lemon-1.2: comparison lemon/network

equal deleted inserted replaced

-:48bb94977009
+:8729eb5cd438
 */
 #ifndef LEMON_NETWORK_SIMPLEX_H
 #define LEMON_NETWORK_SIMPLEX_H
-/// \ingroup min_cost_flow
+/// \ingroup min_cost_flow_algs
 ///
 /// \file
 /// \brief Network Simplex algorithm for finding a minimum cost flow.
 #include <vector>
 #include <lemon/core.h>
 #include <lemon/math.h>
 namespace lemon {
-/// \addtogroup min_cost_flow
+/// \addtogroup min_cost_flow_algs
 /// @{
 /// \brief Implementation of the primal Network Simplex algorithm
 /// for finding a \ref min_cost_flow "minimum cost flow".
 ///
 ///
 /// Enum type containing constants for selecting the supply type,
 /// i.e. the direction of the inequalities in the supply/demand
 /// constraints of the \ref min_cost_flow "minimum cost flow problem".
 ///
-/// The default supply type is \c GEQ, since this form is supported
+/// The default supply type is \c GEQ, the \c LEQ type can be
-/// by other minimum cost flow algorithms and the \ref Circulation
+/// selected using \ref supplyType().
-/// algorithm, as well.
+/// The equality form is a special case of both supply types.
-/// The \c LEQ problem type can be selected using the \ref supplyType()
-/// function.
-///
-/// Note that the equality form is a special case of both supply types.
 enum SupplyType {
 /// This option means that there are <em>"greater or equal"</em>
-/// supply/demand constraints in the definition, i.e. the exact
+/// supply/demand constraints in the definition of the problem.
-/// formulation of the problem is the following.
-/**
-\f[ \min\sum_{uv\in A} f(uv) \cdot cost(uv) \f]
-\f[ \sum_{uv\in A} f(uv) - \sum_{vu\in A} f(vu) \geq
-sup(u) \quad \forall u\in V \f]
-\f[ lower(uv) \leq f(uv) \leq upper(uv) \quad \forall uv\in A \f]
-*/
-/// It means that the total demand must be greater or equal to the
-/// total supply (i.e. \f$\sum_{u\in V} sup(u)\f$ must be zero or
-/// negative) and all the supplies have to be carried out from
-/// the supply nodes, but there could be demands that are not
-/// satisfied.
 GEQ,
-/// It is just an alias for the \c GEQ option.
-CARRY_SUPPLIES = GEQ,
 /// This option means that there are <em>"less or equal"</em>
-/// supply/demand constraints in the definition, i.e. the exact
+/// supply/demand constraints in the definition of the problem.
-/// formulation of the problem is the following.
+LEQ
-/**
-\f[ \min\sum_{uv\in A} f(uv) \cdot cost(uv) \f]
-\f[ \sum_{uv\in A} f(uv) - \sum_{vu\in A} f(vu) \leq
-sup(u) \quad \forall u\in V \f]
-\f[ lower(uv) \leq f(uv) \leq upper(uv) \quad \forall uv\in A \f]
-*/
-/// It means that the total demand must be less or equal to the
-/// total supply (i.e. \f$\sum_{u\in V} sup(u)\f$ must be zero or
-/// positive) and all the demands have to be satisfied, but there
-/// could be supplies that are not carried out from the supply
-/// nodes.
-LEQ,
-/// It is just an alias for the \c LEQ option.
-SATISFY_DEMANDS = LEQ
 };
 /// \brief Constants for selecting the pivot rule.
