum == 0); kpeter@593: } else { kpeter@593: int i = 0; kpeter@593: for (NodeIt n(_orig_graph); n != INVALID; ++n, ++i) { kpeter@593: _node.push_back(n); kpeter@593: _node_id[n] = i; kpeter@593: _supply[i] = 0; kpeter@593: } kpeter@593: _supply[_node_id[_orig_source]] = _orig_flow_value; kpeter@593: _supply[_node_id[_orig_target]] = -_orig_flow_value; kpeter@593: } kpeter@593: if (!valid_supply) return false; kpeter@593: kpeter@593: // Set data for the artificial root node kpeter@593: _root = _node_num; kpeter@593: _depth[_root] = 0; kpeter@593: _parent[_root] = -1; kpeter@593: _pred[_root] = -1; kpeter@593: _thread[_root] = 0; kpeter@593: _supply[_root] = 0; kpeter@593: _pi[_root] = 0; kpeter@593: kpeter@593: // Store the arcs in a mixed order kpeter@593: int k = std::max(int(sqrt(_arc_num)), 10); kpeter@593: int i = 0; kpeter@593: for (ArcIt e(_orig_graph); e != INVALID; ++e) { kpeter@593: _arc[i] = e; kpeter@593: if ((i += k) >= _arc_num) i = (i % k) + 1; kpeter@593: } kpeter@593: kpeter@593: // Initialize arc maps kpeter@593: for (int i = 0; i != _arc_num; ++i) { kpeter@593: Arc e = _arc[i]; kpeter@593: _source[i] = _node_id[_orig_graph.source(e)]; kpeter@593: _target[i] = _node_id[_orig_graph.target(e)]; kpeter@593: _cost[i] = _orig_cost[e]; kpeter@593: _cap[i] = _orig_cap[e]; kpeter@593: } kpeter@593: kpeter@593: // Remove non-zero lower bounds kpeter@593: if (_orig_lower) { kpeter@593: for (int i = 0; i != _arc_num; ++i) { kpeter@593: Capacity c = (*_orig_lower)[_arc[i]]; kpeter@593: if (c != 0) { kpeter@593: _cap[i] -= c; kpeter@593: _supply[_source[i]] -= c; kpeter@593: _supply[_target[i]] += c; kpeter@593: } kpeter@593: } kpeter@593: } kpeter@593: kpeter@593: // Add artificial arcs and initialize the spanning tree data structure kpeter@593: Cost max_cost = std::numeric_limits::max() / 4; kpeter@593: Capacity max_cap = std::numeric_limits::max(); kpeter@593: for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) { kpeter@593: _thread[u] = u + 1; kpeter@593: _depth[u] = 1; kpeter@593: _parent[u] = _root; kpeter@593: _pred[u] = e; kpeter@593: if (_supply[u] >= 0) { kpeter@593: _flow[e] = _supply[u]; kpeter@593: _forward[u] = true; kpeter@593: _pi[u] = -max_cost; kpeter@593: } else { kpeter@593: _flow[e] = -_supply[u]; kpeter@593: _forward[u] = false; kpeter@593: _pi[u] = max_cost; kpeter@593: } kpeter@593: _cost[e] = max_cost; kpeter@593: _cap[e] = max_cap; kpeter@593: _state[e] = STATE_TREE; kpeter@593: } kpeter@593: kpeter@593: return true; kpeter@593: } kpeter@593: kpeter@593: // Find the join node kpeter@593: void findJoinNode() { kpeter@593: int u = _source[_in_arc]; kpeter@593: int v = _target[_in_arc]; kpeter@593: while (_depth[u] > _depth[v]) u = _parent[u]; kpeter@593: while (_depth[v] > _depth[u]) v = _parent[v]; kpeter@593: while (u != v) { kpeter@593: u = _parent[u]; kpeter@593: v = _parent[v]; kpeter@593: } kpeter@593: join = u; kpeter@593: } kpeter@593: kpeter@593: // Find the leaving arc of the cycle and returns true if the kpeter@593: // leaving arc is not the same as the entering arc kpeter@593: bool findLeavingArc() { kpeter@593: // Initialize first and second nodes according to the direction kpeter@593: // of the cycle kpeter@593: if (_state[_in_arc] == STATE_LOWER) { kpeter@593: first = _source[_in_arc]; kpeter@593: second = _target[_in_arc]; kpeter@593: } else { kpeter@593: first = _target[_in_arc]; kpeter@593: second = _source[_in_arc]; kpeter@593: } kpeter@593: delta = _cap[_in_arc]; kpeter@593: int result = 0; kpeter@593: Capacity d; kpeter@593: int