lemon/network_simplex.h
author Alpar Juttner <alpar@cs.elte.hu>
Fri, 12 Feb 2010 21:53:15 +0100
changeset 826 02109e17027f
parent 811 fe80a8145653
child 830 75c97c3786d6
child 839 f3bc4e9b5f3a
permissions -rw-r--r--
Merge
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/* -*- mode: C++; indent-tabs-mode: nil; -*-
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 *
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 * This file is a part of LEMON, a generic C++ optimization library.
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 *
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 * Copyright (C) 2003-2009
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 * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
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 * (Egervary Research Group on Combinatorial Optimization, EGRES).
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 *
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 * Permission to use, modify and distribute this software is granted
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 * provided that this copyright notice appears in all copies. For
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 * precise terms see the accompanying LICENSE file.
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 *
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 * This software is provided "AS IS" with no warranty of any kind,
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 * express or implied, and with no claim as to its suitability for any
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 * purpose.
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 *
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 */
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#ifndef LEMON_NETWORK_SIMPLEX_H
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#define LEMON_NETWORK_SIMPLEX_H
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/// \ingroup min_cost_flow_algs
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///
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/// \file
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/// \brief Network Simplex algorithm for finding a minimum cost flow.
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#include <vector>
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#include <limits>
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#include <algorithm>
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#include <lemon/core.h>
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#include <lemon/math.h>
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namespace lemon {
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  /// \addtogroup min_cost_flow_algs
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  /// @{
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  /// \brief Implementation of the primal Network Simplex algorithm
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  /// for finding a \ref min_cost_flow "minimum cost flow".
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  ///
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  /// \ref NetworkSimplex implements the primal Network Simplex algorithm
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  /// for finding a \ref min_cost_flow "minimum cost flow"
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  /// \ref amo93networkflows, \ref dantzig63linearprog,
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  /// \ref kellyoneill91netsimplex.
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  /// This algorithm is a highly efficient specialized version of the
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  /// linear programming simplex method directly for the minimum cost
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  /// flow problem.
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  ///
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  /// In general, %NetworkSimplex is the fastest implementation available
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  /// in LEMON for this problem.
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  /// Moreover, it supports both directions of the supply/demand inequality
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  /// constraints. For more information, see \ref SupplyType.
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  ///
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  /// Most of the parameters of the problem (except for the digraph)
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  /// can be given using separate functions, and the algorithm can be
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  /// executed using the \ref run() function. If some parameters are not
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  /// specified, then default values will be used.
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  ///
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  /// \tparam GR The digraph type the algorithm runs on.
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  /// \tparam V The number type used for flow amounts, capacity bounds
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  /// and supply values in the algorithm. By default, it is \c int.
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  /// \tparam C The number type used for costs and potentials in the
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  /// algorithm. By default, it is the same as \c V.
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  ///
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  /// \warning Both number types must be signed and all input data must
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  /// be integer.
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  ///
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  /// \note %NetworkSimplex provides five different pivot rule
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  /// implementations, from which the most efficient one is used
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  /// by default. For more information, see \ref PivotRule.
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  template <typename GR, typename V = int, typename C = V>
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  class NetworkSimplex
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  {
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  public:
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    /// The type of the flow amounts, capacity bounds and supply values
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    typedef V Value;
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    /// The type of the arc costs
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    typedef C Cost;
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  public:
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    /// \brief Problem type constants for the \c run() function.
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    ///
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    /// Enum type containing the problem type constants that can be
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    /// returned by the \ref run() function of the algorithm.
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    enum ProblemType {
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      /// The problem has no feasible solution (flow).
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      INFEASIBLE,
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      /// The problem has optimal solution (i.e. it is feasible and
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      /// bounded), and the algorithm has found optimal flow and node
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      /// potentials (primal and dual solutions).
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      OPTIMAL,
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      /// The objective function of the problem is unbounded, i.e.
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      /// there is a directed cycle having negative total cost and
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      /// infinite upper bound.
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      UNBOUNDED
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    };
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    /// \brief Constants for selecting the type of the supply constraints.
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    ///
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    /// Enum type containing constants for selecting the supply type,
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    /// i.e. the direction of the inequalities in the supply/demand
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    /// constraints of the \ref min_cost_flow "minimum cost flow problem".
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    ///
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    /// The default supply type is \c GEQ, the \c LEQ type can be
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    /// selected using \ref supplyType().
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    /// The equality form is a special case of both supply types.
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    enum SupplyType {
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      /// This option means that there are <em>"greater or equal"</em>
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      /// supply/demand constraints in the definition of the problem.
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      GEQ,
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      /// This option means that there are <em>"less or equal"</em>
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      /// supply/demand constraints in the definition of the problem.
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      LEQ
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    };
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    /// \brief Constants for selecting the pivot rule.
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    ///
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    /// Enum type containing constants for selecting the pivot rule for
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    /// the \ref run() function.
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    ///
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    /// \ref NetworkSimplex provides five different pivot rule
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    /// implementations that significantly affect the running time
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    /// of the algorithm.
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    /// By default, \ref BLOCK_SEARCH "Block Search" is used, which
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    /// proved to be the most efficient and the most robust on various
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    /// test inputs.
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    /// However, another pivot rule can be selected using the \ref run()
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    /// function with the proper parameter.
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    enum PivotRule {
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      /// The \e First \e Eligible pivot rule.
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      /// The next eligible arc is selected in a wraparound fashion
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      /// in every iteration.
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      FIRST_ELIGIBLE,
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      /// The \e Best \e Eligible pivot rule.
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      /// The best eligible arc is selected in every iteration.
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      BEST_ELIGIBLE,
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      /// The \e Block \e Search pivot rule.
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      /// A specified number of arcs are examined in every iteration
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      /// in a wraparound fashion and the best eligible arc is selected
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      /// from this block.
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      BLOCK_SEARCH,
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      /// The \e Candidate \e List pivot rule.
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      /// In a major iteration a candidate list is built from eligible arcs
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      /// in a wraparound fashion and in the following minor iterations
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      /// the best eligible arc is selected from this list.
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      CANDIDATE_LIST,
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      /// The \e Altering \e Candidate \e List pivot rule.
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      /// It is a modified version of the Candidate List method.
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      /// It keeps only the several best eligible arcs from the former
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      /// candidate list and extends this list in every iteration.
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      ALTERING_LIST
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    };
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  private:
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    TEMPLATE_DIGRAPH_TYPEDEFS(GR);
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    typedef std::vector<int> IntVector;
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    typedef std::vector<char> CharVector;
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    typedef std::vector<Value> ValueVector;
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    typedef std::vector<Cost> CostVector;
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    // State constants for arcs
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    enum ArcStateEnum {
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      STATE_UPPER = -1,
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      STATE_TREE  =  0,
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      STATE_LOWER =  1
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    };
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  private:
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    // Data related to the underlying digraph
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    const GR &_graph;
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    int _node_num;
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    int _arc_num;
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    int _all_arc_num;
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    int _search_arc_num;
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    // Parameters of the problem
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    bool _have_lower;
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    SupplyType _stype;
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    Value _sum_supply;
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    // Data structures for storing the digraph
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    IntNodeMap _node_id;
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    IntArcMap _arc_id;
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    IntVector _source;
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    IntVector _target;
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    // Node and arc data
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    ValueVector _lower;
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    ValueVector _upper;
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    ValueVector _cap;
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    CostVector _cost;
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    ValueVector _supply;
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    ValueVector _flow;
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    CostVector _pi;
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    // Data for storing the spanning tree structure
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    IntVector _parent;
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    IntVector _pred;
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    IntVector _thread;
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    IntVector _rev_thread;
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    IntVector _succ_num;
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    IntVector _last_succ;
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    IntVector _dirty_revs;
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    CharVector _forward;
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    CharVector _state;
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    int _root;
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    // Temporary data used in the current pivot iteration
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    int in_arc, join, u_in, v_in, u_out, v_out;
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    int first, second, right, last;
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    int stem, par_stem, new_stem;
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    Value delta;
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    const Value MAX;
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  public:
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    /// \brief Constant for infinite upper bounds (capacities).
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    ///
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    /// Constant for infinite upper bounds (capacities).
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    /// It is \c std::numeric_limits<Value>::infinity() if available,
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    /// \c std::numeric_limits<Value>::max() otherwise.
