Changes in / [915:c2ff0a365245:916:81f7e910060b] in lemon
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lemon/capacity_scaling.h
r899 r911 140 140 141 141 typedef std::vector<int> IntVector; 142 typedef std::vector<char> BoolVector;143 142 typedef std::vector<Value> ValueVector; 144 143 typedef std::vector<Cost> CostVector; 144 typedef std::vector<char> BoolVector; 145 // Note: vector<char> is used instead of vector<bool> for efficiency reasons 145 146 146 147 private: … … 799 800 if (_factor > 1) { 800 801 // With scaling 801 Value max_sup = 0, max_dem = 0 ;802 for (int i = 0; i != _ node_num; ++i) {802 Value max_sup = 0, max_dem = 0, max_cap = 0; 803 for (int i = 0; i != _root; ++i) { 803 804 Value ex = _excess[i]; 804 805 if ( ex > max_sup) max_sup = ex; 805 806 if (-ex > max_dem) max_dem = -ex; 806 }807 Value max_cap = 0;808 for (int j = 0; j != _res_arc_num; ++j) {809 if (_res_cap[j] > max_cap) max_cap = _res_cap[j];807 int last_out = _first_out[i+1] - 1; 808 for (int j = _first_out[i]; j != last_out; ++j) { 809 if (_res_cap[j] > max_cap) max_cap = _res_cap[j]; 810 } 810 811 } 811 812 max_sup = std::min(std::min(max_sup, max_dem), max_cap); -
lemon/cost_scaling.h
r899 r911 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/cycle_canceling.h
r898 r911 145 145 146 146 typedef std::vector<int> IntVector; 147 typedef std::vector<char> CharVector;148 147 typedef std::vector<double> DoubleVector; 149 148 typedef std::vector<Value> ValueVector; 150 149 typedef std::vector<Cost> CostVector; 150 typedef std::vector<char> BoolVector; 151 // Note: vector<char> is used instead of vector<bool> for efficiency reasons 151 152 152 153 private: … … 199 200 IntArcMap _arc_idb; 200 201 IntVector _first_out; 201 CharVector _forward;202 BoolVector _forward; 202 203 IntVector _source; 203 204 IntVector _target; … … 963 964 DoubleVector pi(_res_node_num, 0.0); 964 965 IntVector level(_res_node_num); 965 CharVector reached(_res_node_num);966 CharVector processed(_res_node_num);966 BoolVector reached(_res_node_num); 967 BoolVector processed(_res_node_num); 967 968 IntVector pred_node(_res_node_num); 968 969 IntVector pred_arc(_res_node_num); -
lemon/glpk.h
r793 r902 26 26 #include <lemon/lp_base.h> 27 27 28 // forward declaration29 #if !defined _GLP_PROB && !defined GLP_PROB30 #define _GLP_PROB31 #define GLP_PROB32 typedef struct { double _opaque_prob; } glp_prob;33 /* LP/MIP problem object */34 #endif35 36 28 namespace lemon { 37 29 30 namespace _solver_bits { 31 class VoidPtr { 32 private: 33 void *_ptr; 34 public: 35 VoidPtr() : _ptr(0) {} 36 37 template <typename T> 38 VoidPtr(T* ptr) : _ptr(reinterpret_cast<void*>(ptr)) {} 39 40 template <typename T> 41 VoidPtr& operator=(T* ptr) { 42 _ptr = reinterpret_cast<void*>(ptr); 43 return *this; 44 } 45 46 template <typename T> 47 operator T*() const { return reinterpret_cast<T*>(_ptr); } 48 }; 49 } 38 50 39 51 /// \brief Base interface for the GLPK LP and MIP solver … … 44 56 protected: 45 57 46 typedef glp_prob LPX; 47 glp_prob* lp; 58 _solver_bits::VoidPtr lp; 48 59 49 60 GlpkBase(); … … 124 135 125 136 ///Pointer to the underlying GLPK data structure. 126 LPX *lpx() {return lp;}137 _solver_bits::VoidPtr lpx() {return lp;} 127 138 ///Const pointer to the underlying GLPK data structure. 128 const LPX *lpx() const {return lp;}139 _solver_bits::VoidPtr lpx() const {return lp;} 129 140 130 141 ///Returns the constraint identifier understood by GLPK. -
