// -*- c++ -*-

 #ifndef HUGO_MINCOSTFLOW_H

 #define HUGO_MINCOSTFLOW_H

 ///\ingroup galgs

 ///\file

 ///\brief An algorithm for finding the minimum cost flow of given value in an uncapacitated network

 #include <hugo/dijkstra.h>

 #include <hugo/graph_wrapper.h>

 #include <hugo/maps.h>

 #include <vector>

 #include <list>

 #include <for_each_macros.h>

 #include <hugo/union_find.h>

 namespace hugo {

 /// \addtogroup galgs

 /// @{

   ///\brief Implementation of an algorithm for finding the minimum cost flow 

   /// of given value in an uncapacitated network

   /// 

   ///

   /// The class \ref hugo::MinCostFlow "MinCostFlow" implements

   /// an algorithm for solving the following general minimum cost flow problem>

   /// 

   ///

   ///

   /// \warning It is assumed here that the problem has a feasible solution

   ///

   /// The range of the length (weight) function is nonnegative reals but 

   /// the range of capacity function is the set of nonnegative integers. 

   /// It is not a polinomial time algorithm for counting the minimum cost

   /// maximal flow, since it counts the minimum cost flow for every value 0..M

   /// where \c M is the value of the maximal flow.

   ///

   ///\author Attila Bernath

   template <typename Graph, typename LengthMap, typename SupplyDemandMap>

   class MinCostFlow {

     typedef typename LengthMap::ValueType Length;

     typedef typename SupplyDemandMap::ValueType SupplyDemand;

     typedef typename Graph::Node Node;

     typedef typename Graph::NodeIt NodeIt;

     typedef typename Graph::Edge Edge;

     typedef typename Graph::OutEdgeIt OutEdgeIt;

     typedef typename Graph::template EdgeMap<int> EdgeIntMap;

     //    typedef ConstMap<Edge,int> ConstMap;

     typedef ResGraphWrapper<const Graph,int,CapacityMap,EdgeIntMap> ResGraphType;

     typedef typename ResGraphType::Edge ResGraphEdge;

     class ModLengthMap {   

       //typedef typename ResGraphType::template NodeMap<Length> NodeMap;

       typedef typename Graph::template NodeMap<Length> NodeMap;

       const ResGraphType& G;

       //      const EdgeIntMap& rev;

       const LengthMap &ol;

       const NodeMap &pot;

     public :

       typedef typename LengthMap::KeyType KeyType;

       typedef typename LengthMap::ValueType ValueType;

       ValueType operator[](typename ResGraphType::Edge e) const {     

 	if (G.forward(e))

 	  return  ol[e]-(pot[G.head(e)]-pot[G.tail(e)]);   

 	else

 	  return -ol[e]-(pot[G.head(e)]-pot[G.tail(e)]);   

       }     

       ModLengthMap(const ResGraphType& _G,

 		   const LengthMap &o,  const NodeMap &p) : 

 	G(_G), /*rev(_rev),*/ ol(o), pot(p){}; 

     };//ModLengthMap

   protected:

     //Input

     const Graph& G;

     const LengthMap& length;

     const SupplyDemandMap& supply_demand;//supply or demand of nodes

     //auxiliary variables

     //To store the flow

     EdgeIntMap flow; 

     //To store the potentila (dual variables)

     typename Graph::template NodeMap<Length> potential;

     //To store excess-deficit values

     SupplyDemandMap excess_deficit;

     Length total_length;

   public :

     MinCostFlow(Graph& _G, LengthMap& _length, SupplyDemandMap& _supply_demand) : G(_G), 

       length(_length), supply_demand(_supply_demand), flow(_G), potential(_G){ }

     ///Runs the algorithm.

     ///Runs the algorithm.

     ///\todo May be it does make sense to be able to start with a nonzero 

     /// feasible primal-dual solution pair as well.

     int run() {

       //Resetting variables from previous runs

       //total_length = 0;

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

       typedef Heap< Node, SupplyDemand, typename Graph::template NodeMap<int>,

 	std::greater<SupplyDemand> > 	HeapType;

       //A heap for the excess nodes

       HeapMap excess_nodes_map(G,-1);

       HeapType excess_nodes(excess_nodes_map);

       //A heap for the deficit nodes

       HeapMap deficit_nodes_map(G,-1);

       HeapType deficit_nodes(deficit_nodes_map);

       //A container to store nonabundant arcs

       list<Edge> nonabundant_arcs;

       FOR_EACH_LOC(typename Graph::EdgeIt, e, G){

 	flow.set(e,0);

 	nonabundant_arcs.push_back(e);

       }

       //Initial value for delta

       SupplyDemand delta = 0;

       typedef UnionFindEnum<Node, Graph::template NodeMap> UFE;

       //A union-find structure to store the abundant components

       UFE::MapType abund_comp_map(G);

       UFE abundant_components(abund_comp_map);

       FOR_EACH_LOC(typename Graph::NodeIt, n, G){

        	excess_deficit.set(n,supply_demand[n]);

 	//A supply node

 	if (excess_deficit[n] > 0){

 	  excess_nodes.push(n,excess_deficit[n]);

 	}

 	//A demand node

 	if (excess_deficit[n] < 0){

 	  deficit_nodes.push(n, - excess_deficit[n]);

 	}

 	//Finding out starting value of delta

 	if (delta < abs(excess_deficit[n])){

 	  delta = abs(excess_deficit[n]);

