Changeset 840:2914b6f0fde0 in lemon-main for lemon/cost_scaling.h
- Timestamp:
- 02/26/10 14:00:20 (15 years ago)
- Branch:
- default
- Parents:
- 838:2c35bef44dd1 (diff), 839:f3bc4e9b5f3a (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent. - Phase:
- public
- Files:
-
- 2 edited
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lemon/cost_scaling.h
r831 r840 202 202 203 203 typedef std::vector<int> IntVector; 204 typedef std::vector<char> BoolVector;205 204 typedef std::vector<Value> ValueVector; 206 205 typedef std::vector<Cost> CostVector; 207 206 typedef std::vector<LargeCost> LargeCostVector; 207 typedef std::vector<char> BoolVector; 208 // Note: vector<char> is used instead of vector<bool> for efficiency reasons 208 209 209 210 private: … … 249 250 bool _have_lower; 250 251 Value _sum_supply; 252 int _sup_node_num; 251 253 252 254 // Data structures for storing the digraph … … 277 279 int _alpha; 278 280 281 IntVector _buckets; 282 IntVector _bucket_next; 283 IntVector _bucket_prev; 284 IntVector _rank; 285 int _max_rank; 286 279 287 // Data for a StaticDigraph structure 280 288 typedef std::pair<int, int> IntPair; … … 829 837 } 830 838 839 _sup_node_num = 0; 840 for (NodeIt n(_graph); n != INVALID; ++n) { 841 if (sup[n] > 0) ++_sup_node_num; 842 } 843 831 844 // Find a feasible flow using Circulation 832 845 Circulation<Digraph, ConstMap<Arc, Value>, ValueArcMap, ValueNodeMap> … … 863 876 for (int a = _first_out[_root]; a != _res_arc_num; ++a) { 864 877 int ra = _reverse[a]; 865 _res_cap[a] = 1;878 _res_cap[a] = 0; 866 879 _res_cap[ra] = 0; 867 880 _cost[a] = 0; … … 877 890 // Maximum path length for partial augment 878 891 const int MAX_PATH_LENGTH = 4; 879 892 893 // Initialize data structures for buckets 894 _max_rank = _alpha * _res_node_num; 895 _buckets.resize(_max_rank); 896 _bucket_next.resize(_res_node_num + 1); 897 _bucket_prev.resize(_res_node_num + 1); 898 _rank.resize(_res_node_num + 1); 899 880 900 // Execute the algorithm 881 901 switch (method) { … … 916 936 } 917 937 } 938 939 // Initialize a cost scaling phase 940 void initPhase() { 941 // Saturate arcs not satisfying the optimality condition 942 for (int u = 0; u != _res_node_num; ++u) { 943 int last_out = _first_out[u+1]; 944 LargeCost pi_u = _pi[u]; 945 for (int a = _first_out[u]; a != last_out; ++a) { 946 int v = _target[a]; 947 if (_res_cap[a] > 0 && _cost[a] + pi_u - _pi[v] < 0) { 948 Value delta = _res_cap[a]; 949 _excess[u] -= delta; 950 _excess[v] += delta; 951 _res_cap[a] = 0; 952 _res_cap[_reverse[a]] += delta; 953 } 954 } 955 } 956 957 // Find active nodes (i.e. nodes with positive excess) 958 for (int u = 0; u != _res_node_num; ++u) { 959 if (_excess[u] > 0) _active_nodes.push_back(u); 960 } 961 962 // Initialize the next arcs 963 for (int u = 0; u != _res_node_num; ++u) { 964 _next_out[u] = _first_out[u]; 965 } 966 } 967 968 // Early termination heuristic 969 bool earlyTermination() { 970 const double EARLY_TERM_FACTOR = 3.0; 971 972 // Build a static residual graph 973 _arc_vec.clear(); 974 _cost_vec.clear(); 975 for (int j = 0; j != _res_arc_num; ++j) { 976 if (_res_cap[j] > 0) { 977 _arc_vec.push_back(IntPair(_source[j], _target[j])); 978 _cost_vec.push_back(_cost[j] + 1); 979 } 980 } 981 _sgr.build(_res_node_num, _arc_vec.begin(), _arc_vec.end()); 982 983 // Run Bellman-Ford algorithm to check if the current flow is optimal 984 BellmanFord<StaticDigraph, LargeCostArcMap> bf(_sgr, _cost_map); 985 bf.init(0); 986 bool done = false; 987 int K = int(EARLY_TERM_FACTOR * std::sqrt(double(_res_node_num))); 988 for (int i = 0; i < K && !done; ++i) { 989 done = bf.processNextWeakRound(); 990 } 991 return done; 992 } 993 994 // Global potential update heuristic 995 void globalUpdate() { 996 int bucket_end = _root + 1; 997 998 // Initialize buckets 999 for (int r = 0; r != _max_rank; ++r) { 1000 _buckets[r] = bucket_end; 1001 } 1002 Value total_excess = 0; 1003 for (int i = 0; i != _res_node_num; ++i) { 1004 if (_excess[i] < 0) { 1005 _rank[i] = 0; 1006 _bucket_next[i] = _buckets[0]; 1007 _bucket_prev[_buckets[0]] = i; 1008 _buckets[0] = i; 1009 } else { 1010 total_excess += _excess[i]; 1011 _rank[i] = _max_rank; 1012 } 1013 } 1014 if (total_excess == 0) return; 