 ///
 /// Enum type containing constants for selecting the pivot rule for
 // Data related to the underlying digraph
 const GR &_graph;
 int _node_num;
 int _arc_num;
+int _all_arc_num;
+int _search_arc_num;
 // Parameters of the problem
 bool _have_lower;
 SupplyType _stype;
 Value _sum_supply;
 const IntVector  &_target;
 const CostVector &_cost;
 const IntVector  &_state;
 const CostVector &_pi;
 int &_in_arc;
-int _arc_num;
+int _search_arc_num;
 // Pivot rule data
 int _next_arc;
 public:
 // Constructor
 FirstEligiblePivotRule(NetworkSimplex &ns) :
 _source(ns._source), _target(ns._target),
 _cost(ns._cost), _state(ns._state), _pi(ns._pi),
-_in_arc(ns.in_arc), _arc_num(ns._arc_num), _next_arc(0)
+_in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num),
+_next_arc(0)
 {}
 // Find next entering arc
 bool findEnteringArc() {
 Cost c;
-for (int e = _next_arc; e < _arc_num; ++e) {
+for (int e = _next_arc; e < _search_arc_num; ++e) {
 c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
 if (c < 0) {
 _in_arc = e;
 _next_arc = e + 1;
 return true;
 const IntVector  &_target;
 const CostVector &_cost;
 const IntVector  &_state;
 const CostVector &_pi;
 int &_in_arc;
-int _arc_num;
+int _search_arc_num;
 public:
 // Constructor
 BestEligiblePivotRule(NetworkSimplex &ns) :
 _source(ns._source), _target(ns._target),
 _cost(ns._cost), _state(ns._state), _pi(ns._pi),
-_in_arc(ns.in_arc), _arc_num(ns._arc_num)
+_in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num)
 {}
 // Find next entering arc
 bool findEnteringArc() {
 Cost c, min = 0;
-for (int e = 0; e < _arc_num; ++e) {
+for (int e = 0; e < _search_arc_num; ++e) {
 c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
 if (c < min) {
 min = c;
 _in_arc = e;
 }
 const IntVector  &_target;
 const CostVector &_cost;
 const IntVector  &_state;
 const CostVector &_pi;
 int &_in_arc;
-int _arc_num;
+int _search_arc_num;
 // Pivot rule data
 int _block_size;
 int _next_arc;
 // Constructor
 BlockSearchPivotRule(NetworkSimplex &ns) :
 _source(ns._source), _target(ns._target),
 _cost(ns._cost), _state(ns._state), _pi(ns._pi),
-_in_arc(ns.in_arc), _arc_num(ns._arc_num), _next_arc(0)
+_in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num),
+_next_arc(0)
 {
 // The main parameters of the pivot rule
-const double BLOCK_SIZE_FACTOR = 2.0;
+const double BLOCK_SIZE_FACTOR = 0.5;
 const int MIN_BLOCK_SIZE = 10;
 _block_size = std::max( int(BLOCK_SIZE_FACTOR *
-std::sqrt(double(_arc_num))),
+std::sqrt(double(_search_arc_num))),
 MIN_BLOCK_SIZE );
 }
 // Find next entering arc
 bool findEnteringArc() {
 Cost c, min = 0;
 int cnt = _block_size;
 int e, min_arc = _next_arc;
-for (e = _next_arc; e < _arc_num; ++e) {
+for (e = _next_arc; e < _search_arc_num; ++e) {
 c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
 if (c < min) {
 min = c;
 min_arc = e;
 }
 const IntVector  &_target;
 const CostVector &_cost;
 const IntVector  &_state;
 const CostVector &_pi;
 int &_in_arc;
-int _arc_num;
+int _search_arc_num;
 // Pivot rule data
 IntVector _candidates;
 int _list_length, _minor_limit;
 int _curr_length, _minor_count;
 /// Constructor
 CandidateListPivotRule(NetworkSimplex &ns) :
 _source(ns._source), _target(ns._target),
 _cost(ns._cost), _state(ns._state), _pi(ns._pi),
-_in_arc(ns.in_arc), _arc_num(ns._arc_num), _next_arc(0)
+_in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num),
+_next_arc(0)
 {
 // The main parameters of the pivot rule
 const double LIST_LENGTH_FACTOR = 1.0;
 const int MIN_LIST_LENGTH = 10;
 const double MINOR_LIMIT_FACTOR = 0.1;
 const int MIN_MINOR_LIMIT = 3;
 _list_length = std::max( int(LIST_LENGTH_FACTOR *
-std::sqrt(double(_arc_num))),
+std::sqrt(double(_search_arc_num))),
 MIN_LIST_LENGTH );
 _minor_limit = std::max( int(MINOR_LIMIT_FACTOR * _list_length),
 MIN_MINOR_LIMIT );
 _curr_length = _minor_count = 0;
