/* -*- C++ -*-

  *

  * This file is a part of LEMON, a generic C++ optimization library

  *

  * Copyright (C) 2003-2008

  * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport

  * (Egervary Research Group on Combinatorial Optimization, EGRES).

  *

  * Permission to use, modify and distribute this software is granted

  * provided that this copyright notice appears in all copies. For

  * precise terms see the accompanying LICENSE file.

  *

  * This software is provided "AS IS" with no warranty of any kind,

  * express or implied, and with no claim as to its suitability for any

  * purpose.

  *

  */

 #ifndef LEMON_CAPACITY_SCALING_H

 #define LEMON_CAPACITY_SCALING_H

 /// \ingroup min_cost_flow

 ///

 /// \file

 /// \brief Capacity scaling algorithm for finding a minimum cost flow.

 #include <vector>

 #include <lemon/graph_adaptor.h>

 #include <lemon/bin_heap.h>

 namespace lemon {

   /// \addtogroup min_cost_flow

   /// @{

   /// \brief Implementation of the capacity scaling algorithm for

   /// finding a minimum cost flow.

   ///

   /// \ref CapacityScaling implements the capacity scaling version

   /// of the successive shortest path algorithm for finding a minimum

   /// cost flow.

   ///

   /// \tparam Graph The directed graph type the algorithm runs on.

   /// \tparam LowerMap The type of the lower bound map.

   /// \tparam CapacityMap The type of the capacity (upper bound) map.

   /// \tparam CostMap The type of the cost (length) map.

   /// \tparam SupplyMap The type of the supply map.

   ///

   /// \warning

   /// - Edge capacities and costs should be \e non-negative \e integers.

   /// - Supply values should be \e signed \e integers.

   /// - \c LowerMap::Value must be convertible to \c CapacityMap::Value.

   /// - \c CapacityMap::Value and \c SupplyMap::Value must be

   ///   convertible to each other.

   /// - All value types must be convertible to \c CostMap::Value, which

   ///   must be signed type.

   ///

   /// \author Peter Kovacs

   template < typename Graph,

              typename LowerMap = typename Graph::template EdgeMap<int>,

              typename CapacityMap = typename Graph::template EdgeMap<int>,

              typename CostMap = typename Graph::template EdgeMap<int>,

              typename SupplyMap = typename Graph::template NodeMap<int> >

   class CapacityScaling

   {

     GRAPH_TYPEDEFS(typename Graph);

     typedef typename CapacityMap::Value Capacity;

     typedef typename CostMap::Value Cost;

     typedef typename SupplyMap::Value Supply;

     typedef typename Graph::template EdgeMap<Capacity> CapacityEdgeMap;

     typedef typename Graph::template NodeMap<Supply> SupplyNodeMap;

     typedef typename Graph::template NodeMap<Edge> PredMap;

   public:

     /// The type of the flow map.

     typedef typename Graph::template EdgeMap<Capacity> FlowMap;

     /// The type of the potential map.

     typedef typename Graph::template NodeMap<Cost> PotentialMap;

   private:

     /// \brief Special implementation of the \ref Dijkstra algorithm

     /// for finding shortest paths in the residual network.

     ///

     /// \ref ResidualDijkstra is a special implementation of the

     /// \ref Dijkstra algorithm for finding shortest paths in the

     /// residual network of the graph with respect to the reduced edge

     /// costs and modifying the node potentials according to the

     /// distance of the nodes.

     class ResidualDijkstra

     {

       typedef typename Graph::template NodeMap<Cost> CostNodeMap;

       typedef typename Graph::template NodeMap<Edge> PredMap;

       typedef typename Graph::template NodeMap<int> HeapCrossRef;

       typedef BinHeap<Cost, HeapCrossRef> Heap;

     private:

       // The directed graph the algorithm runs on

       const Graph &_graph;

       // The main maps

       const FlowMap &_flow;

       const CapacityEdgeMap &_res_cap;

       const CostMap &_cost;

       const SupplyNodeMap &_excess;

       PotentialMap &_potential;

       // The distance map

       CostNodeMap _dist;

       // The pred edge map

       PredMap &_pred;

       // The processed (i.e. permanently labeled) nodes

       std::vector<Node> _proc_nodes;

     public:

       /// The constructor of the class.