1015 1016 // Search the buckets 1017 int r = 0; 1018 for ( ; r != _max_rank; ++r) { 1019 while (_buckets[r] != bucket_end) { 1020 // Remove the first node from the current bucket 1021 int u = _buckets[r]; 1022 _buckets[r] = _bucket_next[u]; 1023 1024 // Search the incomming arcs of u 1025 LargeCost pi_u = _pi[u]; 1026 int last_out = _first_out[u+1]; 1027 for (int a = _first_out[u]; a != last_out; ++a) { 1028 int ra = _reverse[a]; 1029 if (_res_cap[ra] > 0) { 1030 int v = _source[ra]; 1031 int old_rank_v = _rank[v]; 1032 if (r < old_rank_v) { 1033 // Compute the new rank of v 1034 LargeCost nrc = (_cost[ra] + _pi[v] - pi_u) / _epsilon; 1035 int new_rank_v = old_rank_v; 1036 if (nrc < LargeCost(_max_rank)) 1037 new_rank_v = r + 1 + int(nrc); 1038 1039 // Change the rank of v 1040 if (new_rank_v < old_rank_v) { 1041 _rank[v] = new_rank_v; 1042 _next_out[v] = _first_out[v]; 1043 1044 // Remove v from its old bucket 1045 if (old_rank_v < _max_rank) { 1046 if (_buckets[old_rank_v] == v) { 1047 _buckets[old_rank_v] = _bucket_next[v]; 1048 } else { 1049 _bucket_next[_bucket_prev[v]] = _bucket_next[v]; 1050 _bucket_prev[_bucket_next[v]] = _bucket_prev[v]; 1051 } 1052 } 1053 1054 // Insert v to its new bucket 1055 _bucket_next[v] = _buckets[new_rank_v]; 1056 _bucket_prev[_buckets[new_rank_v]] = v; 1057 _buckets[new_rank_v] = v; 1058 } 1059 } 1060 } 1061 } 1062 1063 // Finish search if there are no more active nodes 1064 if (_excess[u] > 0) { 1065 total_excess -= _excess[u]; 1066 if (total_excess <= 0) break; 1067 } 1068 } 1069 if (total_excess <= 0) break; 1070 } 1071 1072 // Relabel nodes 1073 for (int u = 0; u != _res_node_num; ++u) { 1074 int k = std::min(_rank[u], r); 1075 if (k > 0) { 1076 _pi[u] -= _epsilon * k; 1077 _next_out[u] = _first_out[u]; 1078 } 1079 } 1080 } 918 1081 919 1082 /// Execute the algorithm performing augment and relabel operations 920 1083 void startAugment(int max_length = std::numeric_limits<int>::max()) { 921 1084 // Paramters for heuristics 922 const int BF_HEURISTIC_EPSILON_BOUND = 1000; 923 const int BF_HEURISTIC_BOUND_FACTOR = 3; 924 1085 const int EARLY_TERM_EPSILON_LIMIT = 1000; 1086 const double GLOBAL_UPDATE_FACTOR = 3.0; 1087 1088 const int global_update_freq = int(GLOBAL_UPDATE_FACTOR * 1089 (_res_node_num + _sup_node_num * _sup_node_num)); 1090 int next_update_limit = global_update_freq; 1091 1092 int relabel_cnt = 0; 1093 925 1094 // Perform cost scaling phases 926 IntVector pred_arc(_res_node_num); 927 std::vector<int> path_nodes; 1095 std::vector<int> path; 928 1096 for ( ; _epsilon >= 1; _epsilon = _epsilon < _alpha && _epsilon > 1 ? 929 1097 1 : _epsilon / _alpha ) 930 1098 { 931 // "Early Termination" heuristic: use Bellman-Ford algorithm 932 // to check if the current flow is optimal 933 if (_epsilon <= BF_HEURISTIC_EPSILON_BOUND) { 934 _arc_vec.clear(); 935 _cost_vec.clear(); 936 for (int j = 0; j != _res_arc_num; ++j) { 937 if (_res_cap[j] > 0) { 938 _arc_vec.push_back(IntPair(_source[j], _target[j])); 939 _cost_vec.push_back(_cost[j] + 1); 940 } 941 } 942 _sgr.build(_res_node_num, _arc_vec.begin(), _arc_vec.end()); 943 944 BellmanFord<StaticDigraph, LargeCostArcMap> bf(_sgr, _cost_map); 945 bf.init(0); 946 bool done = false; 947 int K = int(BF_HEURISTIC_BOUND_FACTOR * sqrt(_res_node_num)); 948 for (int i = 0; i < K && !done; ++i) 949 done = bf.processNextWeakRound(); 950 if (done) break; 951 } 952 953 // Saturate arcs not satisfying the optimality condition 954 for (int a = 0; a != _res_arc_num; ++a) { 955 if (_res_cap[a] > 0 && 956 _cost[a] + _pi[_source[a]] - _pi[_target[a]] < 0) { 957 Value delta = _res_cap[a]; 958 _excess[_source[a]] -= delta; 959 _excess[_target[a]] += delta; 960 _res_cap[a] = 0; 961 _res_cap[_reverse[a]] += delta; 962 } 1099 // Early termination heuristic 1100 if (_epsilon <= EARLY_TERM_EPSILON_LIMIT) { 1101 if (earlyTermination()) break; 963 1102 } 964 1103 965 // Find active nodes (i.e. nodes with positive excess) 966 for (int u = 0; u != _res_node_num; ++u) { 967 if (_excess[u] > 0) _active_nodes.push_back(u); 968 } 969 970 // Initialize the next arcs 971 for (int u = 0; u != _res_node_num; ++u) { 972 _next_out[u] = _first_out[u]; 973 } 974 1104 // Initialize current phase 1105 initPhase(); 1106 975 1107 // Perform partial augment and relabel operations 976 1108 while (true) { … … 982 1114 if (_active_nodes.size() == 0) break; 983 1115 int start = _active_nodes.front(); 984 path_nodes.clear();985 path_nodes.push_back(start);986 1116 987 1117 // Find an augmenting path from the start node 1118 path.clear(); 988 1119 int tip = start; 989 while (_excess[tip] >= 0 && 990 int(path_nodes.size()) <= max_length) { 1120 while (_excess[tip] >= 0 && int(path.size()) < max_length) { 991 1121 int u; 992 LargeCost min_red_cost, rc; 993 int last_out = _sum_supply < 0 ? 