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    const Value INF;
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  private:
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    // Implementation of the First Eligible pivot rule
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    class FirstEligiblePivotRule
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    {
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    private:
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      // References to the NetworkSimplex class
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      const IntVector  &_source;
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      const IntVector  &_target;
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      const CostVector &_cost;
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      const CharVector &_state;
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      const CostVector &_pi;
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      int &_in_arc;
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      int _search_arc_num;
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      // Pivot rule data
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      int _next_arc;
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    public:
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      // Constructor
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      FirstEligiblePivotRule(NetworkSimplex &ns) :
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        _source(ns._source), _target(ns._target),
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        _cost(ns._cost), _state(ns._state), _pi(ns._pi),
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        _in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num),
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        _next_arc(0)
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      {}
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      // Find next entering arc
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      bool findEnteringArc() {
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        Cost c;
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        for (int e = _next_arc; e < _search_arc_num; ++e) {
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          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
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          if (c < 0) {
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            _in_arc = e;
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            _next_arc = e + 1;
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            return true;
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          }
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        }
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        for (int e = 0; e < _next_arc; ++e) {
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          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
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          if (c < 0) {
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            _in_arc = e;
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            _next_arc = e + 1;
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            return true;
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          }
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        }
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        return false;
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      }
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    }; //class FirstEligiblePivotRule
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    // Implementation of the Best Eligible pivot rule
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    class BestEligiblePivotRule
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    {
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    private:
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      // References to the NetworkSimplex class
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      const IntVector  &_source;
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      const IntVector  &_target;
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      const CostVector &_cost;
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      const CharVector &_state;
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      const CostVector &_pi;
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      int &_in_arc;
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      int _search_arc_num;
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    public:
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      // Constructor
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      BestEligiblePivotRule(NetworkSimplex &ns) :
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        _source(ns._source), _target(ns._target),
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        _cost(ns._cost), _state(ns._state), _pi(ns._pi),
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        _in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num)
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      {}
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      // Find next entering arc
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      bool findEnteringArc() {
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        Cost c, min = 0;
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        for (int e = 0; e < _search_arc_num; ++e) {
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          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
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          if (c < min) {
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            min = c;
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            _in_arc = e;
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          }
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        }
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        return min < 0;
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      }
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    }; //class BestEligiblePivotRule
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    // Implementation of the Block Search pivot rule
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    class BlockSearchPivotRule
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    {
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    private:
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      // References to the NetworkSimplex class
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      const IntVector  &_source;
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      const IntVector  &_target;
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      const CostVector &_cost;
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      const CharVector &_state;
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      const CostVector &_pi;
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      int &_in_arc;
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      int _search_arc_num;
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      // Pivot rule data
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      int _block_size;
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      int _next_arc;
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    public:
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      // Constructor
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      BlockSearchPivotRule(NetworkSimplex &ns) :
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        _source(ns._source), _target(ns._target),
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        _cost(ns._cost), _state(ns._state), _pi(ns._pi),
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        _in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num),
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        _next_arc(0)
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      {
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        // The main parameters of the pivot rule
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        const double BLOCK_SIZE_FACTOR = 0.5;
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        const int MIN_BLOCK_SIZE = 10;
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        _block_size = std::max( int(BLOCK_SIZE_FACTOR *
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                                    std::sqrt(double(_search_arc_num))),
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                                MIN_BLOCK_SIZE );
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      }
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      // Find next entering arc
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      bool findEnteringArc() {
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        Cost c, min = 0;
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        int cnt = _block_size;
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        int e;
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        for (e = _next_arc; e < _search_arc_num; ++e) {
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          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
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          if (c < min) {
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            min = c;
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            _in_arc = e;
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          }
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          if (--cnt == 0) {
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            if (min < 0) goto search_end;
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            cnt = _block_size;
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          }
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        }
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        for (e = 0; e < _next_arc; ++e) {
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          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
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          if (c < min) {
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            min = c;
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            _in_arc = e;
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          }
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          if (--cnt == 0) {
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            if (min < 0) goto search_end;
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            cnt = _block_size;
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          }
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        }
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        if (min >= 0) return false;
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      search_end:
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        _next_arc = e;
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        return true;
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      }
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    }; //class BlockSearchPivotRule
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    // Implementation of the Candidate List pivot rule
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    class CandidateListPivotRule
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    {
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    private:
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      // References to the NetworkSimplex class
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      const IntVector  &_source;
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      const IntVector  &_target;
kpeter@607
   410
      const CostVector &_cost;
kpeter@811
   411
      const CharVector &_state;
kpeter@607
   412
      const CostVector &_pi;
kpeter@601
   413
      int &_in_arc;
kpeter@663
   414
      int _search_arc_num;
kpeter@601
   415
kpeter@601
   416
      // Pivot rule data
kpeter@601
   417
      IntVector _candidates;
kpeter@601
   418
      int _list_length, _minor_limit;
kpeter@601
   419
      int _curr_length, _minor_count;
kpeter@601
   420
      int _next_arc;
kpeter@601
   421
kpeter@601
   422
    public:
kpeter@601
   423
kpeter@601
   424
      /// Constructor
kpeter@601
   425
      CandidateListPivotRule(NetworkSimplex &ns) :
kpeter@603
   426
        _source(ns._source), _target(ns._target),
kpeter@601
   427
        _cost(ns._cost), _state(ns._state), _pi(ns._pi),
kpeter@663
   428
        _in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num),
kpeter@663
   429
        _next_arc(0)
kpeter@601
   430
      {
kpeter@601
   431
        // The main parameters of the pivot rule
kpeter@727
   432
        const double LIST_LENGTH_FACTOR = 0.25;
kpeter@601
   433
        const int MIN_LIST_LENGTH = 10;
kpeter@601