       ResidualDijkstra( const Graph &graph,

                         const FlowMap &flow,

                         const CapacityEdgeMap &res_cap,

                         const CostMap &cost,

                         const SupplyMap &excess,

                         PotentialMap &potential,

                         PredMap &pred ) :

         _graph(graph), _flow(flow), _res_cap(res_cap), _cost(cost),

         _excess(excess), _potential(potential), _dist(graph),

         _pred(pred)

       {}

       /// Runs the algorithm from the given source node.

       Node run(Node s, Capacity delta) {

         HeapCrossRef heap_cross_ref(_graph, Heap::PRE_HEAP);

         Heap heap(heap_cross_ref);

         heap.push(s, 0);

         _pred[s] = INVALID;

         _proc_nodes.clear();

         // Processing nodes

         while (!heap.empty() && _excess[heap.top()] > -delta) {

           Node u = heap.top(), v;

           Cost d = heap.prio() + _potential[u], nd;

           _dist[u] = heap.prio();

           heap.pop();

           _proc_nodes.push_back(u);

           // Traversing outgoing edges

           for (OutEdgeIt e(_graph, u); e != INVALID; ++e) {

             if (_res_cap[e] >= delta) {

               v = _graph.target(e);

               switch(heap.state(v)) {

               case Heap::PRE_HEAP:

                 heap.push(v, d + _cost[e] - _potential[v]);

                 _pred[v] = e;

                 break;

               case Heap::IN_HEAP:

                 nd = d + _cost[e] - _potential[v];

                 if (nd < heap[v]) {

                   heap.decrease(v, nd);

                   _pred[v] = e;

                 }

                 break;

               case Heap::POST_HEAP:

                 break;

               }

             }

           }

           // Traversing incoming edges

           for (InEdgeIt e(_graph, u); e != INVALID; ++e) {

             if (_flow[e] >= delta) {

               v = _graph.source(e);

               switch(heap.state(v)) {

               case Heap::PRE_HEAP:

                 heap.push(v, d - _cost[e] - _potential[v]);

                 _pred[v] = e;

                 break;

               case Heap::IN_HEAP:

                 nd = d - _cost[e] - _potential[v];

                 if (nd < heap[v]) {

                   heap.decrease(v, nd);

                   _pred[v] = e;

                 }

                 break;

               case Heap::POST_HEAP:

                 break;

               }

             }

           }

         }

         if (heap.empty()) return INVALID;

         // Updating potentials of processed nodes

         Node t = heap.top();

         Cost t_dist = heap.prio();

         for (int i = 0; i < int(_proc_nodes.size()); ++i)

           _potential[_proc_nodes[i]] += _dist[_proc_nodes[i]] - t_dist;

         return t;

       }

     }; //class ResidualDijkstra

   private:

     // The directed graph the algorithm runs on

     const Graph &_graph;

     // The original lower bound map

     const LowerMap *_lower;

     // The modified capacity map

     CapacityEdgeMap _capacity;

     // The original cost map

     const CostMap &_cost;

     // The modified supply map

     SupplyNodeMap _supply;

     bool _valid_supply;

     // Edge map of the current flow

     FlowMap _flow;

     // Node map of the current potentials

     PotentialMap _potential;

     // The residual capacity map

     CapacityEdgeMap _res_cap;

     // The excess map

     SupplyNodeMap _excess;

     // The excess nodes (i.e. nodes with positive excess)

     std::vector<Node> _excess_nodes;

     // The deficit nodes (i.e. nodes with negative excess)

     std::vector<Node> _deficit_nodes;

     // The delta parameter used for capacity scaling

     Capacity _delta;

     // The maximum number of phases

     int _phase_num;

     // The pred edge map

     PredMap _pred;

     // Implementation of the Dijkstra algorithm for finding augmenting

     // shortest paths in the residual network

     ResidualDijkstra _dijkstra;

   public :

     /// \brief General constructor of the class (with lower bounds).

     ///

     /// General constructor of the class (with lower bounds).

     ///

     /// \param graph The directed graph the algorithm runs on.