994 _first_out[tip+1] : _first_out[tip+1] - 1; 1122 LargeCost min_red_cost, rc, pi_tip = _pi[tip]; 1123 int last_out = _first_out[tip+1]; 995 1124 for (int a = _next_out[tip]; a != last_out; ++a) { 996 if (_res_cap[a] > 0 && 997 _cost[a] + _pi[_source[a]] - _pi[_target[a]] < 0) { 998 u = _target[a]; 999 pred_arc[u] = a; 1125 u = _target[a]; 1126 if (_res_cap[a] > 0 && _cost[a] + pi_tip - _pi[u] < 0) { 1127 path.push_back(a); 1000 1128 _next_out[tip] = a; 1001 1129 tip = u; 1002 path_nodes.push_back(tip);1003 1130 goto next_step; 1004 1131 } … … 1006 1133 1007 1134 // Relabel tip node 1008 min_red_cost = std::numeric_limits<LargeCost>::max() / 2; 1135 min_red_cost = std::numeric_limits<LargeCost>::max(); 1136 if (tip != start) { 1137 int ra = _reverse[path.back()]; 1138 min_red_cost = _cost[ra] + pi_tip - _pi[_target[ra]]; 1139 } 1009 1140 for (int a = _first_out[tip]; a != last_out; ++a) { 1010 rc = _cost[a] + _pi[_source[a]]- _pi[_target[a]];1141 rc = _cost[a] + pi_tip - _pi[_target[a]]; 1011 1142 if (_res_cap[a] > 0 && rc < min_red_cost) { 1012 1143 min_red_cost = rc; … … 1014 1145 } 1015 1146 _pi[tip] -= min_red_cost + _epsilon; 1016 1017 // Reset the next arc of tip1018 1147 _next_out[tip] = _first_out[tip]; 1148 ++relabel_cnt; 1019 1149 1020 1150 // Step back 1021 1151 if (tip != start) { 1022 path_nodes.pop_back();1023 tip = path_nodes.back();1152 tip = _source[path.back()]; 1153 path.pop_back(); 1024 1154 } 1025 1155 … … 1029 1159 // Augment along the found path (as much flow as possible) 1030 1160 Value delta; 1031 int u, v = path_nodes.front(), pa; 1032 for (int i = 1; i < int(path_nodes.size()); ++i) { 1161 int pa, u, v = start; 1162 for (int i = 0; i != int(path.size()); ++i) { 1163 pa = path[i]; 1033 1164 u = v; 1034 v = path_nodes[i]; 1035 pa = pred_arc[v]; 1165 v = _target[pa]; 1036 1166 delta = std::min(_res_cap[pa], _excess[u]); 1037 1167 _res_cap[pa] -= delta; … … 1042 1172 _active_nodes.push_back(v); 1043 1173 } 1174 1175 // Global update heuristic 1176 if (relabel_cnt >= next_update_limit) { 1177 globalUpdate(); 1178 next_update_limit += global_update_freq; 1179 } 1044 1180 } 1045 1181 } … … 1049 1185 void startPush() { 1050 1186 // Paramters for heuristics 1051 const int BF_HEURISTIC_EPSILON_BOUND = 1000; 1052 const int BF_HEURISTIC_BOUND_FACTOR = 3; 1053 1187 const int EARLY_TERM_EPSILON_LIMIT = 1000; 1188 const double GLOBAL_UPDATE_FACTOR = 2.0; 1189 1190 const int global_update_freq = int(GLOBAL_UPDATE_FACTOR * 1191 (_res_node_num + _sup_node_num * _sup_node_num)); 1192 int next_update_limit = global_update_freq; 1193 1194 int relabel_cnt = 0; 1195 1054 1196 // Perform cost scaling phases 1055 1197 BoolVector hyper(_res_node_num, false); 1198 LargeCostVector hyper_cost(_res_node_num); 1056 1199 for ( ; _epsilon >= 1; _epsilon = _epsilon < _alpha && _epsilon > 1 ? 1057 1200 1 : _epsilon / _alpha ) 1058 1201 { 1059 // "Early Termination" heuristic: use Bellman-Ford algorithm 1060 // to check if the current flow is optimal 1061 if (_epsilon <= BF_HEURISTIC_EPSILON_BOUND) { 1062 _arc_vec.clear(); 1063 _cost_vec.clear(); 1064 for (int j = 0; j != _res_arc_num; ++j) { 1065 if (_res_cap[j] > 0) { 1066 _arc_vec.push_back(IntPair(_source[j], _target[j])); 1067 _cost_vec.push_back(_cost[j] + 1); 1068 } 1069 } 1070 _sgr.build(_res_node_num, _arc_vec.begin(), _arc_vec.end()); 1071 1072 BellmanFord<StaticDigraph, LargeCostArcMap> bf(_sgr, _cost_map); 1073 bf.init(0); 1074 bool done = false; 1075 int K = int(BF_HEURISTIC_BOUND_FACTOR * sqrt(_res_node_num)); 1076 for (int i = 0; i < K && !done; ++i) 1077 done = bf.processNextWeakRound(); 1078 if (done) break; 1079 } 1080 1081 // Saturate arcs not satisfying the optimality condition 1082 for (int a = 0; a != _res_arc_num; ++a) { 1083 if (_res_cap[a] > 0 && 1084 _cost[a] + _pi[_source[a]] - _pi[_target[a]] < 0) { 1085 Value delta = _res_cap[a]; 1086 _excess[_source[a]] -= delta; 1087 _excess[_target[a]] += delta; 1088 _res_cap[a] = 0; 1089 _res_cap[_reverse[a]] += delta; 1090 } 1091 } 1092 1093 // Find active nodes (i.e. nodes with positive excess) 1094 for (int u = 0; u != _res_node_num; ++u) { 1095 if (_excess[u] > 0) _active_nodes.push_back(u); 1096 } 1097 1098 // Initialize the next arcs 1099 for (int u = 0; u != _res_node_num; ++u) { 1100 _next_out[u] = _first_out[u]; 1101 } 1202 // Early termination heuristic 1203 if (_epsilon <= EARLY_TERM_EPSILON_LIMIT) { 1204 if (earlyTermination()) break; 1205 } 1206 1207 // Initialize current phase 1208 initPhase(); 1102 1209 1103 1210 // Perform push and relabel operations 1104 1211 while (_active_nodes.size() > 0) { 1105 LargeCost min_red_cost, rc ;1212 LargeCost min_red_cost, rc, pi_n; 1106 1213 Value delta; 1107 1214 int n, t, a, last_out = _res_arc_num; 1108 1215 1216 next_node: 1109 1217 // Select an active node (FIFO selection) 1110 next_node:1111 1218 n = _active_nodes.front(); 1112 last_out = _ sum_supply < 0 ?1113 _first_out[n+1] : _first_out[n+1] - 1;1114 1219 last_out = _first_out[n+1]; 1220 pi_n = _pi[n]; 1221 1115 1222 // Perform push operations if there are admissible arcs 1116 1223 if (_excess[n] > 0) { 1117 1224 for (a = _next_out[n]; a != last_out; ++a) { 1118 1225 if (_res_cap[a] > 0 && 1119 _cost[a] + _pi[_source[a]]- _pi[_target[a]] < 0) {1226 _cost[a] + pi_n - _pi[_target[a]] < 0) { 1120 1227 delta = std::min(_res_cap[a], _excess[n]); 1121 1228 t = _target[a]; … … 1123 1230 // Push-look-ahead heuristic 1124 1231 Value ahead = -_excess[t]; 1125 int last_out_t = _ sum_supply < 0 ?1126 _first_out[t+1] : _first_out[t+1] - 1;1232 int last_out_t = _first_out[t+1]; 1233 LargeCost pi_t = _pi[t]; 1127 1234 for (int ta = _next_out[t]; ta != last_out_t; ++ta) { 1128 1235 if (_res_cap[ta] > 0 && 1129 _cost[ta] + _pi[_source[ta]]- _pi[_target[ta]] < 0)1236 _cost[ta] + pi_t - _pi[_target[ta]] < 0) 1130 1237 ahead += _res_cap[ta]; 1131 1238 if (ahead >= delta) break; … … 1134 1241 1135 1242 // Push flow along the arc 1136 if (ahead < delta ) {1243 if (ahead < delta && !hyper[t]) { 1137 1244 _res_cap[a] -= ahead; 1138 1245 _res_cap[_reverse[a]] += ahead; … … 1141 1248 _active_nodes.push_front(t); 1142 1249 hyper[t] = true; 1250 hyper_cost[t] = _cost[a] + pi_n - pi_t; 1143 1251 _next_out[n] = a; 1144 1252 goto next_node; … … 1163 1271 // Relabel the node if it is still active (or hyper) 1164 1272 if (_excess[n] > 0 || hyper[n]) { 1165 min_red_cost = std::numeric_limits<LargeCost>::max() / 2; 1273 min_red_cost = hyper[n] ? -hyper_cost[n] : 1274 std::numeric_limits<LargeCost>::max(); 1166 1275 for (int a = _first_out[n]; a != last_out; ++a) { 1167 rc = _cost[a] + _pi[_source[a]]- _pi[_target[a]];1276 rc = _cost[a] + pi_n - _pi[_target[a]]; 1168 1277 if (_res_cap[a] > 0 && rc < min_red_cost) { 1169 1278 min_red_cost = rc; … … 1171 1280 } 1172 1281 _pi[n] -= min_red_cost + _epsilon; 1282 _next_out[n] = _first_out[n]; 1173 1283 hyper[n] = false; 1174 1175 // Reset the next arc 1176 _next_out[n] = _first_out[n]; 1284 ++relabel_cnt; 1177 1285 } 1178 1286 … … 1184 1292 _active_nodes.pop_front(); 1185 1293 } 1294 1295 // Global update heuristic 1296 if (relabel_cnt >= next_update_limit) { 1297 globalUpdate(); 1298 for (int u = 0; u != _res_node_num; ++u) 1299 hyper[u] = false; 1300 next_update_limit += global_update_freq; 1301 } 1186 1302 } 1187 1303 } -
lemon/cost_scaling.h