e; kpeter@593: kpeter@593: // Search the cycle along the path form the first node to the root kpeter@593: for (int u = first; u != join; u = _parent[u]) { kpeter@593: e = _pred[u]; kpeter@593: d = _forward[u] ? _flow[e] : _cap[e] - _flow[e]; kpeter@593: if (d < delta) { kpeter@593: delta = d; kpeter@593: u_out = u; kpeter@593: result = 1; kpeter@593: } kpeter@593: } kpeter@593: // Search the cycle along the path form the second node to the root kpeter@593: for (int u = second; u != join; u = _parent[u]) { kpeter@593: e = _pred[u]; kpeter@593: d = _forward[u] ? _cap[e] - _flow[e] : _flow[e]; kpeter@593: if (d <= delta) { kpeter@593: delta = d; kpeter@593: u_out = u; kpeter@593: result = 2; kpeter@593: } kpeter@593: } kpeter@593: kpeter@593: if (result == 1) { kpeter@593: u_in = first; kpeter@593: v_in = second; kpeter@593: } else { kpeter@593: u_in = second; kpeter@593: v_in = first; kpeter@593: } kpeter@593: return result != 0; kpeter@593: } kpeter@593: kpeter@593: // Change _flow and _state vectors kpeter@593: void changeFlow(bool change) { kpeter@593: // Augment along the cycle kpeter@593: if (delta > 0) { kpeter@593: Capacity val = _state[_in_arc] * delta; kpeter@593: _flow[_in_arc] += val; kpeter@593: for (int u = _source[_in_arc]; u != join; u = _parent[u]) { kpeter@593: _flow[_pred[u]] += _forward[u] ? -val : val; kpeter@593: } kpeter@593: for (int u = _target[_in_arc]; u != join; u = _parent[u]) { kpeter@593: _flow[_pred[u]] += _forward[u] ? val : -val; kpeter@593: } kpeter@593: } kpeter@593: // Update the state of the entering and leaving arcs kpeter@593: if (change) { kpeter@593: _state[_in_arc] = STATE_TREE; kpeter@593: _state[_pred[u_out]] = kpeter@593: (_flow[_pred[u_out]] == 0) ? STATE_LOWER : STATE_UPPER; kpeter@593: } else { kpeter@593: _state[_in_arc] = -_state[_in_arc]; kpeter@593: } kpeter@593: } kpeter@593: kpeter@593: // Update _thread and _parent vectors kpeter@593: void updateThreadParent() { kpeter@593: int u; kpeter@593: v_out = _parent[u_out]; kpeter@593: kpeter@593: // Handle the case when join and v_out coincide kpeter@593: bool par_first = false; kpeter@593: if (join == v_out) { kpeter@593: for (u = join; u != u_in && u != v_in; u = _thread[u]) ; kpeter@593: if (u == v_in) { kpeter@593: par_first = true; kpeter@593: while (_thread[u] != u_out) u = _thread[u]; kpeter@593: first = u; kpeter@593: } kpeter@593: } kpeter@593: kpeter@593: // Find the last successor of u_in (u) and the node after it (right) kpeter@593: // according to the thread index kpeter@593: for (u = u_in; _depth[_thread[u]] > _depth[u_in]; u = _thread[u]) ; kpeter@593: right = _thread[u]; kpeter@593: if (_thread[v_in] == u_out) { kpeter@593: for (last = u; _depth[last] > _depth[u_out]; last = _thread[last]) ; kpeter@593: if (last == u_out) last = _thread[last]; kpeter@593: } kpeter@593: else last = _thread[v_in]; kpeter@593: kpeter@593: // Update stem nodes kpeter@593: _thread[v_in] = stem = u_in; kpeter@593: par_stem = v_in; kpeter@593: while (stem != u_out) { kpeter@593: _thread[u] = new_stem = _parent[stem]; kpeter@593: kpeter@593: // Find the node just before the stem node (u) according to kpeter@593: // the original thread index kpeter@593: for (u = new_stem; _thread[u] != stem; u = _thread[u]) ; kpeter@593: _thread[u] = right; kpeter@593: kpeter@593: // Change the parent node of stem and shift stem and par_stem nodes kpeter@593: _parent[stem] = par_stem; kpeter@593: par_stem = stem; kpeter@593: stem = new_stem; kpeter@593: kpeter@593: // Find the last successor of stem (u) and the node after