   434
        const double MINOR_LIMIT_FACTOR = 0.1;
kpeter@601
   435
        const int MIN_MINOR_LIMIT = 3;
kpeter@601
   436
alpar@612
   437
        _list_length = std::max( int(LIST_LENGTH_FACTOR *
kpeter@663
   438
                                     std::sqrt(double(_search_arc_num))),
kpeter@601
   439
                                 MIN_LIST_LENGTH );
kpeter@601
   440
        _minor_limit = std::max( int(MINOR_LIMIT_FACTOR * _list_length),
kpeter@601
   441
                                 MIN_MINOR_LIMIT );
kpeter@601
   442
        _curr_length = _minor_count = 0;
kpeter@601
   443
        _candidates.resize(_list_length);
kpeter@601
   444
      }
kpeter@601
   445
kpeter@601
   446
      /// Find next entering arc
kpeter@601
   447
      bool findEnteringArc() {
kpeter@607
   448
        Cost min, c;
kpeter@727
   449
        int e;
kpeter@601
   450
        if (_curr_length > 0 && _minor_count < _minor_limit) {
kpeter@601
   451
          // Minor iteration: select the best eligible arc from the
kpeter@601
   452
          // current candidate list
kpeter@601
   453
          ++_minor_count;
kpeter@601
   454
          min = 0;
kpeter@601
   455
          for (int i = 0; i < _curr_length; ++i) {
kpeter@601
   456
            e = _candidates[i];
kpeter@601
   457
            c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
kpeter@601
   458
            if (c < min) {
kpeter@601
   459
              min = c;
kpeter@727
   460
              _in_arc = e;
kpeter@601
   461
            }
kpeter@727
   462
            else if (c >= 0) {
kpeter@601
   463
              _candidates[i--] = _candidates[--_curr_length];
kpeter@601
   464
            }
kpeter@601
   465
          }
kpeter@727
   466
          if (min < 0) return true;
kpeter@601
   467
        }
kpeter@601
   468
kpeter@601
   469
        // Major iteration: build a new candidate list
kpeter@601
   470
        min = 0;
kpeter@601
   471
        _curr_length = 0;
kpeter@663
   472
        for (e = _next_arc; e < _search_arc_num; ++e) {
kpeter@601
   473
          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
kpeter@601
   474
          if (c < 0) {
kpeter@601
   475
            _candidates[_curr_length++] = e;
kpeter@601
   476
            if (c < min) {
kpeter@601
   477
              min = c;
kpeter@727
   478
              _in_arc = e;
kpeter@601
   479
            }
kpeter@727
   480
            if (_curr_length == _list_length) goto search_end;
kpeter@601
   481
          }
kpeter@601
   482
        }
kpeter@727
   483
        for (e = 0; e < _next_arc; ++e) {
kpeter@727
   484
          c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
kpeter@727
   485
          if (c < 0) {
kpeter@727
   486
            _candidates[_curr_length++] = e;
kpeter@727
   487
            if (c < min) {
kpeter@727
   488
              min = c;
kpeter@727
   489
              _in_arc = e;
kpeter@601
   490
            }
kpeter@727
   491
            if (_curr_length == _list_length) goto search_end;
kpeter@601
   492
          }
kpeter@601
   493
        }
kpeter@601
   494
        if (_curr_length == 0) return false;
kpeter@727
   495
      
kpeter@727
   496
      search_end:        
kpeter@601
   497
        _minor_count = 1;
kpeter@601
   498
        _next_arc = e;
kpeter@601
   499
        return true;
kpeter@601
   500
      }
kpeter@601
   501
kpeter@601
   502
    }; //class CandidateListPivotRule
kpeter@601
   503
kpeter@601
   504
kpeter@605
   505
    // Implementation of the Altering Candidate List pivot rule
kpeter@601
   506
    class AlteringListPivotRule
kpeter@601
   507
    {
kpeter@601
   508
    private:
kpeter@601
   509
kpeter@601
   510
      // References to the NetworkSimplex class
kpeter@601
   511
      const IntVector  &_source;
kpeter@601
   512
      const IntVector  &_target;
kpeter@607
   513
      const CostVector &_cost;
kpeter@811
   514
      const CharVector &_state;
kpeter@607
   515
      const CostVector &_pi;
kpeter@601
   516
      int &_in_arc;
kpeter@663
   517
      int _search_arc_num;
kpeter@601
   518
kpeter@601
   519
      // Pivot rule data
kpeter@601
   520
      int _block_size, _head_length, _curr_length;
kpeter@601
   521
      int _next_arc;
kpeter@601
   522
      IntVector _candidates;
kpeter@607
   523
      CostVector _cand_cost;
kpeter@601
   524
kpeter@601
   525
      // Functor class to compare arcs during sort of the candidate list
kpeter@601
   526
      class SortFunc
kpeter@601
   527
      {
kpeter@601
   528
      private:
kpeter@607
   529
        const CostVector &_map;
kpeter@601
   530
      public:
kpeter@607
   531
        SortFunc(const CostVector &map) : _map(map) {}
kpeter@601
   532
        bool operator()(int left, int right) {
kpeter@601
   533
          return _map[left] > _map[right];
kpeter@601
   534
        }
kpeter@601
   535
      };
kpeter@601
   536
kpeter@601
   537
      SortFunc _sort_func;
kpeter@601
   538
kpeter@601
   539
    public:
kpeter@601
   540
kpeter@605
   541
      // Constructor
kpeter@601
   542
      AlteringListPivotRule(NetworkSimplex &ns) :
kpeter@603
   543
        _source(ns._source), _target(ns._target),
kpeter@601
   544
        _cost(ns._cost), _state(ns._state), _pi(ns._pi),
kpeter@663
   545
        _in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num),
kpeter@663
   546
        _next_arc(0), _cand_cost(ns._search_arc_num), _sort_func(_cand_cost)
kpeter@601
   547
      {
kpeter@601
   548
        // The main parameters of the pivot rule
kpeter@727
   549
        const double BLOCK_SIZE_FACTOR = 1.0;
kpeter@601
   550
        const int MIN_BLOCK_SIZE = 10;
kpeter@601
   551
        const double HEAD_LENGTH_FACTOR = 0.1;
kpeter@601
   552
        const int MIN_HEAD_LENGTH = 3;
kpeter@601
   553
alpar@612
   554
        _block_size = std::max( int(BLOCK_SIZE_FACTOR *
kpeter@663
   555
                                    std::sqrt(double(_search_arc_num))),
kpeter@601
   556
                                MIN_BLOCK_SIZE );
kpeter@601
   557
        _head_length = std::max( int(HEAD_LENGTH_FACTOR * _block_size),
kpeter@601
   558
                                 MIN_HEAD_LENGTH );
kpeter@601
   559
        _candidates.resize(_head_length + _block_size);
kpeter@601
   560
        _curr_length = 0;
kpeter@601
   561
      }
kpeter@601
   562
kpeter@605
   563
      // Find next entering arc
kpeter@601
   564
      bool findEnteringArc() {
kpeter@601
   565
        // Check the current candidate list
kpeter@601
   566
        int e;
kpeter@601
   567
        for (int i = 0; i < _curr_length; ++i) {
kpeter@601
   568
          e = _candidates[i];
kpeter@601
   569
          _cand_cost[e] = _state[e] *
kpeter@601
   570
            (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
kpeter@601
   571
          if (_cand_cost[e] >= 0) {
kpeter@601
   572
            _candidates[i--] = _candidates[--_curr_length];
kpeter@601
   573
          }
kpeter@601
   574
        }
kpeter@601
   575
kpeter@601
   576
        // Extend the list
kpeter@601
   577
        int cnt = _block_size;
kpeter@601
   578
        int limit = _head_length;
kpeter@601
   579
kpeter@727
   580
        for (e = _next_arc; e < _search_arc_num; ++e) {
kpeter@601
   581
          _cand_cost[e] = _state[e] *
kpeter@601
   582
            (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
kpeter@601
   583
          if (_cand_cost[e] < 0) {
kpeter@601
   584
            _candidates[_curr_length++] = e;
kpeter@601
   585
          }
kpeter@601
   586
          if (--cnt == 0) {
kpeter@727
   587
            if (_curr_length > limit) goto search_end;
kpeter@601
   588
            limit = 0;
kpeter@601
   589
            cnt = _block_size;
kpeter@601
   590
          }
kpeter@601
   591
        }
kpeter@727
   592
        for (e = 0; e < _next_arc; ++e) {
kpeter@727
   593
          _cand_cost[e] = _state[e] *
kpeter@727
   594
            (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
kpeter@727
   595
          if (_cand_cost[e] < 0) {
kpeter@727
   596
            _candidates[_curr_length++] = e;
kpeter@727
   597
          }
kpeter@727
   598
          if (--cnt == 0) {
kpeter@727
   599
            if (_curr_length > limit) goto search_end;
kpeter@727
   600
            limit = 0;
kpeter@727
   601
            cnt = _block_size;
kpeter@601
   602
          }
kpeter@601
   603
        }
kpeter@601
   604
        if (_curr_length == 0) return false;
kpeter@727
   605
        
kpeter@727
   606
      search_end:
kpeter@601
   607
kpeter@601
   608
        // Make heap of the candidate list (approximating a partial sort)
kpeter@601
   609
        make_heap( _candidates.begin(), _candidates.begin() + _curr_length,
kpeter@601
   610
                   _sort_func );
kpeter@601
   611
kpeter@601
   612
        // Pop the first element of the heap
kpeter@601
   613
        _in_arc = _candidates[0];
kpeter@727
   614
        _next_arc = e;
kpeter@601
   615
        pop_heap( _candidates.begin(), _candidates.begin() + _curr_length,
kpeter@601
   616
                  _sort_func );
kpeter@601
   617
        _curr_length = std::min(_head_length, _curr_length - 1);
kpeter@601
   618
        return true;
kpeter@601
   619
      }
kpeter@601
   620
kpeter@601
   621
    }; //class AlteringListPivotRule
kpeter@601
   622
kpeter@601
   623
  public:
kpeter@601
   624
kpeter@605
   625
    /// \brief Constructor.
kpeter@601
   626
    ///
kpeter@609
   627
    /// The constructor of the class.
kpeter@601
   628
    ///
kpeter@603
   629
    /// \param graph The digraph the algorithm runs on.
kpeter@728
   630
    /// \param arc_mixing Indicate if the arcs have to be stored in a
kpeter@728
   631
    /// mixed order in the internal data structure. 
kpeter@728
   632
    /// In special cases, it could lead to better overall performance,
kpeter@728
   633
    /// but it is usually slower. Therefore it is disabled by default.
kpeter@728
   634
    NetworkSimplex(const GR& graph, bool arc_mixing = false) :
kpeter@642
   635
      _graph(graph), _node_id(graph), _arc_id(graph),
kpeter@811
   636
      MAX(std::numeric_limits<Value>::max()),
kpeter@641
   637
      INF(std::numeric_limits<Value>::has_infinity ?
kpeter@811
   638
          std::numeric_limits<Value>::infinity() : MAX)
kpeter@605
   639
    {
kpeter@812
   640
      // Check the number types
kpeter@641
   641
      LEMON_ASSERT(std::numeric_limits<Value>::is_signed,
kpeter@640
   642
        "The flow type of NetworkSimplex must be signed");
kpeter@640
   643
      LEMON_ASSERT(std::numeric_limits<Cost>::is_signed,
kpeter@640
   644
        "The cost type of NetworkSimplex must be signed");
kpeter@642
   645
        
kpeter@642
   646
      // Resize vectors
kpeter@642
   647
      _node_num = countNodes(_graph);
kpeter@642
   648
      _arc_num = countArcs(_graph);
kpeter@642
   649
      int all_node_num = _node_num + 1;
kpeter@663
   650
      int max_arc_num = _arc_num + 2 * _node_num;
kpeter@601
   651
kpeter@663
   652
      _source.resize(max_arc_num);
kpeter@663
   653
      _target.resize(max_arc_num);
kpeter@642
   654
kpeter@663
   655
      _lower.resize(_arc_num);
kpeter@663
   656
      _upper.resize(_arc_num);
kpeter@663
   657
      _cap.resize(max_arc_num);
kpeter@663
   658
      _cost.resize(max_arc_num);
kpeter@642
   659
      _supply.resize(all_node_num);
kpeter@663
   660
      _flow.resize(max_arc_num);
kpeter@642
   661
      _pi.resize(all_node_num);
kpeter@642
   662
kpeter@642
   663
      _parent.resize(all_node_num);
kpeter@642
   664
      _pred.resize(all_node_num);
kpeter@642
   665
      _forward.resize(all_node_num);
kpeter@642
   666
      _thread.resize(all_node_num);
kpeter@642
   667
      _rev_thread.resize(all_node_num);
kpeter@642
   668
      _succ_num.resize(all_node_num);
kpeter@642
   669
      _last_succ.resize(all_node_num);
kpeter@663
   670
      _state.resize(max_arc_num);
kpeter@642
   671
kpeter@728
   672
      // Copy the graph
kpeter@642
   673
      int i = 0;
kpeter@642
   674
      for (NodeIt n(_graph); n != INVALID; ++n, ++i) {
kpeter@642
   675
        _node_id[n] = i;
kpeter@642
   676
      }
kpeter@728
   677
      if (arc_mixing) {
kpeter@728
   678
        // Store the arcs in a mixed order
kpeter@728
   679
        int k = std::max(int(std::sqrt(double(_arc_num))), 10);
kpeter@728
   680
        int i = 0, j = 0;
kpeter@728
   681
        for (ArcIt a(_graph); a != INVALID; ++a) {
kpeter@728
   682
          _arc_id[a] = i;
kpeter@728
   683
          _source[i] = _node_id[_graph.source(a)];
kpeter@728
   684
          _target[i] = _node_id[_graph.target(a)];
kpeter@728
   685
          if ((i += k) >= _arc_num) i = ++j;
kpeter@728
   686
        }
kpeter@728
   687
      } else {
kpeter@728
   688
        // Store the arcs in the original order
kpeter@728
   689
        int i = 0;
kpeter@728
   690
        for (ArcIt a(_graph); a != INVALID; ++a, ++i) {
kpeter@728
   691
          _arc_id[a] = i;
kpeter@728
   692
          _source[i] = _node_id[_graph.source(a)];
kpeter@728
   693
          _target[i] = _node_id[_graph.target(a)];
kpeter@728
   694
        }
kpeter@642
   695
      }
kpeter@642
   696
      
kpeter@729
   697
      // Reset parameters
kpeter@729
   698
      reset();
kpeter@601
   699
    }
kpeter@601
   700
kpeter@609
   701
    /// \name Parameters
kpeter@609
   702
    /// The parameters of the algorithm can be specified using these
kpeter@609
   703
    /// functions.