r839 r840 105 105 /// \tparam GR The digraph type the algorithm runs on. 106 106 /// \tparam V The number type used for flow amounts, capacity bounds 107 /// and supply values in the algorithm. By default it is \c int.107 /// and supply values in the algorithm. By default, it is \c int. 108 108 /// \tparam C The number type used for costs and potentials in the 109 /// algorithm. By default it is the same as \c V. 109 /// algorithm. By default, it is the same as \c V. 110 /// \tparam TR The traits class that defines various types used by the 111 /// algorithm. By default, it is \ref CostScalingDefaultTraits 112 /// "CostScalingDefaultTraits<GR, V, C>". 113 /// In most cases, this parameter should not be set directly, 114 /// consider to use the named template parameters instead. 110 115 /// 111 116 /// \warning Both number types must be signed and all input data must … … 137 142 /// 138 143 /// The large cost type used for internal computations. 139 /// Using the \ref CostScalingDefaultTraits "default traits class", 140 /// it is \c long \c long if the \c Cost type is integer, 144 /// By default, it is \c long \c long if the \c Cost type is integer, 141 145 /// otherwise it is \c double. 142 146 typedef typename TR::LargeCost LargeCost; … … 341 345 LEMON_ASSERT(std::numeric_limits<Cost>::is_signed, 342 346 "The cost type of CostScaling must be signed"); 343 347 348 // Reset data structures 349 reset(); 350 } 351 352 /// \name Parameters 353 /// The parameters of the algorithm can be specified using these 354 /// functions. 355 356 /// @{ 357 358 /// \brief Set the lower bounds on the arcs. 359 /// 360 /// This function sets the lower bounds on the arcs. 361 /// If it is not used before calling \ref run(), the lower bounds 362 /// will be set to zero on all arcs. 363 /// 364 /// \param map An arc map storing the lower bounds. 365 /// Its \c Value type must be convertible to the \c Value type 366 /// of the algorithm. 367 /// 368 /// \return <tt>(*this)</tt> 369 template <typename LowerMap> 370 CostScaling& lowerMap(const LowerMap& map) { 371 _have_lower = true; 372 for (ArcIt a(_graph); a != INVALID; ++a) { 373 _lower[_arc_idf[a]] = map[a]; 374 _lower[_arc_idb[a]] = map[a]; 375 } 376 return *this; 377 } 378 379 /// \brief Set the upper bounds (capacities) on the arcs. 380 /// 381 /// This function sets the upper bounds (capacities) on the arcs. 382 /// If it is not used before calling \ref run(), the upper bounds 383 /// will be set to \ref INF on all arcs (i.e. the flow value will be 384 /// unbounded from above). 385 /// 386 /// \param map An arc map storing the upper bounds. 387 /// Its \c Value type must be convertible to the \c Value type 388 /// of the algorithm. 389 /// 390 /// \return <tt>(*this)</tt> 391 template<typename UpperMap> 392 CostScaling& upperMap(const UpperMap& map) { 393 for (ArcIt a(_graph); a != INVALID; ++a) { 394 _upper[_arc_idf[a]] = map[a]; 395 } 396 return *this; 397 } 398 399 /// \brief Set the costs of the arcs. 400 /// 401 /// This function sets the costs of the arcs. 402 /// If it is not used before calling \ref run(), the costs 403 /// will be set to \c 1 on all arcs. 404 /// 405 /// \param map An arc map storing the costs. 406 /// Its \c Value type must be convertible to the \c Cost type 407 /// of the algorithm. 408 /// 409 /// \return <tt>(*this)</tt> 410 template<typename CostMap> 411 CostScaling& costMap(const CostMap& map) { 412 for (ArcIt a(_graph); a != INVALID; ++a) { 413 _scost[_arc_idf[a]] = map[a]; 414 _scost[_arc_idb[a]] = -map[a]; 415 } 416 return *this; 417 } 418 419 /// \brief Set the supply values of the nodes. 420 /// 421 /// This function sets the supply values of the nodes. 422 /// If neither this function nor \ref stSupply() is used before 423 /// calling \ref run(), the supply of each node will be set to zero. 424 /// 425 /// \param map A node map storing the supply values. 426 /// Its \c Value type must be convertible to the \c Value type 427 /// of the algorithm. 428 /// 429 /// \return <tt>(*this)</tt> 430 template<typename SupplyMap> 431 CostScaling& supplyMap(const SupplyMap& map) { 432 for (NodeIt n(_graph); n != INVALID; ++n) { 433 _supply[_node_id[n]] = map[n]; 434 } 435 return *this; 436 } 437 438 /// \brief Set single source and target nodes and a supply value. 439 /// 440 /// This function sets a single source node and a single target node 441 /// and the required flow value. 442 /// If neither this function nor \ref supplyMap() is used before 443 /// calling \ref run(), the supply of each node will be set to zero. 444 /// 445 /// Using this function has the same effect as using \ref supplyMap() 446 /// with such a map in which \c k is assigned to \c s, \c -k is 447 /// assigned to \c t and all other nodes have zero supply value. 