 	}

 	//Initialize the copy of the Dijkstra potential to zero

 	potential.set(n,0);

 	//Every single point is an abundant component initially 

 	abundant_components.insert(n);

       }

       //It'll be allright as an initial value, though this value 

       //can be the maximum deficit here

       SupplyDemand max_excess = delta;

       //We need a residual graph which is uncapacitated

       ResGraphType res_graph(G, flow);

       ModLengthMap mod_length(res_graph, length, potential);

       Dijkstra<ResGraphType, ModLengthMap> dijkstra(res_graph, mod_length);

       while (max_excess > 0){

 	//Reset delta if still too big

 	if (8*number_of_nodes*max_excess <= delta){

 	  delta = max_excess;

 	}

 	/*

 	 * Beginning of the delta scaling phase 

 	*/

 	//Merge and stuff

 	{

 	  SupplyDemand buf=8*number_of_nodes*delta;

 	  list<Edge>::iterator i = nonabundant_arcs.begin();

 	  while ( i != nonabundant_arcs.end() ){

 	    if (flow[i]>=buf){

 	      Node a = abundant_components.find(res_graph.head(i));

 	      Node b = abundant_components.find(res_graph.tail(i));

 	      //Merge

 	      if (a != b){

 		//Find path and augment

 		//!!!

 		//Find path and augment

 		abundant_components.join(a,b);

 	      }

 	      //What happens to i?

 	      nonabundant_arcs.erase(i);

 	    }

 	    else

 	      ++i;

 	  }

 	}

 	Node s = excess_nodes.top(); 

 	SupplyDemand max_excess = excess_nodes[s];

 	Node t = deficit_nodes.top(); 

 	if (max_excess < dificit_nodes[t]){

 	  max_excess = dificit_nodes[t];

 	}

 	while(max_excess > 0){

 	  //s es t valasztasa

 	  //Dijkstra part	

 	  dijkstra.run(s);

 	  /*We know from theory that t can be reached

 	  if (!dijkstra.reached(t)){

 	    //There are no k paths from s to t

 	    break;

 	  };

 	  */

 	  //We have to change the potential

 	  FOR_EACH_LOC(typename ResGraphType::NodeIt, n, res_graph){

 	    potential[n] += dijkstra.distMap()[n];

 	  }

 	  //Augmenting on the sortest path

 	  Node n=t;

 	  ResGraphEdge e;

 	  while (n!=s){

 	    e = dijkstra.pred(n);

 	    n = dijkstra.predNode(n);

 	    res_graph.augment(e,delta);

 	    /*

 	    //Let's update the total length

 	    if (res_graph.forward(e))

 	      total_length += length[e];

 	    else 

 	      total_length -= length[e];	    

 	    */

 	  }

 	  //Update the excess_nodes heap

 	  if (delta >= excess_nodes[s]){

 	    if (delta > excess_nodes[s])

 	      deficit_nodes.push(s,delta - excess_nodes[s]);

 	    excess_nodes.pop();

 	  } 

 	  else{

 	    excess_nodes[s] -= delta;

 	  }

 	  //Update the deficit_nodes heap

 	  if (delta >= deficit_nodes[t]){

 	    if (delta > deficit_nodes[t])

 	      excess_nodes.push(t,delta - deficit_nodes[t]);

 	    deficit_nodes.pop();

 	  } 

 	  else{

 	    deficit_nodes[t] -= delta;

 	  }

 	  //Dijkstra part ends here

 	}

 	/*

 	 * End of the delta scaling phase 

 	*/

 	//Whatever this means

 	delta = delta / 2;

 	/*This is not necessary here

 	//Update the max_excess

 	max_excess = 0;

 	FOR_EACH_LOC(typename Graph::NodeIt, n, G){

 	  if (max_excess < excess_deficit[n]){

 	    max_excess = excess_deficit[n];

 	  }

 	}

 	*/

       }//while(max_excess > 0)

       return i;

     }

     ///This function gives back the total length of the found paths.

     ///Assumes that \c run() has been run and nothing changed since then.

     Length totalLength(){

       return total_length;

     }

     ///Returns a const reference to the EdgeMap \c flow. \pre \ref run() must

     ///be called before using this function.

     const EdgeIntMap &getFlow() const { return flow;}

   ///Returns a const reference to the NodeMap \c potential (the dual solution).

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

     const EdgeIntMap &getPotential() const { return potential;}

     ///This function checks, whether the given solution is optimal

     ///Running after a \c run() should return with true

     ///In this "state of the art" this only check optimality, doesn't bother with feasibility

     ///

     ///\todo Is this OK here?

     bool checkComplementarySlackness(){

       Length mod_pot;

       Length fl_e;

       FOR_EACH_LOC(typename Graph::EdgeIt, e, G){

 	//C^{\Pi}_{i,j}

 	mod_pot = length[e]-potential[G.head(e)]+potential[G.tail(e)];

 	fl_e = flow[e];

 	//	std::cout << fl_e << std::endl;

 	if (0<fl_e && fl_e<capacity[e]){

 	  if (mod_pot != 0)

 	    return false;

 	}

 	else{

 	  if (mod_pot > 0 && fl_e != 0)

 	    return false;

 	  if (mod_pot < 0 && fl_e != capacity[e])

 	    return false;

 	}

       }

       return true;

     }

   }; //class MinCostFlow

   ///@}

 } //namespace hugo

 #endif //HUGO_MINCOSTFLOW_H