     /// \param lower The lower bounds of the edges.

     /// \param capacity The capacities (upper bounds) of the edges.

     /// \param cost The cost (length) values of the edges.

     /// \param supply The supply values of the nodes (signed).

     CapacityScaling( const Graph &graph,

                      const LowerMap &lower,

                      const CapacityMap &capacity,

                      const CostMap &cost,

                      const SupplyMap &supply ) :

       _graph(graph), _lower(&lower), _capacity(graph), _cost(cost),

       _supply(graph), _flow(graph, 0), _potential(graph, 0),

       _res_cap(graph), _excess(graph), _pred(graph),

       _dijkstra(_graph, _flow, _res_cap, _cost, _excess, _potential, _pred)

     {

       // Removing non-zero lower bounds

       _capacity = subMap(capacity, lower);

       _res_cap = _capacity;

       Supply sum = 0;

       for (NodeIt n(_graph); n != INVALID; ++n) {

         Supply s = supply[n];

         for (InEdgeIt e(_graph, n); e != INVALID; ++e)

           s += lower[e];

         for (OutEdgeIt e(_graph, n); e != INVALID; ++e)

           s -= lower[e];

         _supply[n] = s;

         sum += s;

       }

       _valid_supply = sum == 0;

     }

     /// \brief General constructor of the class (without lower bounds).

     ///

     /// General constructor of the class (without lower bounds).

     ///

     /// \param graph The directed graph the algorithm runs on.

     /// \param capacity The capacities (upper bounds) of the edges.

     /// \param cost The cost (length) values of the edges.

     /// \param supply The supply values of the nodes (signed).

     CapacityScaling( const Graph &graph,

                      const CapacityMap &capacity,

                      const CostMap &cost,

                      const SupplyMap &supply ) :

       _graph(graph), _lower(NULL), _capacity(capacity), _cost(cost),

       _supply(supply), _flow(graph, 0), _potential(graph, 0),

       _res_cap(capacity), _excess(graph), _pred(graph),

       _dijkstra(_graph, _flow, _res_cap, _cost, _excess, _potential, _pred)

     {

       // Checking the sum of supply values

       Supply sum = 0;

       for (NodeIt n(_graph); n != INVALID; ++n) sum += _supply[n];

       _valid_supply = sum == 0;

     }

     /// \brief Simple constructor of the class (with lower bounds).

     ///

     /// Simple constructor of the class (with lower bounds).

     ///

     /// \param graph The directed graph the algorithm runs on.

     /// \param lower The lower bounds of the edges.

     /// \param capacity The capacities (upper bounds) of the edges.

     /// \param cost The cost (length) values of the edges.

     /// \param s The source node.

     /// \param t The target node.

     /// \param flow_value The required amount of flow from node \c s

     /// to node \c t (i.e. the supply of \c s and the demand of \c t).

     CapacityScaling( const Graph &graph,

                      const LowerMap &lower,

                      const CapacityMap &capacity,

                      const CostMap &cost,

                      Node s, Node t,

                      Supply flow_value ) :

       _graph(graph), _lower(&lower), _capacity(graph), _cost(cost),

       _supply(graph), _flow(graph, 0), _potential(graph, 0),

       _res_cap(graph), _excess(graph), _pred(graph),

       _dijkstra(_graph, _flow, _res_cap, _cost, _excess, _potential, _pred)

     {

       // Removing non-zero lower bounds

       _capacity = subMap(capacity, lower);

       _res_cap = _capacity;

       for (NodeIt n(_graph); n != INVALID; ++n) {

         Supply sum = 0;

         if (n == s) sum =  flow_value;

         if (n == t) sum = -flow_value;

         for (InEdgeIt e(_graph, n); e != INVALID; ++e)

           sum += lower[e];

         for (OutEdgeIt e(_graph, n); e != INVALID; ++e)

           sum -= lower[e];

         _supply[n] = sum;

       }

       _valid_supply = true;

     }

     /// \brief Simple constructor of the class (without lower bounds).

     ///

     /// Simple constructor of the class (without lower bounds).

     ///

     /// \param graph The directed graph the algorithm runs on.

     /// \param capacity The capacities (upper bounds) of the edges.