448 /// 449 /// \param s The source node. 450 /// \param t The target node. 451 /// \param k The required amount of flow from node \c s to node \c t 452 /// (i.e. the supply of \c s and the demand of \c t). 453 /// 454 /// \return <tt>(*this)</tt> 455 CostScaling& stSupply(const Node& s, const Node& t, Value k) { 456 for (int i = 0; i != _res_node_num; ++i) { 457 _supply[i] = 0; 458 } 459 _supply[_node_id[s]] = k; 460 _supply[_node_id[t]] = -k; 461 return *this; 462 } 463 464 /// @} 465 466 /// \name Execution control 467 /// The algorithm can be executed using \ref run(). 468 469 /// @{ 470 471 /// \brief Run the algorithm. 472 /// 473 /// This function runs the algorithm. 474 /// The paramters can be specified using functions \ref lowerMap(), 475 /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(). 476 /// For example, 477 /// \code 478 /// CostScaling<ListDigraph> cs(graph); 479 /// cs.lowerMap(lower).upperMap(upper).costMap(cost) 480 /// .supplyMap(sup).run(); 481 /// \endcode 482 /// 483 /// This function can be called more than once. All the given parameters 484 /// are kept for the next call, unless \ref resetParams() or \ref reset() 485 /// is used, thus only the modified parameters have to be set again. 486 /// If the underlying digraph was also modified after the construction 487 /// of the class (or the last \ref reset() call), then the \ref reset() 488 /// function must be called. 489 /// 490 /// \param method The internal method that will be used in the 491 /// algorithm. For more information, see \ref Method. 492 /// \param factor The cost scaling factor. It must be larger than one. 493 /// 494 /// \return \c INFEASIBLE if no feasible flow exists, 495 /// \n \c OPTIMAL if the problem has optimal solution 496 /// (i.e. it is feasible and bounded), and the algorithm has found 497 /// optimal flow and node potentials (primal and dual solutions), 498 /// \n \c UNBOUNDED if the digraph contains an arc of negative cost 499 /// and infinite upper bound. It means that the objective function 500 /// is unbounded on that arc, however, note that it could actually be 501 /// bounded over the feasible flows, but this algroithm cannot handle 502 /// these cases. 503 /// 504 /// \see ProblemType, Method 505 /// \see resetParams(), reset() 506 ProblemType run(Method method = PARTIAL_AUGMENT, int factor = 8) { 507 _alpha = factor; 508 ProblemType pt = init(); 509 if (pt != OPTIMAL) return pt; 510 start(method); 511 return OPTIMAL; 512 } 513 514 /// \brief Reset all the parameters that have been given before. 515 /// 516 /// This function resets all the paramaters that have been given 517 /// before using functions \ref lowerMap(), \ref upperMap(), 518 /// \ref costMap(), \ref supplyMap(), \ref stSupply(). 519 /// 520 /// It is useful for multiple \ref run() calls. Basically, all the given 521 /// parameters are kept for the next \ref run() call, unless 522 /// \ref resetParams() or \ref reset() is used. 523 /// If the underlying digraph was also modified after the construction 524 /// of the class or the last \ref reset() call, then the \ref reset() 525 /// function must be used, otherwise \ref resetParams() is sufficient. 526 /// 527 /// For example, 528 /// \code 529 /// CostScaling<ListDigraph> cs(graph); 530 /// 531 /// // First run 532 /// cs.lowerMap(lower).upperMap(upper).costMap(cost) 533 /// .supplyMap(sup).run(); 534 /// 535 /// // Run again with modified cost map (resetParams() is not called, 536 /// // so only the cost map have to be set again) 537 /// cost[e] += 100; 538 /// cs.costMap(cost).run(); 539 /// 540 /// // Run again from scratch using resetParams() 541 /// // (the lower bounds will be set to zero on all arcs) 542 /// cs.resetParams(); 543 /// cs.upperMap(capacity).costMap(cost) 544 /// .supplyMap(sup).run(); 545 /// \endcode 546 /// 547 /// \return <tt>(*this)</tt> 548 /// 549 /// \see reset(), run() 550 CostScaling& resetParams() { 551 for (int i = 0; i != _res_node_num; ++i) { 552 _supply[i] = 0; 553 } 554 int limit = _first_out[_root]; 555 for (int j = 0; j != limit; ++j) { 556 _lower[j] = 0; 557 _upper[j] = INF; 558 _scost[j] = _forward[j] ? 