it (right) kpeter@593: // according to the thread index kpeter@593: for (u = stem; _depth[_thread[u]] > _depth[stem]; u = _thread[u]) ; kpeter@593: right = _thread[u]; kpeter@593: } kpeter@593: _parent[u_out] = par_stem; kpeter@593: _thread[u] = last; kpeter@593: kpeter@593: if (join == v_out && par_first) { kpeter@593: if (first != v_in) _thread[first] = right; kpeter@593: } else { kpeter@593: for (u = v_out; _thread[u] != u_out; u = _thread[u]) ; kpeter@593: _thread[u] = right; kpeter@593: } kpeter@593: } kpeter@593: kpeter@593: // Update _pred and _forward vectors kpeter@593: void updatePredArc() { kpeter@593: int u = u_out, v; kpeter@593: while (u != u_in) { kpeter@593: v = _parent[u]; kpeter@593: _pred[u] = _pred[v]; kpeter@593: _forward[u] = !_forward[v]; kpeter@593: u = v; kpeter@593: } kpeter@593: _pred[u_in] = _in_arc; kpeter@593: _forward[u_in] = (u_in == _source[_in_arc]); kpeter@593: } kpeter@593: kpeter@593: // Update _depth and _potential vectors kpeter@593: void updateDepthPotential() { kpeter@593: _depth[u_in] = _depth[v_in] + 1; kpeter@593: Cost sigma = _forward[u_in] ? kpeter@593: _pi[v_in] - _pi[u_in] - _cost[_pred[u_in]] : kpeter@593: _pi[v_in] - _pi[u_in] + _cost[_pred[u_in]]; kpeter@593: _pi[u_in] += sigma; kpeter@593: for(int u = _thread[u_in]; _parent[u] != -1; u = _thread[u]) { kpeter@593: _depth[u] = _depth[_parent[u]] + 1; kpeter@593: if (_depth[u] <= _depth[u_in]) break; kpeter@593: _pi[u] += sigma; kpeter@593: } kpeter@593: } kpeter@593: kpeter@593: // Execute the algorithm kpeter@593: bool start(PivotRuleEnum pivot_rule) { kpeter@593: // Select the pivot rule implementation kpeter@593: switch (pivot_rule) { kpeter@593: case FIRST_ELIGIBLE_PIVOT: kpeter@593: return start(); kpeter@593: case BEST_ELIGIBLE_PIVOT: kpeter@593: return start(); kpeter@593: case BLOCK_SEARCH_PIVOT: kpeter@593: return start(); kpeter@593: case CANDIDATE_LIST_PIVOT: kpeter@593: return start(); kpeter@593: case ALTERING_LIST_PIVOT: kpeter@593: return start(); kpeter@593: } kpeter@593: return false; kpeter@593: } kpeter@593: kpeter@593: template kpeter@593: bool start() { kpeter@593: PivotRuleImplementation pivot(*this); kpeter@593: kpeter@593: // Execute the network simplex algorithm kpeter@593: while (pivot.findEnteringArc()) { kpeter@593: findJoinNode(); kpeter@593: bool change = findLeavingArc(); kpeter@593: changeFlow(change); kpeter@593: if (change) { kpeter@593: updateThreadParent(); kpeter@593: updatePredArc(); kpeter@593: updateDepthPotential(); kpeter@593: } kpeter@593: } kpeter@593: kpeter@593: // Check if the flow amount equals zero on all the artificial arcs kpeter@593: for (int e = _arc_num; e != _arc_num + _node_num; ++e) { kpeter@593: if (_flow[e] > 0) return false; kpeter@593: } kpeter@593: kpeter@593: // Copy flow values to _flow_result kpeter@593: if (_orig_lower) { kpeter@593: for (int i = 0; i != _arc_num; ++i) { kpeter@593: Arc e = _arc[i]; kpeter@593: (*_flow_result)[e] = (*_orig_lower)[e] + _flow[i]; kpeter@593: } kpeter@593: } else { kpeter@593: for (int i = 0; i != _arc_num; ++i) { kpeter@593: (*_flow_result)[_arc[i]] = _flow[i]; kpeter@593: } kpeter@593: } kpeter@593: // Copy potential values to _potential_result kpeter@593: for (int i = 0; i != _node_num; ++i) { kpeter@593: (*_potential_result)[_node[i]] = _pi[i]; kpeter@593: } kpeter@593: kpeter@593: return true; kpeter@593: } kpeter@593: kpeter@593: }; //class NetworkSimplex kpeter@593: kpeter@593: ///@} kpeter@593: kpeter@593: } //namespace lemon kpeter@593: kpeter@593: #endif //LEMON_NETWORK_SIMPLEX_H