 _candidates.resize(_list_length);
 }
 // Major iteration: build a new candidate list
 min = 0;
 _curr_length = 0;
-for (e = _next_arc; e < _arc_num; ++e) {
+for (e = _next_arc; e < _search_arc_num; ++e) {
 c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
 if (c < 0) {
 _candidates[_curr_length++] = e;
 if (c < min) {
 min = c;
 const IntVector  &_target;
 const CostVector &_cost;
 const IntVector  &_state;
 const CostVector &_pi;
 int &_in_arc;
-int _arc_num;
+int _search_arc_num;
 // Pivot rule data
 int _block_size, _head_length, _curr_length;
 int _next_arc;
 IntVector _candidates;
 // Constructor
 AlteringListPivotRule(NetworkSimplex &ns) :
 _source(ns._source), _target(ns._target),
 _cost(ns._cost), _state(ns._state), _pi(ns._pi),
-_in_arc(ns.in_arc), _arc_num(ns._arc_num),
+_in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num),
-_next_arc(0), _cand_cost(ns._arc_num), _sort_func(_cand_cost)
+_next_arc(0), _cand_cost(ns._search_arc_num), _sort_func(_cand_cost)
 {
 // The main parameters of the pivot rule
 const double BLOCK_SIZE_FACTOR = 1.5;
 const int MIN_BLOCK_SIZE = 10;
 const double HEAD_LENGTH_FACTOR = 0.1;
 const int MIN_HEAD_LENGTH = 3;
 _block_size = std::max( int(BLOCK_SIZE_FACTOR *
-std::sqrt(double(_arc_num))),
+std::sqrt(double(_search_arc_num))),
 MIN_BLOCK_SIZE );
 _head_length = std::max( int(HEAD_LENGTH_FACTOR * _block_size),
 MIN_HEAD_LENGTH );
 _candidates.resize(_head_length + _block_size);
 _curr_length = 0;
 // Extend the list
 int cnt = _block_size;
 int last_arc = 0;
 int limit = _head_length;
-for (int e = _next_arc; e < _arc_num; ++e) {
+for (int e = _next_arc; e < _search_arc_num; ++e) {
 _cand_cost[e] = _state[e] *
 (_cost[e] + _pi[_source[e]] - _pi[_target[e]]);
 if (_cand_cost[e] < 0) {
 _candidates[_curr_length++] = e;
 last_arc = e;
 // Resize vectors
 _node_num = countNodes(_graph);
 _arc_num = countArcs(_graph);
 int all_node_num = _node_num + 1;
-int all_arc_num = _arc_num + _node_num;
+int max_arc_num = _arc_num + 2 * _node_num;
-_source.resize(all_arc_num);
+_source.resize(max_arc_num);
-_target.resize(all_arc_num);
+_target.resize(max_arc_num);
-_lower.resize(all_arc_num);
+_lower.resize(_arc_num);
-_upper.resize(all_arc_num);
+_upper.resize(_arc_num);
-_cap.resize(all_arc_num);
+_cap.resize(max_arc_num);
-_cost.resize(all_arc_num);
+_cost.resize(max_arc_num);
 _supply.resize(all_node_num);
-_flow.resize(all_arc_num);
+_flow.resize(max_arc_num);
 _pi.resize(all_node_num);
 _parent.resize(all_node_num);
 _pred.resize(all_node_num);
 _forward.resize(all_node_num);
 _thread.resize(all_node_num);
 _rev_thread.resize(all_node_num);
 _succ_num.resize(all_node_num);
 _last_succ.resize(all_node_num);
-_state.resize(all_arc_num);
+_state.resize(max_arc_num);
 // Copy the graph (store the arcs in a mixed order)
 int i = 0;
 for (NodeIt n(_graph); n != INVALID; ++n, ++i) {
 _node_id[n] = i;
 }
 // Initialize artifical cost
 Cost ART_COST;
 if (std::numeric_limits<Cost>::is_exact) {
-ART_COST = std::numeric_limits<Cost>::max() / 4 + 1;
+ART_COST = std::numeric_limits<Cost>::max() / 2 + 1;
 } else {
 ART_COST = std::numeric_limits<Cost>::min();
 for (int i = 0; i != _arc_num; ++i) {
 if (_cost[i] > ART_COST) ART_COST = _cost[i];
 }
 _thread[_root] = 0;
 _rev_thread[0] = _root;
 _succ_num[_root] = _node_num + 1;
 _last_succ[_root] = _root - 1;
 _supply[_root] = -_sum_supply;
-_pi[_root] = _sum_supply < 0 ? -ART_COST : ART_COST;
+_pi[_root] = 0;
 // Add artificial arcs and initialize the spanning tree data structure
-for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) {
+if (_sum_supply == 0) {
-_parent[u] = _root;
+// EQ supply constraints
-_pred[u] = e;
+_search_arc_num = _arc_num;
-_thread[u] = u + 1;
+_all_arc_num = _arc_num + _node_num;
-_rev_thread[u + 1] = u;
+for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) {
-_succ_num[u] = 1;
+_parent[u] = _root;