     /// \param cost The cost (length) values of the edges.

     /// \param s The source node.

     /// \param t The target node.

     /// \param flow_value The required amount of flow from node \c s

     /// to node \c t (i.e. the supply of \c s and the demand of \c t).

     CapacityScaling( const Graph &graph,

                      const CapacityMap &capacity,

                      const CostMap &cost,

                      Node s, Node t,

                      Supply flow_value ) :

       _graph(graph), _lower(NULL), _capacity(capacity), _cost(cost),

       _supply(graph, 0), _flow(graph, 0), _potential(graph, 0),

       _res_cap(capacity), _excess(graph), _pred(graph),

       _dijkstra(_graph, _flow, _res_cap, _cost, _excess, _potential, _pred)

     {

       _supply[s] =  flow_value;

       _supply[t] = -flow_value;

       _valid_supply = true;

     }

     /// \brief Runs the algorithm.

     ///

     /// Runs the algorithm.

     ///

     /// \param scaling Enable or disable capacity scaling.

     /// If the maximum edge capacity and/or the amount of total supply

     /// is rather small, the algorithm could be slightly faster without

     /// scaling.

     ///

     /// \return \c true if a feasible flow can be found.

     bool run(bool scaling = true) {

       return init(scaling) && start();

     }

     /// \brief Returns a const reference to the edge map storing the

     /// found flow.

     ///

     /// Returns a const reference to the edge map storing the found flow.

     ///

     /// \pre \ref run() must be called before using this function.

     const FlowMap& flowMap() const {

       return _flow;

     }

     /// \brief Returns a const reference to the node map storing the

     /// found potentials (the dual solution).

     ///

     /// Returns a const reference to the node map storing the found

     /// potentials (the dual solution).

     ///

     /// \pre \ref run() must be called before using this function.

     const PotentialMap& potentialMap() const {

       return _potential;

     }

     /// \brief Returns the total cost of the found flow.

     ///

     /// Returns the total cost of the found flow. The complexity of the

     /// function is \f$ O(e) \f$.

     ///

     /// \pre \ref run() must be called before using this function.

     Cost totalCost() const {

       Cost c = 0;

       for (EdgeIt e(_graph); e != INVALID; ++e)

         c += _flow[e] * _cost[e];

       return c;

     }

   private:

     /// Initializes the algorithm.

     bool init(bool scaling) {

       if (!_valid_supply) return false;

       _excess = _supply;

       // Initilaizing delta value

       if (scaling) {

         // With scaling

         Supply max_sup = 0, max_dem = 0;

         for (NodeIt n(_graph); n != INVALID; ++n) {

           if ( _supply[n] > max_sup) max_sup =  _supply[n];

           if (-_supply[n] > max_dem) max_dem = -_supply[n];

         }

         if (max_dem < max_sup) max_sup = max_dem;

         _phase_num = 0;

         for (_delta = 1; 2 * _delta <= max_sup; _delta *= 2)

           ++_phase_num;

       } else {

         // Without scaling

         _delta = 1;

       }

       return true;

     }

     bool start() {

       if (_delta > 1)

         return startWithScaling();

       else

         return startWithoutScaling();

     }

     /// Executes the capacity scaling algorithm.

     bool startWithScaling() {

       // Processing capacity scaling phases

       Node s, t;

       int phase_cnt = 0;

       int factor = 4;

       while (true) {

         // Saturating all edges not satisfying the optimality condition

         for (EdgeIt e(_graph); e != INVALID; ++e) {

           Node u = _graph.source(e), v = _graph.target(e);

           Cost c = _cost[e] + _potential[u] - _potential[v];

           if (c < 0 && _res_cap[e] >= _delta) {

             _excess[u] -= _res_cap[e];

             _excess[v] += _res_cap[e];

             _flow[e] = _capacity[e];

             _res_cap[e] = 0;

           }

           else if (c > 0 && _flow[e] >= _delta) {

             _excess[u] += _flow[e];

             _excess[v] -= _flow[e];

             _flow[e] = 0;

             _res_cap[e] = _capacity[e];

           }

         }

         // Finding excess nodes and deficit nodes

         _excess_nodes.clear();