kpeter@609
   704
kpeter@609
   705
    /// @{
kpeter@609
   706
kpeter@605
   707
    /// \brief Set the lower bounds on the arcs.
kpeter@605
   708
    ///
kpeter@605
   709
    /// This function sets the lower bounds on the arcs.
kpeter@640
   710
    /// If it is not used before calling \ref run(), the lower bounds
kpeter@640
   711
    /// will be set to zero on all arcs.
kpeter@605
   712
    ///
kpeter@605
   713
    /// \param map An arc map storing the lower bounds.
kpeter@641
   714
    /// Its \c Value type must be convertible to the \c Value type
kpeter@605
   715
    /// of the algorithm.
kpeter@605
   716
    ///
kpeter@605
   717
    /// \return <tt>(*this)</tt>
kpeter@640
   718
    template <typename LowerMap>
kpeter@640
   719
    NetworkSimplex& lowerMap(const LowerMap& map) {
kpeter@642
   720
      _have_lower = true;
kpeter@605
   721
      for (ArcIt a(_graph); a != INVALID; ++a) {
kpeter@642
   722
        _lower[_arc_id[a]] = map[a];
kpeter@605
   723
      }
kpeter@605
   724
      return *this;
kpeter@605
   725
    }
kpeter@605
   726
kpeter@605
   727
    /// \brief Set the upper bounds (capacities) on the arcs.
kpeter@605
   728
    ///
kpeter@605
   729
    /// This function sets the upper bounds (capacities) on the arcs.
kpeter@640
   730
    /// If it is not used before calling \ref run(), the upper bounds
kpeter@640
   731
    /// will be set to \ref INF on all arcs (i.e. the flow value will be
kpeter@812
   732
    /// unbounded from above).
kpeter@605
   733
    ///
kpeter@605
   734
    /// \param map An arc map storing the upper bounds.
kpeter@641
   735
    /// Its \c Value type must be convertible to the \c Value type
kpeter@605
   736
    /// of the algorithm.
kpeter@605
   737
    ///
kpeter@605
   738
    /// \return <tt>(*this)</tt>
kpeter@640
   739
    template<typename UpperMap>
kpeter@640
   740
    NetworkSimplex& upperMap(const UpperMap& map) {
kpeter@605
   741
      for (ArcIt a(_graph); a != INVALID; ++a) {
kpeter@642
   742
        _upper[_arc_id[a]] = map[a];
kpeter@605
   743
      }
kpeter@605
   744
      return *this;
kpeter@605
   745
    }
kpeter@605
   746
kpeter@605
   747
    /// \brief Set the costs of the arcs.
kpeter@605
   748
    ///
kpeter@605
   749
    /// This function sets the costs of the arcs.
kpeter@605
   750
    /// If it is not used before calling \ref run(), the costs
kpeter@605
   751
    /// will be set to \c 1 on all arcs.
kpeter@605
   752
    ///
kpeter@605
   753
    /// \param map An arc map storing the costs.
kpeter@607
   754
    /// Its \c Value type must be convertible to the \c Cost type
kpeter@605
   755
    /// of the algorithm.
kpeter@605
   756
    ///
kpeter@605
   757
    /// \return <tt>(*this)</tt>
kpeter@640
   758
    template<typename CostMap>
kpeter@640
   759
    NetworkSimplex& costMap(const CostMap& map) {
kpeter@605
   760
      for (ArcIt a(_graph); a != INVALID; ++a) {
kpeter@642
   761
        _cost[_arc_id[a]] = map[a];
kpeter@605
   762
      }
kpeter@605
   763
      return *this;
kpeter@605
   764
    }
kpeter@605
   765
kpeter@605
   766
    /// \brief Set the supply values of the nodes.
kpeter@605
   767
    ///
kpeter@605
   768
    /// This function sets the supply values of the nodes.
kpeter@605
   769
    /// If neither this function nor \ref stSupply() is used before
kpeter@605
   770
    /// calling \ref run(), the supply of each node will be set to zero.
kpeter@605
   771
    ///
kpeter@605
   772
    /// \param map A node map storing the supply values.
kpeter@641
   773
    /// Its \c Value type must be convertible to the \c Value type
kpeter@605
   774
    /// of the algorithm.
kpeter@605
   775
    ///
kpeter@605
   776
    /// \return <tt>(*this)</tt>
kpeter@640
   777
    template<typename SupplyMap>
kpeter@640
   778
    NetworkSimplex& supplyMap(const SupplyMap& map) {
kpeter@605
   779
      for (NodeIt n(_graph); n != INVALID; ++n) {
kpeter@642
   780
        _supply[_node_id[n]] = map[n];
kpeter@605
   781
      }
kpeter@605
   782
      return *this;
kpeter@605
   783
    }
kpeter@605
   784
kpeter@605
   785
    /// \brief Set single source and target nodes and a supply value.
kpeter@605
   786
    ///
kpeter@605
   787
    /// This function sets a single source node and a single target node
kpeter@605
   788
    /// and the required flow value.
kpeter@605
   789
    /// If neither this function nor \ref supplyMap() is used before
kpeter@605
   790
    /// calling \ref run(), the supply of each node will be set to zero.
kpeter@605
   791
    ///
kpeter@640
   792
    /// Using this function has the same effect as using \ref supplyMap()
kpeter@640
   793
    /// with such a map in which \c k is assigned to \c s, \c -k is
kpeter@640
   794
    /// assigned to \c t and all other nodes have zero supply value.
kpeter@640
   795
    ///
kpeter@605
   796
    /// \param s The source node.
kpeter@605
   797
    /// \param t The target node.
kpeter@605
   798
    /// \param k The required amount of flow from node \c s to node \c t
kpeter@605
   799
    /// (i.e. the supply of \c s and the demand of \c t).
kpeter@605
   800
    ///
kpeter@605
   801
    /// \return <tt>(*this)</tt>
kpeter@641
   802
    NetworkSimplex& stSupply(const Node& s, const Node& t, Value k) {
kpeter@642
   803
      for (int i = 0; i != _node_num; ++i) {
kpeter@642
   804
        _supply[i] = 0;
kpeter@642
   805
      }
kpeter@642
   806
      _supply[_node_id[s]] =  k;
kpeter@642
   807
      _supply[_node_id[t]] = -k;
kpeter@605
   808
      return *this;
kpeter@605
   809
    }
kpeter@609
   810
    
kpeter@640
   811
    /// \brief Set the type of the supply constraints.
kpeter@609
   812
    ///
kpeter@640
   813
    /// This function sets the type of the supply/demand constraints.
kpeter@640
   814
    /// If it is not used before calling \ref run(), the \ref GEQ supply
kpeter@609
   815
    /// type will be used.
kpeter@609
   816
    ///
kpeter@786
   817
    /// For more information, see \ref SupplyType.
kpeter@609
   818
    ///
kpeter@609
   819
    /// \return <tt>(*this)</tt>
kpeter@640
   820
    NetworkSimplex& supplyType(SupplyType supply_type) {
kpeter@640
   821
      _stype = supply_type;
kpeter@609
   822
      return *this;
kpeter@609
   823
    }
kpeter@605
   824
kpeter@609
   825
    /// @}
kpeter@601
   826
kpeter@605
   827
    /// \name Execution Control
kpeter@605
   828
    /// The algorithm can be executed using \ref run().
kpeter@605
   829
kpeter@601
   830
    /// @{
kpeter@601
   831
kpeter@601
   832
    /// \brief Run the algorithm.
kpeter@601
   833
    ///
kpeter@601
   834
    /// This function runs the algorithm.
kpeter@609
   835
    /// The paramters can be specified using functions \ref lowerMap(),
kpeter@640
   836
    /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(), 
kpeter@642
   837
    /// \ref supplyType().
kpeter@609
   838
    /// For example,
kpeter@605
   839
    /// \code
kpeter@605
   840
    ///   NetworkSimplex<ListDigraph> ns(graph);
kpeter@640
   841
    ///   ns.lowerMap(lower).upperMap(upper).costMap(cost)
kpeter@605
   842
    ///     .supplyMap(sup).run();
kpeter@605
   843
    /// \endcode
kpeter@601
   844
    ///
kpeter@606
   845
    /// This function can be called more than once. All the parameters
kpeter@606
   846
    /// that have been given are kept for the next call, unless
kpeter@606
   847
    /// \ref reset() is called, thus only the modified parameters
kpeter@606
   848
    /// have to be set again. See \ref reset() for examples.