lemon/graph_to_eps.h
r833 r909 685 685 #else 686 686 os << bits::getWinFormattedDate(); 687 os << std::endl; 687 688 #endif 688 689 } 689 os << std::endl;690 690 691 691 if (_autoArcWidthScale) { -
lemon/hartmann_orlin.h
r891 r914 406 406 /// \pre \ref run() or \ref findMinMean() must be called before 407 407 /// using this function. 408 LargeValue cycleLength() const {409 return _best_length;408 Value cycleLength() const { 409 return static_cast<Value>(_best_length); 410 410 } 411 411 -
lemon/howard.h
r891 r914 385 385 /// \pre \ref run() or \ref findMinMean() must be called before 386 386 /// using this function. 387 LargeValue cycleLength() const {388 return _best_length;387 Value cycleLength() const { 388 return static_cast<Value>(_best_length); 389 389 } 390 390 -
lemon/karp.h
r891 r914 393 393 /// \pre \ref run() or \ref findMinMean() must be called before 394 394 /// using this function. 395 LargeValue cycleLength() const {396 return _cycle_length;395 Value cycleLength() const { 396 return static_cast<Value>(_cycle_length); 397 397 } 398 398 -
lemon/lp_base.h
r833 r903 1230 1230 Row r; 1231 1231 c.expr().simplify(); 1232 r._id = _addRowId(_addRow(c.lowerBounded()?c.lowerBound() :-INF,1232 r._id = _addRowId(_addRow(c.lowerBounded()?c.lowerBound()-*c.expr():-INF, 1233 1233 ExprIterator(c.expr().comps.begin(), cols), 1234 1234 ExprIterator(c.expr().comps.end(), cols), 1235 c.upperBounded()?c.upperBound() :INF));1235 c.upperBounded()?c.upperBound()-*c.expr():INF)); 1236 1236 return r; 1237 1237 } -
lemon/network_simplex.h
r898 r911 165 165 166 166 typedef std::vector<int> IntVector; 167 typedef std::vector<char> CharVector;168 167 typedef std::vector<Value> ValueVector; 169 168 typedef std::vector<Cost> CostVector; 169 typedef std::vector<char> BoolVector; 170 // Note: vector<char> is used instead of vector<bool> for efficiency reasons 170 171 171 172 // State constants for arcs … … 214 215 IntVector _last_succ; 215 216 IntVector _dirty_revs; 216 CharVector _forward;217 CharVector _state;217 BoolVector _forward; 218 BoolVector _state; 218 219 int _root; 219 220 … … 246 247 const IntVector &_target; 247 248 const CostVector &_cost; 248 const CharVector &_state;249 const BoolVector &_state; 249 250 const CostVector &_pi; 250 251 int &_in_arc; … … 267 268 bool findEnteringArc() { 268 269 Cost c; 269 for (int e = _next_arc; e <_search_arc_num; ++e) {270 for (int e = _next_arc; e != _search_arc_num; ++e) { 270 271 c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); 271 272 if (c < 0) { … … 275 276 } 276 277 } 277 for (int e = 0; e <_next_arc; ++e) {278 for (int e = 0; e != _next_arc; ++e) { 278 279 c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); 279 280 if (c < 0) { … … 298 299 const IntVector &_target; 299 300 const CostVector &_cost; 300 const CharVector &_state;301 const BoolVector &_state; 301 302 const CostVector &_pi; 302 303 int &_in_arc; … … 315 316 bool findEnteringArc() { 316 317 Cost c, min = 0; 317 for (int e = 0; e <_search_arc_num; ++e) {318 for (int e = 0; e != _search_arc_num; ++e) { 318 319 c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); 319 320 if (c < min) { … … 337 338 const IntVector &_target; 338 339 const CostVector &_cost; 339 const CharVector &_state;340 const BoolVector &_state; 340 341 const CostVector &_pi; 341 342 int &_in_arc; … … 356 357 { 357 358 // The main parameters of the pivot rule 358 const double BLOCK_SIZE_FACTOR = 0.5;359 const double BLOCK_SIZE_FACTOR = 1.0; 359 360 const int MIN_BLOCK_SIZE = 10; 360 361 … … 369 370 int cnt = _block_size; 370 371 int e; 371 for (e = _next_arc; e <_search_arc_num; ++e) {372 for (e = _next_arc; e != _search_arc_num; ++e) { 372 373 c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); 373 374 if (c < min) { … … 380 381 } 381 382 } 382 for (e = 0; e <_next_arc; ++e) {383 for (e = 0; e != _next_arc; ++e) { 383 384 c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); 384 385 if (c < min) { … … 410 411 const IntVector &_target; 411 412 const CostVector &_cost; 412 const CharVector &_state;413 const BoolVector &_state; 413 414 const CostVector &_pi; 414 415 int &_in_arc; … … 471 472 min = 0; 472 473 _curr_length = 0; 473 for (e = _next_arc; e <_search_arc_num; ++e) {474 for (e = _next_arc; e != _search_arc_num; ++e) { 474 475 c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); 475 476 if (c < 0) { … … 482 483 } 483 484 } 484 for (e = 0; e <_next_arc; ++e) {485 for (e = 0; e != _next_arc; ++e) { 485 486 c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); 486 487 if (c < 0) { … … 513 514 const IntVector &_target; 514 515 const CostVector &_cost; 515 const CharVector &_state;516 const BoolVector &_state; 516 517 const CostVector &_pi; 517 518 int &_in_arc; … … 566 567 // Check the current candidate list 567 568 int e; 568 for (int i = 0; i <_curr_length; ++i) {569 for (int i = 0; i != _curr_length; ++i) { 569 570 e = _candidates[i]; 570 571 _cand_cost[e] = _state[e] * … … 579 580 int limit = _head_length; 580 581 581 for (e = _next_arc; e <_search_arc_num; ++e) {582 for (e = _next_arc; e != _search_arc_num; ++e) { 582 583 _cand_cost[e] = _state[e] * 583 584 (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); … … 591 592 } 592 593 } 593 for (e = 0; e <_next_arc; ++e) {594 for (e = 0; e != _next_arc; ++e) { 594 595 _cand_cost[e] = _state[e] * 595 596 (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); … … 1361 1362 1362 1363 // Update _rev_thread using the new _thread values 1363 for (int i = 0; i <int(_dirty_revs.size()); ++i) {1364 for (int i = 0; i != int(_dirty_revs.size()); ++i) { 1364 1365 u = _dirty_revs[i]; 1365 1366 _rev_thread[_thread[u]] = u; … … 1431 1432 _pi[u] += sigma; 1432 1433 } 1434 } 1435 1436 // Heuristic initial pivots 1437 bool initialPivots() { 1438 Value curr, total = 0; 1439 std::vector<Node> supply_nodes, demand_nodes; 1440 for (NodeIt u(_graph); u != INVALID; ++u) { 1441 curr = _supply[_node_id[u]]; 1442 if (curr > 0) { 1443 total += curr; 1444 supply_nodes.push_back(u); 1445 } 1446 else if (curr < 0) { 1447 demand_nodes.push_back(u); 1448 } 1449 } 1450 if (_sum_supply > 0) total -= _sum_supply; 1451 if (total <= 0) return