         _deficit_nodes.clear();

         for (NodeIt n(_graph); n != INVALID; ++n) {

           if (_excess[n] >=  _delta) _excess_nodes.push_back(n);

           if (_excess[n] <= -_delta) _deficit_nodes.push_back(n);

         }

         int next_node = 0;

         // Finding augmenting shortest paths

         while (next_node < int(_excess_nodes.size())) {

           // Checking deficit nodes

           if (_delta > 1) {

             bool delta_deficit = false;

             for (int i = 0; i < int(_deficit_nodes.size()); ++i) {

               if (_excess[_deficit_nodes[i]] <= -_delta) {

                 delta_deficit = true;

                 break;

               }

             }

             if (!delta_deficit) break;

           }

           // Running Dijkstra

           s = _excess_nodes[next_node];

           if ((t = _dijkstra.run(s, _delta)) == INVALID) {

             if (_delta > 1) {

               ++next_node;

               continue;

             }

             return false;

           }

           // Augmenting along a shortest path from s to t.

           Capacity d = _excess[s] < -_excess[t] ? _excess[s] : -_excess[t];

           Node u = t;

           Edge e;

           if (d > _delta) {

             while ((e = _pred[u]) != INVALID) {

               Capacity rc;

               if (u == _graph.target(e)) {

                 rc = _res_cap[e];

                 u = _graph.source(e);

               } else {

                 rc = _flow[e];

                 u = _graph.target(e);

               }

               if (rc < d) d = rc;

             }

           }

           u = t;

           while ((e = _pred[u]) != INVALID) {

             if (u == _graph.target(e)) {

               _flow[e] += d;

               _res_cap[e] -= d;

               u = _graph.source(e);

             } else {

               _flow[e] -= d;

               _res_cap[e] += d;

               u = _graph.target(e);

             }

           }

           _excess[s] -= d;

           _excess[t] += d;

           if (_excess[s] < _delta) ++next_node;

         }

         if (_delta == 1) break;

         if (++phase_cnt > _phase_num / 4) factor = 2;

         _delta = _delta <= factor ? 1 : _delta / factor;

       }

       // Handling non-zero lower bounds

       if (_lower) {

         for (EdgeIt e(_graph); e != INVALID; ++e)

           _flow[e] += (*_lower)[e];

       }

       return true;

     }

     /// Executes the successive shortest path algorithm.

     bool startWithoutScaling() {

       // Finding excess nodes

       for (NodeIt n(_graph); n != INVALID; ++n)

         if (_excess[n] > 0) _excess_nodes.push_back(n);

       if (_excess_nodes.size() == 0) return true;

       int next_node = 0;

       // Finding shortest paths

       Node s, t;

       while ( _excess[_excess_nodes[next_node]] > 0 ||

               ++next_node < int(_excess_nodes.size()) )

       {

         // Running Dijkstra

         s = _excess_nodes[next_node];

         if ((t = _dijkstra.run(s, 1)) == INVALID)

           return false;

         // Augmenting along a shortest path from s to t

         Capacity d = _excess[s] < -_excess[t] ? _excess[s] : -_excess[t];

         Node u = t;

         Edge e;

         while ((e = _pred[u]) != INVALID) {

           Capacity rc;

           if (u == _graph.target(e)) {

             rc = _res_cap[e];

             u = _graph.source(e);

           } else {

             rc = _flow[e];

             u = _graph.target(e);

           }

           if (rc < d) d = rc;

         }

         u = t;

         while ((e = _pred[u]) != INVALID) {

           if (u == _graph.target(e)) {

             _flow[e] += d;

             _res_cap[e] -= d;

             u = _graph.source(e);

           } else {

             _flow[e] -= d;

             _res_cap[e] += d;

             u = _graph.target(e);

           }

         }

         _excess[s] -= d;

         _excess[t] += d;

       }

       // Handling non-zero lower bounds

       if (_lower) {

         for (EdgeIt e(_graph); e != INVALID; ++e)

           _flow[e] += (*_lower)[e];

       }

       return true;

     }

   }; //class CapacityScaling

   ///@}

 } //namespace lemon

 #endif //LEMON_CAPACITY_SCALING_H