kpeter@786
   849
    /// However, the underlying digraph must not be modified after this
kpeter@642
   850
    /// class have been constructed, since it copies and extends the graph.
kpeter@606
   851
    ///
kpeter@605
   852
    /// \param pivot_rule The pivot rule that will be used during the
kpeter@786
   853
    /// algorithm. For more information, see \ref PivotRule.
kpeter@601
   854
    ///
kpeter@640
   855
    /// \return \c INFEASIBLE if no feasible flow exists,
kpeter@640
   856
    /// \n \c OPTIMAL if the problem has optimal solution
kpeter@640
   857
    /// (i.e. it is feasible and bounded), and the algorithm has found
kpeter@640
   858
    /// optimal flow and node potentials (primal and dual solutions),
kpeter@640
   859
    /// \n \c UNBOUNDED if the objective function of the problem is
kpeter@640
   860
    /// unbounded, i.e. there is a directed cycle having negative total
kpeter@640
   861
    /// cost and infinite upper bound.
kpeter@640
   862
    ///
kpeter@640
   863
    /// \see ProblemType, PivotRule
kpeter@640
   864
    ProblemType run(PivotRule pivot_rule = BLOCK_SEARCH) {
kpeter@640
   865
      if (!init()) return INFEASIBLE;
kpeter@640
   866
      return start(pivot_rule);
kpeter@601
   867
    }
kpeter@601
   868
kpeter@606
   869
    /// \brief Reset all the parameters that have been given before.
kpeter@606
   870
    ///
kpeter@606
   871
    /// This function resets all the paramaters that have been given
kpeter@609
   872
    /// before using functions \ref lowerMap(), \ref upperMap(),
kpeter@642
   873
    /// \ref costMap(), \ref supplyMap(), \ref stSupply(), \ref supplyType().
kpeter@606
   874
    ///
kpeter@606
   875
    /// It is useful for multiple run() calls. If this function is not
kpeter@606
   876
    /// used, all the parameters given before are kept for the next
kpeter@606
   877
    /// \ref run() call.
kpeter@786
   878
    /// However, the underlying digraph must not be modified after this
kpeter@642
   879
    /// class have been constructed, since it copies and extends the graph.
kpeter@606
   880
    ///
kpeter@606
   881
    /// For example,
kpeter@606
   882
    /// \code
kpeter@606
   883
    ///   NetworkSimplex<ListDigraph> ns(graph);
kpeter@606
   884
    ///
kpeter@606
   885
    ///   // First run
kpeter@640
   886
    ///   ns.lowerMap(lower).upperMap(upper).costMap(cost)
kpeter@606
   887
    ///     .supplyMap(sup).run();
kpeter@606
   888
    ///
kpeter@606
   889
    ///   // Run again with modified cost map (reset() is not called,
kpeter@606
   890
    ///   // so only the cost map have to be set again)
kpeter@606
   891
    ///   cost[e] += 100;
kpeter@606
   892
    ///   ns.costMap(cost).run();
kpeter@606
   893
    ///
kpeter@606
   894
    ///   // Run again from scratch using reset()
kpeter@606
   895
    ///   // (the lower bounds will be set to zero on all arcs)
kpeter@606
   896
    ///   ns.reset();
kpeter@640
   897
    ///   ns.upperMap(capacity).costMap(cost)
kpeter@606
   898
    ///     .supplyMap(sup).run();
kpeter@606
   899
    /// \endcode
kpeter@606
   900
    ///
kpeter@606
   901
    /// \return <tt>(*this)</tt>
kpeter@606
   902
    NetworkSimplex& reset() {
kpeter@642
   903
      for (int i = 0; i != _node_num; ++i) {
kpeter@642
   904
        _supply[i] = 0;
kpeter@642
   905
      }
kpeter@642
   906
      for (int i = 0; i != _arc_num; ++i) {
kpeter@642
   907
        _lower[i] = 0;
kpeter@642
   908
        _upper[i] = INF;
kpeter@642
   909
        _cost[i] = 1;
kpeter@642
   910
      }
kpeter@642
   911
      _have_lower = false;
kpeter@640
   912
      _stype = GEQ;
kpeter@606
   913
      return *this;
kpeter@606
   914
    }
kpeter@606
   915
kpeter@601
   916
    /// @}
kpeter@601
   917
kpeter@601
   918
    /// \name Query Functions
kpeter@601
   919
    /// The results of the algorithm can be obtained using these
kpeter@601
   920
    /// functions.\n
kpeter@605
   921
    /// The \ref run() function must be called before using them.
kpeter@605
   922
kpeter@601
   923
    /// @{
kpeter@601
   924
kpeter@605
   925
    /// \brief Return the total cost of the found flow.
kpeter@605
   926
    ///
kpeter@605
   927
    /// This function returns the total cost of the found flow.
kpeter@640
   928
    /// Its complexity is O(e).
kpeter@605
   929
    ///
kpeter@605
   930
    /// \note The return type of the function can be specified as a
kpeter@605
   931
    /// template parameter. For example,
kpeter@605
   932
    /// \code
kpeter@605
   933
    ///   ns.totalCost<double>();
kpeter@605
   934
    /// \endcode
kpeter@607
   935
    /// It is useful if the total cost cannot be stored in the \c Cost
kpeter@605
   936
    /// type of the algorithm, which is the default return type of the
kpeter@605
   937
    /// function.
kpeter@605
   938
    ///
kpeter@605
   939
    /// \pre \ref run() must be called before using this function.
kpeter@642
   940
    template <typename Number>
kpeter@642
   941
    Number totalCost() const {
kpeter@642
   942
      Number c = 0;
kpeter@642
   943
      for (ArcIt a(_graph); a != INVALID; ++a) {
kpeter@642
   944
        int i = _arc_id[a];
kpeter@642
   945
        c += Number(_flow[i]) * Number(_cost[i]);
kpeter@605
   946
      }
kpeter@605
   947
      return c;
kpeter@605
   948
    }
kpeter@605
   949
kpeter@605
   950
#ifndef DOXYGEN
kpeter@607
   951
    Cost totalCost() const {
kpeter@607
   952
      return totalCost<Cost>();
kpeter@605
   953
    }
kpeter@605
   954
#endif
kpeter@605
   955
kpeter@605
   956
    /// \brief Return the flow on the given arc.
kpeter@605
   957
    ///
kpeter@605
   958
    /// This function returns the flow on the given arc.
kpeter@605
   959
    ///
kpeter@605
   960
    /// \pre \ref run() must be called before using this function.
kpeter@641
   961
    Value flow(const Arc& a) const {
kpeter@642
   962
      return _flow[_arc_id[a]];
kpeter@605
   963
    }
kpeter@605
   964
kpeter@642
   965
    /// \brief Return the flow map (the primal solution).
kpeter@601
   966
    ///
kpeter@642
   967
    /// This function copies the flow value on each arc into the given
kpeter@642
   968
    /// map. The \c Value type of the algorithm must be convertible to
kpeter@642
   969
    /// the \c Value type of the map.
kpeter@601
   970
    ///
kpeter@601
   971
    /// \pre \ref run() must be called before using this function.
kpeter@642
   972
    template <typename FlowMap>
kpeter@642
   973
    void flowMap(FlowMap &map) const {
kpeter@642
   974
      for (ArcIt a(_graph); a != INVALID; ++a) {
kpeter@642
   975
        map.set(a, _flow[_arc_id[a]]);
kpeter@642
   976
      }
kpeter@601
   977
    }
kpeter@601
   978
kpeter@605
   979
    /// \brief Return the potential (dual value) of the given node.
kpeter@605
   980
    ///
kpeter@605
   981
    /// This function returns the potential (dual value) of the
kpeter@605
   982
    /// given node.
kpeter@605
   983
    ///
kpeter@605
   984
    /// \pre \ref run() must be called before using this function.
kpeter@607
   985
    Cost potential(const Node& n) const {
kpeter@642
   986
      return _pi[_node_id[n]];
kpeter@605
   987
    }
kpeter@605
   988
kpeter@642
   989
    /// \brief Return the potential map (the dual solution).
kpeter@601
   990
    ///
kpeter@642
   991
    /// This function copies the potential (dual value) of each node
kpeter@642
   992
    /// into the given map.
kpeter@642
   993
    /// The \c Cost type of the algorithm must be convertible to the
kpeter@642
   994
    /// \c Value type of the map.
kpeter@601
   995
    ///
kpeter@601
   996
    /// \pre \ref run() must be called before using this function.