true; 1452 1453 IntVector arc_vector; 1454 if (_sum_supply >= 0) { 1455 if (supply_nodes.size() == 1 && demand_nodes.size() == 1) { 1456 // Perform a reverse graph search from the sink to the source 1457 typename GR::template NodeMap<bool> reached(_graph, false); 1458 Node s = supply_nodes[0], t = demand_nodes[0]; 1459 std::vector<Node> stack; 1460 reached[t] = true; 1461 stack.push_back(t); 1462 while (!stack.empty()) { 1463 Node u, v = stack.back(); 1464 stack.pop_back(); 1465 if (v == s) break; 1466 for (InArcIt a(_graph, v); a != INVALID; ++a) { 1467 if (reached[u = _graph.source(a)]) continue; 1468 int j = _arc_id[a]; 1469 if (_cap[j] >= total) { 1470 arc_vector.push_back(j); 1471 reached[u] = true; 1472 stack.push_back(u); 1473 } 1474 } 1475 } 1476 } else { 1477 // Find the min. cost incomming arc for each demand node 1478 for (int i = 0; i != int(demand_nodes.size()); ++i) { 1479 Node v = demand_nodes[i]; 1480 Cost c, min_cost = std::numeric_limits<Cost>::max(); 1481 Arc min_arc = INVALID; 1482 for (InArcIt a(_graph, v); a != INVALID; ++a) { 1483 c = _cost[_arc_id[a]]; 1484 if (c < min_cost) { 1485 min_cost = c; 1486 min_arc = a; 1487 } 1488 } 1489 if (min_arc != INVALID) { 1490 arc_vector.push_back(_arc_id[min_arc]); 1491 } 1492 } 1493 } 1494 } else { 1495 // Find the min. cost outgoing arc for each supply node 1496 for (int i = 0; i != int(supply_nodes.size()); ++i) { 1497 Node u = supply_nodes[i]; 1498 Cost c, min_cost = std::numeric_limits<Cost>::max(); 1499 Arc min_arc = INVALID; 1500 for (OutArcIt a(_graph, u); a != INVALID; ++a) { 1501 c = _cost[_arc_id[a]]; 1502 if (c < min_cost) { 1503 min_cost = c; 1504 min_arc = a; 1505 } 1506 } 1507 if (min_arc != INVALID) { 1508 arc_vector.push_back(_arc_id[min_arc]); 1509 } 1510 } 1511 } 1512 1513 // Perform heuristic initial pivots 1514 for (int i = 0; i != int(arc_vector.size()); ++i) { 1515 in_arc = arc_vector[i]; 1516 if (_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] - 1517 _pi[_target[in_arc]]) >= 0) continue; 1518 findJoinNode(); 1519 bool change = findLeavingArc(); 1520 if (delta >= MAX) return false; 1521 changeFlow(change); 1522 if (change) { 1523 updateTreeStructure(); 1524 updatePotential(); 1525 } 1526 } 1527 return true; 1433 1528 } 1434 1529 … … 1455 1550 PivotRuleImpl pivot(*this); 1456 1551 1552 // Perform heuristic initial pivots 1553 if (!initialPivots()) return UNBOUNDED; 1554 1457 1555 // Execute the Network Simplex algorithm 1458 1556 while (pivot.findEnteringArc()) { -
scripts/bib2dox.py
r801 r905 1 #! /usr/bin/env /usr/local/Python/bin/python2.11 #! /usr/bin/env python 2 2 """ 3 3 BibTeX to Doxygen converter 4 4 Usage: python bib2dox.py bibfile.bib > bibfile.dox 5 5 6 This file is a part of LEMON, a generic C++ optimization library. 7 8 ********************************************************************** 9 6 10 This code is the modification of the BibTeX to XML converter 7 by Vidar Bronken Gundersen et al. See the original copyright notices below. 11 by Vidar Bronken Gundersen et al. 12 See the original copyright notices below. 8 13 9 14 **********************************************************************
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