1 : -1; 559 } 560 for (int j = limit; j != _res_arc_num; ++j) { 561 _lower[j] = 0; 562 _upper[j] = INF; 563 _scost[j] = 0; 564 _scost[_reverse[j]] = 0; 565 } 566 _have_lower = false; 567 return *this; 568 } 569 570 /// \brief Reset all the parameters that have been given before. 571 /// 572 /// This function resets all the paramaters that have been given 573 /// before using functions \ref lowerMap(), \ref upperMap(), 574 /// \ref costMap(), \ref supplyMap(), \ref stSupply(). 575 /// 576 /// It is useful for multiple run() calls. If this function is not 577 /// used, all the parameters given before are kept for the next 578 /// \ref run() call. 579 /// However, the underlying digraph must not be modified after this 580 /// class have been constructed, since it copies and extends the graph. 581 /// \return <tt>(*this)</tt> 582 CostScaling& reset() { 344 583 // Resize vectors 345 584 _node_num = countNodes(_graph); … … 409 648 410 649 // Reset parameters 411 reset(); 412 } 413 414 /// \name Parameters 415 /// The parameters of the algorithm can be specified using these 416 /// functions. 417 418 /// @{ 419 420 /// \brief Set the lower bounds on the arcs. 421 /// 422 /// This function sets the lower bounds on the arcs. 423 /// If it is not used before calling \ref run(), the lower bounds 424 /// will be set to zero on all arcs. 425 /// 426 /// \param map An arc map storing the lower bounds. 427 /// Its \c Value type must be convertible to the \c Value type 428 /// of the algorithm. 429 /// 430 /// \return <tt>(*this)</tt> 431 template <typename LowerMap> 432 CostScaling& lowerMap(const LowerMap& map) { 433 _have_lower = true; 434 for (ArcIt a(_graph); a != INVALID; ++a) { 435 _lower[_arc_idf[a]] = map[a]; 436 _lower[_arc_idb[a]] = map[a]; 437 } 438 return *this; 439 } 440 441 /// \brief Set the upper bounds (capacities) on the arcs. 442 /// 443 /// This function sets the upper bounds (capacities) on the arcs. 444 /// If it is not used before calling \ref run(), the upper bounds 445 /// will be set to \ref INF on all arcs (i.e. the flow value will be 446 /// unbounded from above). 447 /// 448 /// \param map An arc map storing the upper bounds. 449 /// Its \c Value type must be convertible to the \c Value type 450 /// of the algorithm. 451 /// 452 /// \return <tt>(*this)</tt> 453 template<typename UpperMap> 454 CostScaling& upperMap(const UpperMap& map) { 455 for (ArcIt a(_graph); a != INVALID; ++a) { 456 _upper[_arc_idf[a]] = map[a]; 457 } 458 return *this; 459 } 460 461 /// \brief Set the costs of the arcs. 462 /// 463 /// This function sets the costs of the arcs. 464 /// If it is not used before calling \ref run(), the costs 465 /// will be set to \c 1 on all arcs. 466 /// 467 /// \param map An arc map storing the costs. 468 /// Its \c Value type must be convertible to the \c Cost type 469 /// of the algorithm. 470 /// 471 /// \return <tt>(*this)</tt> 472 template<typename CostMap> 473 CostScaling& costMap(const CostMap& map) { 474 for (ArcIt a(_graph); a != INVALID; ++a) { 475 _scost[_arc_idf[a]] = map[a]; 476 _scost[_arc_idb[a]] = -map[a]; 477 } 478 return *this; 479 } 480 481 /// \brief Set the supply values of the nodes. 482 /// 483 /// This function sets the supply values of the nodes. 484 /// If neither this function nor \ref stSupply() is used before 485 /// calling \ref run(), the supply of each node will be set to zero. 486 /// 487 /// \param map A node map storing the supply values. 488 /// Its \c Value type must be convertible to the \c Value type 489 /// of the algorithm. 490 /// 491 /// \return <tt>(*this)</tt> 492 template<typename SupplyMap> 493 CostScaling& supplyMap(const SupplyMap& map) { 494 for (NodeIt n(_graph); n != INVALID; ++n) { 495 _supply[_node_id[n]] = map[n]; 496 } 497 return *this; 498 } 499 500 /// \brief Set single source and target nodes and a supply value. 501 /// 502 /// This function sets a single source node and a single target node 503 /// and the required flow value. 504 /// If neither this function nor \ref supplyMap() is used before 505 /// calling \ref run(), the supply of each node will be set to zero. 506 /// 507 /// Using this function has the same effect as using \ref supplyMap() 508 /// with such a map in which \c k is assigned to \c s, \c -k is 509 /// assigned to \c t and all other nodes have zero supply value. 