kpeter@642
   997
    template <typename PotentialMap>
kpeter@642
   998
    void potentialMap(PotentialMap &map) const {
kpeter@642
   999
      for (NodeIt n(_graph); n != INVALID; ++n) {
kpeter@642
  1000
        map.set(n, _pi[_node_id[n]]);
kpeter@642
  1001
      }
kpeter@601
  1002
    }
kpeter@601
  1003
kpeter@601
  1004
    /// @}
kpeter@601
  1005
kpeter@601
  1006
  private:
kpeter@601
  1007
kpeter@601
  1008
    // Initialize internal data structures
kpeter@601
  1009
    bool init() {
kpeter@605
  1010
      if (_node_num == 0) return false;
kpeter@601
  1011
kpeter@642
  1012
      // Check the sum of supply values
kpeter@642
  1013
      _sum_supply = 0;
kpeter@642
  1014
      for (int i = 0; i != _node_num; ++i) {
kpeter@642
  1015
        _sum_supply += _supply[i];
kpeter@642
  1016
      }
alpar@643
  1017
      if ( !((_stype == GEQ && _sum_supply <= 0) ||
alpar@643
  1018
             (_stype == LEQ && _sum_supply >= 0)) ) return false;
kpeter@601
  1019
kpeter@642
  1020
      // Remove non-zero lower bounds
kpeter@642
  1021
      if (_have_lower) {
kpeter@642
  1022
        for (int i = 0; i != _arc_num; ++i) {
kpeter@642
  1023
          Value c = _lower[i];
kpeter@642
  1024
          if (c >= 0) {
kpeter@811
  1025
            _cap[i] = _upper[i] < MAX ? _upper[i] - c : INF;
kpeter@642
  1026
          } else {
kpeter@811
  1027
            _cap[i] = _upper[i] < MAX + c ? _upper[i] - c : INF;
kpeter@642
  1028
          }
kpeter@642
  1029
          _supply[_source[i]] -= c;
kpeter@642
  1030
          _supply[_target[i]] += c;
kpeter@642
  1031
        }
kpeter@642
  1032
      } else {
kpeter@642
  1033
        for (int i = 0; i != _arc_num; ++i) {
kpeter@642
  1034
          _cap[i] = _upper[i];
kpeter@642
  1035
        }
kpeter@605
  1036
      }
kpeter@601
  1037
kpeter@609
  1038
      // Initialize artifical cost
kpeter@640
  1039
      Cost ART_COST;
kpeter@609
  1040
      if (std::numeric_limits<Cost>::is_exact) {
kpeter@663
  1041
        ART_COST = std::numeric_limits<Cost>::max() / 2 + 1;
kpeter@609
  1042
      } else {
kpeter@640
  1043
        ART_COST = std::numeric_limits<Cost>::min();
kpeter@609
  1044
        for (int i = 0; i != _arc_num; ++i) {
kpeter@640
  1045
          if (_cost[i] > ART_COST) ART_COST = _cost[i];
kpeter@609
  1046
        }
kpeter@640
  1047
        ART_COST = (ART_COST + 1) * _node_num;
kpeter@609
  1048
      }
kpeter@609
  1049
kpeter@642
  1050
      // Initialize arc maps
kpeter@642
  1051
      for (int i = 0; i != _arc_num; ++i) {
kpeter@642
  1052
        _flow[i] = 0;
kpeter@642
  1053
        _state[i] = STATE_LOWER;
kpeter@642
  1054
      }
kpeter@642
  1055
      
kpeter@601
  1056
      // Set data for the artificial root node
kpeter@601
  1057
      _root = _node_num;
kpeter@601
  1058
      _parent[_root] = -1;
kpeter@601
  1059
      _pred[_root] = -1;
kpeter@601
  1060
      _thread[_root] = 0;
kpeter@604
  1061
      _rev_thread[0] = _root;
kpeter@642
  1062
      _succ_num[_root] = _node_num + 1;
kpeter@604
  1063
      _last_succ[_root] = _root - 1;
kpeter@640
  1064
      _supply[_root] = -_sum_supply;
kpeter@663
  1065
      _pi[_root] = 0;
kpeter@601
  1066
kpeter@601
  1067
      // Add artificial arcs and initialize the spanning tree data structure
kpeter@663
  1068
      if (_sum_supply == 0) {
kpeter@663
  1069
        // EQ supply constraints
kpeter@663
  1070
        _search_arc_num = _arc_num;
kpeter@663
  1071
        _all_arc_num = _arc_num + _node_num;
kpeter@663
  1072
        for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) {
kpeter@663
  1073
          _parent[u] = _root;
kpeter@663
  1074
          _pred[u] = e;
kpeter@663
  1075
          _thread[u] = u + 1;
kpeter@663
  1076
          _rev_thread[u + 1] = u;
kpeter@663
  1077
          _succ_num[u] = 1;
kpeter@663
  1078
          _last_succ[u] = u;
kpeter@663
  1079
          _cap[e] = INF;
kpeter@663
  1080
          _state[e] = STATE_TREE;
kpeter@663
  1081
          if (_supply[u] >= 0) {
kpeter@663
  1082
            _forward[u] = true;
kpeter@663
  1083
            _pi[u] = 0;
kpeter@663
  1084
            _source[e] = u;
kpeter@663
  1085
            _target[e] = _root;
kpeter@663
  1086
            _flow[e] = _supply[u];
kpeter@663
  1087
            _cost[e] = 0;
kpeter@663
  1088
          } else {
kpeter@663
  1089
            _forward[u] = false;
kpeter@663
  1090
            _pi[u] = ART_COST;
kpeter@663
  1091
            _source[e] = _root;
kpeter@663
  1092
            _target[e] = u;
kpeter@663
  1093
            _flow[e] = -_supply[u];
kpeter@663
  1094
            _cost[e] = ART_COST;
kpeter@663
  1095
          }
kpeter@601
  1096
        }
kpeter@601
  1097
      }
kpeter@663
  1098
      else if (_sum_supply > 0) {
kpeter@663
  1099
        // LEQ supply constraints
kpeter@663
  1100
        _search_arc_num = _arc_num + _node_num;
kpeter@663
  1101
        int f = _arc_num + _node_num;
kpeter@663
  1102
        for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) {
kpeter@663
  1103
          _parent[u] = _root;
kpeter@663
  1104
          _thread[u] = u + 1;
kpeter@663
  1105
          _rev_thread[u + 1] = u;
kpeter@663
  1106
          _succ_num[u] = 1;
kpeter@663
  1107
          _last_succ[u] = u;
kpeter@663
  1108
          if (_supply[u] >= 0) {
kpeter@663
  1109
            _forward[u] = true;
kpeter@663
  1110
            _pi[u] = 0;
kpeter@663
  1111
            _pred[u] = e;
kpeter@663
  1112
            _source[e] = u;
kpeter@663
  1113
            _target[e] = _root;
kpeter@663
  1114
            _cap[e] = INF;
kpeter@663
  1115
            _flow[e] = _supply[u];
kpeter@663
  1116
            _cost[e] = 0;
kpeter@663
  1117
            _state[e] = STATE_TREE;
kpeter@663
  1118
          } else {
kpeter@663
  1119
            _forward[u] = false;
kpeter@663
  1120
            _pi[u] = ART_COST;
kpeter@663
  1121
            _pred[u] = f;
kpeter@663
  1122
            _source[f] = _root;
kpeter@663
  1123
            _target[f] = u;
kpeter@663
  1124
            _cap[f] = INF;
kpeter@663
  1125
            _flow[f] = -_supply[u];
kpeter@663
  1126
            _cost[f] = ART_COST;
kpeter@663
  1127
            _state[f] = STATE_TREE;
kpeter@663
  1128
            _source[e] = u;
kpeter@663
  1129
            _target[e] = _root;
kpeter@663
  1130
            _cap[e] = INF;
kpeter@663
  1131
            _flow[e] = 0;
kpeter@663
  1132
            _cost[e] = 0;
kpeter@663
  1133
            _state[e] = STATE_LOWER;
kpeter@663
  1134
            ++f;
kpeter@663
  1135
          }
kpeter@663
  1136
        }
kpeter@663
  1137
        _all_arc_num = f;
kpeter@663
  1138
      }
kpeter@663
  1139
      else {
kpeter@663
  1140
        // GEQ supply constraints
kpeter@663
  1141
        _search_arc_num = _arc_num + _node_num;
kpeter@663
  1142
        int f = _arc_num + _node_num;
kpeter@663
  1143
        for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) {
kpeter@663
  1144
          _parent[u] = _root;
kpeter@663
  1145
          _thread[u] = u + 1;
kpeter@663
  1146
          _rev_thread[u + 1] = u;
kpeter@663
  1147
          _succ_num[u] = 1;
kpeter@663
  1148
          _last_succ[u] = u;
kpeter@663
  1149
          if (_supply[u] <= 0) {
kpeter@663
  1150
            _forward[u] = false;
kpeter@663
  1151
            _pi[u] = 0;
kpeter@663
  1152
            _pred[u] = e;
kpeter@663
  1153
            _source[e] = _root;
kpeter@663
  1154
            _target[e] = u;
kpeter@663
  1155
            _cap[e] = INF;
kpeter@663
  1156
            _flow[e] = -_supply[u];
kpeter@663
  1157
            _cost[e] = 0;
kpeter@663
  1158
            _state[e] = STATE_TREE;
kpeter@663
  1159
          } else {
kpeter@663
  1160
            _forward[u] = true;
kpeter@663
  1161
            _pi[u] = -ART_COST;
kpeter@663
  1162
            _pred[u] = f;
kpeter@663
  1163
            _source[f] = u;
kpeter@663
  1164
            _target[f] = _root;
kpeter@663
  1165
            _cap[f] = INF;
kpeter@663
  1166
            _flow[f] = _supply[u];
kpeter@663
  1167
            _state[f] = STATE_TREE;
kpeter@663
  1168
            _cost[f] = ART_COST;
kpeter@663
  1169
            _source[e] = _root;
kpeter@663
  1170
            _target[e] = u;
kpeter@663
  1171
            _cap[e] = INF;
kpeter@663
  1172
            _flow[e] = 0;
kpeter@663
  1173
            _cost[e] = 0;
kpeter@663
  1174
            _state[e] = STATE_LOWER;
kpeter@663
  1175
            ++f;
kpeter@663
  1176
          }
kpeter@663
  1177
        }
kpeter@663
  1178
        _all_arc_num = f;
kpeter@663
  1179
      }
kpeter@601
  1180
kpeter@601
  1181
      return true;
kpeter@601
  1182
    }
kpeter@601
  1183
kpeter@601
  1184
    // Find the join node
kpeter@601
  1185
    void findJoinNode() {
kpeter@603
  1186
      int u = _source[in_arc];
kpeter@603
  1187
      int v = _target[in_arc];
kpeter@601
  1188
      while (u != v) {
kpeter@604
  1189
        if (_succ_num[u] < _succ_num[v]) {
kpeter@604
  1190
          u = _parent[u];
kpeter@604
  1191
        } else {
kpeter@604
  1192
          v = _parent[v];
kpeter@604
  1193
        }
kpeter@601
  1194
      }
kpeter@601
  1195
      join = u;
kpeter@601
  1196
    }
kpeter@601
  1197
kpeter@601
  1198
    // Find the leaving arc of the cycle and returns true if the
kpeter@601
  1199
    // leaving arc is not the same as the entering arc
kpeter@601
  1200
    bool findLeavingArc() {
kpeter@601
  1201
      // Initialize first and second nodes according to the direction
kpeter@601
  1202
      // of the cycle
kpeter@603
  1203
      if (_state[in_arc] == STATE_LOWER) {
kpeter@603
  1204
        first  = _source[in_arc];
kpeter@603
  1205
        second = _target[in_arc];
kpeter@601
  1206
      } else {
kpeter@603
  1207
        first  = _target[in_arc];
kpeter@603
  1208
        second = _source[in_arc];
kpeter@601
  1209
      }
kpeter@603
  1210
      delta = _cap[in_arc];
kpeter@601
  1211
      int result = 0;
kpeter@641
  1212
      Value d;
kpeter@601
  1213
      int e;
kpeter@601
  1214
kpeter@601
  1215
      // Search the cycle along the path form the first node to the root
kpeter@601
  1216
      for (int u = first; u != join; u = _parent[u]) {
kpeter@601
  1217
        e = _pred[u];
kpeter@640
  1218
        d = _forward[u] ?