-_last_succ[u] = u;
+_pred[u] = e;
-_cost[e] = ART_COST;
+_thread[u] = u + 1;
-_cap[e] = INF;
+_rev_thread[u + 1] = u;
-_state[e] = STATE_TREE;
+_succ_num[u] = 1;
-if (_supply[u] > 0 || (_supply[u] == 0 && _sum_supply <= 0)) {
+_last_succ[u] = u;
-_flow[e] = _supply[u];
+_cap[e] = INF;
-_forward[u] = true;
+_state[e] = STATE_TREE;
-_pi[u] = -ART_COST + _pi[_root];
+if (_supply[u] >= 0) {
-} else {
+_forward[u] = true;
-_flow[e] = -_supply[u];
+_pi[u] = 0;
-_forward[u] = false;
+_source[e] = u;
-_pi[u] = ART_COST + _pi[_root];
+_target[e] = _root;
-}
+_flow[e] = _supply[u];
+_cost[e] = 0;
+} else {
+_forward[u] = false;
+_pi[u] = ART_COST;
+_source[e] = _root;
+_target[e] = u;
+_flow[e] = -_supply[u];
+_cost[e] = ART_COST;
+}
+}
+}
+else if (_sum_supply > 0) {
+// LEQ supply constraints
+_search_arc_num = _arc_num + _node_num;
+int f = _arc_num + _node_num;
+for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) {
+_parent[u] = _root;
+_thread[u] = u + 1;
+_rev_thread[u + 1] = u;
+_succ_num[u] = 1;
+_last_succ[u] = u;
+if (_supply[u] >= 0) {
+_forward[u] = true;
+_pi[u] = 0;
+_pred[u] = e;
+_source[e] = u;
+_target[e] = _root;
+_cap[e] = INF;
+_flow[e] = _supply[u];
+_cost[e] = 0;
+_state[e] = STATE_TREE;
+} else {
+_forward[u] = false;
+_pi[u] = ART_COST;
+_pred[u] = f;
+_source[f] = _root;
+_target[f] = u;
+_cap[f] = INF;
+_flow[f] = -_supply[u];
+_cost[f] = ART_COST;
+_state[f] = STATE_TREE;
+_source[e] = u;
+_target[e] = _root;
+_cap[e] = INF;
+_flow[e] = 0;
+_cost[e] = 0;
+_state[e] = STATE_LOWER;
+++f;
+}
+}
+_all_arc_num = f;
+}
+else {
+// GEQ supply constraints
+_search_arc_num = _arc_num + _node_num;
+int f = _arc_num + _node_num;
+for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) {
+_parent[u] = _root;
+_thread[u] = u + 1;
+_rev_thread[u + 1] = u;
+_succ_num[u] = 1;
+_last_succ[u] = u;
+if (_supply[u] <= 0) {
+_forward[u] = false;
+_pi[u] = 0;
+_pred[u] = e;
+_source[e] = _root;
+_target[e] = u;
+_cap[e] = INF;
+_flow[e] = -_supply[u];
+_cost[e] = 0;
+_state[e] = STATE_TREE;
+} else {
+_forward[u] = true;
+_pi[u] = -ART_COST;
+_pred[u] = f;
+_source[f] = u;
+_target[f] = _root;
+_cap[f] = INF;
+_flow[f] = _supply[u];
+_state[f] = STATE_TREE;
+_cost[f] = ART_COST;
+_source[e] = _root;
+_target[e] = u;
+_cap[e] = INF;
+_flow[e] = 0;
+_cost[e] = 0;
+_state[e] = STATE_LOWER;
+++f;
+}
+}
+_all_arc_num = f;
 }
 return true;
 }
 updatePotential();
 }
 }
 // Check feasibility
-if (_sum_supply < 0) {
+for (int e = _search_arc_num; e != _all_arc_num; ++e) {
-for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) {
+if (_flow[e] != 0) return INFEASIBLE;
-if (_supply[u] >= 0 && _flow[e] != 0) return INFEASIBLE;
-}
-}
-else if (_sum_supply > 0) {
-for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) {
-if (_supply[u] <= 0 && _flow[e] != 0) return INFEASIBLE;
-}
-}
-else {
-for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) {
-if (_flow[e] != 0) return INFEASIBLE;
-}
 }
 // Transform the solution and the supply map to the original form
 if (_have_lower) {
 for (int i = 0; i != _arc_num; ++i) {
 _supply[_source[i]] += c;
 _supply[_target[i]] -= c;
 }
 }
 }
+// Shift potentials to meet the requirements of the GEQ/LEQ type
+// optimality conditions
+if (_sum_supply == 0) {
+if (_stype == GEQ) {
+Cost max_pot = std::numeric_limits<Cost>::min();
+for (int i = 0; i != _node_num; ++i) {
+if (_pi[i] > max_pot) max_pot = _pi[i];
+}
+if (max_pot > 0) {
+for (int i = 0; i != _node_num; ++i)
+_pi[i] -= max_pot;
+}
+} else {
+Cost min_pot = std::numeric_limits<Cost>::max();
+for (int i = 0; i != _node_num; ++i) {
+if (_pi[i] < min_pot) min_pot = _pi[i];
+}
+if (min_pot < 0) {
+for (int i = 0; i != _node_num; ++i)
+_pi[i] -= min_pot;
+}
+}
+}
 return OPTIMAL;
 }
 }; //class NetworkSimplex

changeset 713	4ac30454f1c1
parent 643	f3792d5bb294
child 727	cab85bd7859b
child 729	be48a648d28f
child 891	5205145fabf6