510 /// 511 /// \param s The source node. 512 /// \param t The target node. 513 /// \param k The required amount of flow from node \c s to node \c t 514 /// (i.e. the supply of \c s and the demand of \c t). 515 /// 516 /// \return <tt>(*this)</tt> 517 CostScaling& stSupply(const Node& s, const Node& t, Value k) { 518 for (int i = 0; i != _res_node_num; ++i) { 519 _supply[i] = 0; 520 } 521 _supply[_node_id[s]] = k; 522 _supply[_node_id[t]] = -k; 523 return *this; 524 } 525 526 /// @} 527 528 /// \name Execution control 529 /// The algorithm can be executed using \ref run(). 530 531 /// @{ 532 533 /// \brief Run the algorithm. 534 /// 535 /// This function runs the algorithm. 536 /// The paramters can be specified using functions \ref lowerMap(), 537 /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(). 538 /// For example, 539 /// \code 540 /// CostScaling<ListDigraph> cs(graph); 541 /// cs.lowerMap(lower).upperMap(upper).costMap(cost) 542 /// .supplyMap(sup).run(); 543 /// \endcode 544 /// 545 /// This function can be called more than once. All the parameters 546 /// that have been given are kept for the next call, unless 547 /// \ref reset() is called, thus only the modified parameters 548 /// have to be set again. See \ref reset() for examples. 549 /// However, the underlying digraph must not be modified after this 550 /// class have been constructed, since it copies and extends the graph. 551 /// 552 /// \param method The internal method that will be used in the 553 /// algorithm. For more information, see \ref Method. 554 /// \param factor The cost scaling factor. It must be larger than one. 555 /// 556 /// \return \c INFEASIBLE if no feasible flow exists, 557 /// \n \c OPTIMAL if the problem has optimal solution 558 /// (i.e. it is feasible and bounded), and the algorithm has found 559 /// optimal flow and node potentials (primal and dual solutions), 560 /// \n \c UNBOUNDED if the digraph contains an arc of negative cost 561 /// and infinite upper bound. It means that the objective function 562 /// is unbounded on that arc, however, note that it could actually be 563 /// bounded over the feasible flows, but this algroithm cannot handle 564 /// these cases. 565 /// 566 /// \see ProblemType, Method 567 ProblemType run(Method method = PARTIAL_AUGMENT, int factor = 8) { 568 _alpha = factor; 569 ProblemType pt = init(); 570 if (pt != OPTIMAL) return pt; 571 start(method); 572 return OPTIMAL; 573 } 574 575 /// \brief Reset all the parameters that have been given before. 576 /// 577 /// This function resets all the paramaters that have been given 578 /// before using functions \ref lowerMap(), \ref upperMap(), 579 /// \ref costMap(), \ref supplyMap(), \ref stSupply(). 580 /// 581 /// It is useful for multiple run() calls. If this function is not 582 /// used, all the parameters given before are kept for the next 583 /// \ref run() call. 584 /// However, the underlying digraph must not be modified after this 585 /// class have been constructed, since it copies and extends the graph. 586 /// 587 /// For example, 588 /// \code 589 /// CostScaling<ListDigraph> cs(graph); 590 /// 591 /// // First run 592 /// cs.lowerMap(lower).upperMap(upper).costMap(cost) 593 /// .supplyMap(sup).run(); 594 /// 595 /// // Run again with modified cost map (reset() is not called, 596 /// // so only the cost map have to be set again) 597 /// cost[e] += 100; 598 /// cs.costMap(cost).run(); 599 /// 600 /// // Run again from scratch using reset() 601 /// // (the lower bounds will be set to zero on all arcs) 602 /// cs.reset(); 603 /// cs.upperMap(capacity).costMap(cost) 604 /// .supplyMap(sup).run(); 605 /// \endcode 606 /// 607 /// \return <tt>(*this)</tt> 608 CostScaling& reset() { 609 for (int i = 0; i != _res_node_num; ++i) { 610 _supply[i] = 0; 611 } 612 int limit = _first_out[_root]; 613 for (int j = 0; j != limit; ++j) { 614 _lower[j] = 0; 615 _upper[j] = INF; 616 _scost[j] = _forward[j] ? 1 : -1; 617 } 618 for (int j = limit; j != _res_arc_num; ++j) { 619 _lower[j] = 0; 620 _upper[j] = INF; 621 _scost[j] = 0; 622 _scost[_reverse[j]] = 0; 623 } 624 _have_lower = false; 650 resetParams(); 625 651 return *this; 626 652 }
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