kpeter@811
  1219
          _flow[e] : (_cap[e] >= MAX ? INF : _cap[e] - _flow[e]);
kpeter@601
  1220
        if (d < delta) {
kpeter@601
  1221
          delta = d;
kpeter@601
  1222
          u_out = u;
kpeter@601
  1223
          result = 1;
kpeter@601
  1224
        }
kpeter@601
  1225
      }
kpeter@601
  1226
      // Search the cycle along the path form the second node to the root
kpeter@601
  1227
      for (int u = second; u != join; u = _parent[u]) {
kpeter@601
  1228
        e = _pred[u];
kpeter@640
  1229
        d = _forward[u] ? 
kpeter@811
  1230
          (_cap[e] >= MAX ? INF : _cap[e] - _flow[e]) : _flow[e];
kpeter@601
  1231
        if (d <= delta) {
kpeter@601
  1232
          delta = d;
kpeter@601
  1233
          u_out = u;
kpeter@601
  1234
          result = 2;
kpeter@601
  1235
        }
kpeter@601
  1236
      }
kpeter@601
  1237
kpeter@601
  1238
      if (result == 1) {
kpeter@601
  1239
        u_in = first;
kpeter@601
  1240
        v_in = second;
kpeter@601
  1241
      } else {
kpeter@601
  1242
        u_in = second;
kpeter@601
  1243
        v_in = first;
kpeter@601
  1244
      }
kpeter@601
  1245
      return result != 0;
kpeter@601
  1246
    }
kpeter@601
  1247
kpeter@601
  1248
    // Change _flow and _state vectors
kpeter@601
  1249
    void changeFlow(bool change) {
kpeter@601
  1250
      // Augment along the cycle
kpeter@601
  1251
      if (delta > 0) {
kpeter@641
  1252
        Value val = _state[in_arc] * delta;
kpeter@603
  1253
        _flow[in_arc] += val;
kpeter@603
  1254
        for (int u = _source[in_arc]; u != join; u = _parent[u]) {
kpeter@601
  1255
          _flow[_pred[u]] += _forward[u] ? -val : val;
kpeter@601
  1256
        }
kpeter@603
  1257
        for (int u = _target[in_arc]; u != join; u = _parent[u]) {
kpeter@601
  1258
          _flow[_pred[u]] += _forward[u] ? val : -val;
kpeter@601
  1259
        }
kpeter@601
  1260
      }
kpeter@601
  1261
      // Update the state of the entering and leaving arcs
kpeter@601
  1262
      if (change) {
kpeter@603
  1263
        _state[in_arc] = STATE_TREE;
kpeter@601
  1264
        _state[_pred[u_out]] =
kpeter@601
  1265
          (_flow[_pred[u_out]] == 0) ? STATE_LOWER : STATE_UPPER;
kpeter@601
  1266
      } else {
kpeter@603
  1267
        _state[in_arc] = -_state[in_arc];
kpeter@601
  1268
      }
kpeter@601
  1269
    }
kpeter@601
  1270
kpeter@604
  1271
    // Update the tree structure
kpeter@604
  1272
    void updateTreeStructure() {
kpeter@604
  1273
      int u, w;
kpeter@604
  1274
      int old_rev_thread = _rev_thread[u_out];
kpeter@604
  1275
      int old_succ_num = _succ_num[u_out];
kpeter@604
  1276
      int old_last_succ = _last_succ[u_out];
kpeter@601
  1277
      v_out = _parent[u_out];
kpeter@601
  1278
kpeter@604
  1279
      u = _last_succ[u_in];  // the last successor of u_in
kpeter@604
  1280
      right = _thread[u];    // the node after it
kpeter@604
  1281
kpeter@604
  1282
      // Handle the case when old_rev_thread equals to v_in
kpeter@604
  1283
      // (it also means that join and v_out coincide)
kpeter@604
  1284
      if (old_rev_thread == v_in) {
kpeter@604
  1285
        last = _thread[_last_succ[u_out]];
kpeter@604
  1286
      } else {
kpeter@604
  1287
        last = _thread[v_in];
kpeter@601
  1288
      }
kpeter@601
  1289
kpeter@604
  1290
      // Update _thread and _parent along the stem nodes (i.e. the nodes
kpeter@604
  1291
      // between u_in and u_out, whose parent have to be changed)
kpeter@601
  1292
      _thread[v_in] = stem = u_in;
kpeter@604
  1293
      _dirty_revs.clear();
kpeter@604
  1294
      _dirty_revs.push_back(v_in);
kpeter@601
  1295
      par_stem = v_in;
kpeter@601
  1296
      while (stem != u_out) {
kpeter@604
  1297
        // Insert the next stem node into the thread list
kpeter@604
  1298
        new_stem = _parent[stem];
kpeter@604
  1299
        _thread[u] = new_stem;
kpeter@604
  1300
        _dirty_revs.push_back(u);
kpeter@601
  1301
kpeter@604
  1302
        // Remove the subtree of stem from the thread list
kpeter@604
  1303
        w = _rev_thread[stem];
kpeter@604
  1304
        _thread[w] = right;
kpeter@604
  1305
        _rev_thread[right] = w;
kpeter@601
  1306
kpeter@604
  1307
        // Change the parent node and shift stem nodes
kpeter@601
  1308
        _parent[stem] = par_stem;
kpeter@601
  1309
        par_stem = stem;
kpeter@601
  1310
        stem = new_stem;
kpeter@601
  1311
kpeter@604
  1312
        // Update u and right
kpeter@604
  1313
        u = _last_succ[stem] == _last_succ[par_stem] ?
kpeter@604
  1314
          _rev_thread[par_stem] : _last_succ[stem];
kpeter@601
  1315
        right = _thread[u];
kpeter@601
  1316
      }
kpeter@601
  1317
      _parent[u_out] = par_stem;
kpeter@601
  1318
      _thread[u] = last;
kpeter@604
  1319
      _rev_thread[last] = u;
kpeter@604
  1320
      _last_succ[u_out] = u;
kpeter@601
  1321
kpeter@604
  1322
      // Remove the subtree of u_out from the thread list except for
kpeter@604
  1323
      // the case when old_rev_thread equals to v_in
kpeter@604
  1324
      // (it also means that join and v_out coincide)
kpeter@604
  1325
      if (old_rev_thread != v_in) {
kpeter@604
  1326
        _thread[old_rev_thread] = right;
kpeter@604
  1327
        _rev_thread[right] = old_rev_thread;
kpeter@604
  1328
      }
kpeter@604
  1329
kpeter@604
  1330
      // Update _rev_thread using the new _thread values
kpeter@604
  1331
      for (int i = 0; i < int(_dirty_revs.size()); ++i) {
kpeter@604
  1332
        u = _dirty_revs[i];
kpeter@604
  1333
        _rev_thread[_thread[u]] = u;
kpeter@604
  1334
      }
kpeter@604
  1335
kpeter@604
  1336
      // Update _pred, _forward, _last_succ and _succ_num for the
kpeter@604
  1337
      // stem nodes from u_out to u_in
kpeter@604
  1338
      int tmp_sc = 0, tmp_ls = _last_succ[u_out];
kpeter@604
  1339
      u = u_out;
kpeter@604
  1340
      while (u != u_in) {
kpeter@604
  1341
        w = _parent[u];
kpeter@604
  1342
        _pred[u] = _pred[w];
kpeter@604
  1343
        _forward[u] = !_forward[w];
kpeter@604
  1344
        tmp_sc += _succ_num[u] - _succ_num[w];
kpeter@604
  1345
        _succ_num[u] = tmp_sc;
kpeter@604
  1346
        _last_succ[w] = tmp_ls;
kpeter@604
  1347
        u = w;
kpeter@604
  1348
      }
kpeter@604
  1349
      _pred[u_in] = in_arc;
kpeter@604
  1350
      _forward[u_in] = (u_in == _source[in_arc]);
kpeter@604
  1351
      _succ_num[u_in] = old_succ_num;
kpeter@604
  1352
kpeter@604
  1353
      // Set limits for updating _last_succ form v_in and v_out
kpeter@604
  1354
      // towards the root
kpeter@604
  1355
      int up_limit_in = -1;
kpeter@604
  1356
      int up_limit_out = -1;
kpeter@604
  1357
      if (_last_succ[join] == v_in) {
kpeter@604
  1358
        up_limit_out = join;
kpeter@601
  1359
      } else {
kpeter@604
  1360
        up_limit_in = join;
kpeter@604
  1361
      }
kpeter@604
  1362
kpeter@604
  1363
      // Update _last_succ from v_in towards the root
kpeter@604
  1364
      for (u = v_in; u != up_limit_in && _last_succ[u] == v_in;
kpeter@604
  1365
           u = _parent[u]) {
kpeter@604
  1366
        _last_succ[u] = _last_succ[u_out];
kpeter@604
  1367
      }
kpeter@604
  1368
      // Update _last_succ from v_out towards the root
kpeter@604
  1369
      if (join != old_rev_thread && v_in != old_rev_thread) {
kpeter@604
  1370
        for (u = v_out; u != up_limit_out && _last_succ[u] == old_last_succ;
kpeter@604
  1371
             u = _parent[u]) {
kpeter@604
  1372
          _last_succ[u] = old_rev_thread;
kpeter@604
  1373
        }
kpeter@604
  1374
      } else {
kpeter@604
  1375
        for (u = v_out; u != up_limit_out && _last_succ[u] == old_last_succ;
kpeter@604
  1376
             u = _parent[u]) {
kpeter@604
  1377
          _last_succ[u] = _last_succ[u_out];
kpeter@604
  1378
        }
kpeter@604
  1379
      }
kpeter@604
  1380
kpeter@604
  1381
      // Update _succ_num from v_in to join
kpeter@604
  1382
      for (u = v_in; u != join; u = _parent[u]) {
kpeter@604
  1383
        _succ_num[u] += old_succ_num;
kpeter@604
  1384
      }
kpeter@604
  1385
      // Update _succ_num from v_out to join
kpeter@604
  1386
      for (u = v_out; u != join; u = _parent[u]) {
kpeter@604
  1387
        _succ_num[u] -= old_succ_num;
kpeter@601
  1388
      }
kpeter@601
  1389
    }
kpeter@601
  1390
kpeter@604
  1391
    // Update potentials
kpeter@604
  1392
    void updatePotential() {
kpeter@607
  1393
      Cost sigma = _forward[u_in] ?
kpeter@601
  1394
        _pi[v_in] - _pi[u_in] - _cost[_pred[u_in]] :
kpeter@601
  1395
        _pi[v_in] - _pi[u_in] + _cost[_pred[u_in]];
kpeter@608
  1396
      // Update potentials in the subtree, which has been moved
kpeter@608
  1397
      int end = _thread[_last_succ[u_in]];
kpeter@608
  1398
      for (int u = u_in; u != end; u = _thread[u]) {
kpeter@608
  1399
        _pi[u] += sigma;
kpeter@601
  1400
      }
kpeter@601
  1401
    }
kpeter@601
  1402
kpeter@601
  1403
    // Execute the algorithm
kpeter@640
  1404
    ProblemType start(PivotRule pivot_rule) {
kpeter@601
  1405
      // Select the pivot rule implementation
kpeter@601
  1406
      switch (pivot_rule) {
kpeter@605
  1407
        case FIRST_ELIGIBLE:
kpeter@601
  1408
          return start<FirstEligiblePivotRule>();
kpeter@605
  1409
        case BEST_ELIGIBLE:
kpeter@601
  1410
          return start<BestEligiblePivotRule>();
kpeter@605
  1411
        case BLOCK_SEARCH:
kpeter@601
  1412
          return start<BlockSearchPivotRule>();
kpeter@605
  1413
        case CANDIDATE_LIST:
kpeter@601
  1414
          return start<CandidateListPivotRule>();
kpeter@605
  1415
        case ALTERING_LIST:
kpeter@601
  1416
          return start<AlteringListPivotRule>();
kpeter@601
  1417
      }
kpeter@640
  1418
      return INFEASIBLE; // avoid warning
kpeter@601
  1419
    }
kpeter@601
  1420
kpeter@605
  1421
    template <typename PivotRuleImpl>
kpeter@640
  1422
    ProblemType start() {
kpeter@605
  1423
      PivotRuleImpl pivot(*this);
kpeter@601
  1424
kpeter@605
  1425
      // Execute the Network Simplex algorithm
kpeter@601
  1426
      while (pivot.findEnteringArc()) {
kpeter@601
  1427
        findJoinNode();
kpeter@601
  1428
        bool change = findLeavingArc();
kpeter@811
  1429
        if (delta >= MAX) return UNBOUNDED;
kpeter@601
  1430
        changeFlow(change);
kpeter@601
  1431
        if (change) {
kpeter@604
  1432
          updateTreeStructure();
kpeter@604
  1433
          updatePotential();
kpeter@601
  1434
        }
kpeter@601
  1435
      }
kpeter@640
  1436
      
kpeter@640
  1437
      // Check feasibility
kpeter@663
  1438
      for (int e = _search_arc_num; e != _all_arc_num; ++e) {
kpeter@663
  1439
        if (_flow[e] != 0) return INFEASIBLE;
kpeter@640
  1440
      }
kpeter@601
  1441
kpeter@642
  1442
      // Transform the solution and the supply map to the original form
kpeter@642
  1443
      if (_have_lower) {
kpeter@601
  1444
        for (int i = 0; i != _arc_num; ++i) {
kpeter@642
  1445
          Value c = _lower[i];
kpeter@642
  1446
          if (c != 0) {
kpeter@642
  1447
            _flow[i] += c;
kpeter@642
  1448
            _supply[_source[i]] += c;
kpeter@642
  1449
            _supply[_target[i]] -= c;
kpeter@642
  1450
          }
kpeter@601
  1451
        }
kpeter@601
  1452
      }
kpeter@663
  1453
      
kpeter@663
  1454
      // Shift potentials to meet the requirements of the GEQ/LEQ type
kpeter@663
  1455
      // optimality conditions
kpeter@663
  1456
      if (_sum_supply == 0) {
kpeter@663
  1457
        if (_stype == GEQ) {
kpeter@663
  1458
          Cost max_pot = std::numeric_limits<Cost>::min();
kpeter@663
  1459
          for (int i = 0; i != _node_num; ++i) {
kpeter@663
  1460
            if (_pi[i] > max_pot) max_pot = _pi[i];
kpeter@663
  1461
          }
kpeter@663
  1462
          if (max_pot > 0) {
kpeter@663
  1463
            for (int i = 0; i != _node_num; ++i)
kpeter@663
  1464
              _pi[i] -= max_pot;
kpeter@663
  1465
          }
kpeter@663
  1466
        } else {
kpeter@663
  1467
          Cost min_pot = std::numeric_limits<Cost>::max();
kpeter@663
  1468
          for (int i = 0; i != _node_num; ++i) {
kpeter@663
  1469
            if (_pi[i] < min_pot) min_pot = _pi[i];
kpeter@663
  1470
          }
kpeter@663
  1471
          if (min_pot < 0) {
kpeter@663
  1472
            for (int i = 0; i != _node_num; ++i)
kpeter@663
  1473
              _pi[i] -= min_pot;
kpeter@663
  1474
          }
kpeter@663
  1475
        }
kpeter@663
  1476
      }
kpeter@601
  1477
kpeter@640
  1478
      return OPTIMAL;
kpeter@601
  1479
    }
kpeter@601
  1480
kpeter@601
  1481
  }; //class NetworkSimplex
kpeter@601
  1482
kpeter@601
  1483
  ///@}
kpeter@601
  1484
kpeter@601
  1485
} //namespace lemon
kpeter@601
  1486
kpeter@601
  1487
#endif //LEMON_NETWORK_SIMPLEX_H