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Network simplex algorithm

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variable is selected by some pricing strategy, based on the dual multipliers (node potentials), and forms a cycle with the arcs of the tree. The leaving variable is the arc of the cycle with the least augmenting flow. The substitution of entering for leaving arc, and the reconstruction of the tree is called a pivot. When no non-basic arc remains eligible to enter, the optimal solution has been reached.
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The network simplex method is an adaptation of the bounded variable primal simplex algorithm. The basis is represented as a rooted spanning tree of the underlying network, in which variables are represented by arcs, and the simplex multipliers by node potentials. At each iteration, an entering
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For a long time, the existence of a provably efficient network simplex algorithm was one of the major open problems in complexity theory, even though efficient-in-practice versions were available. In 1995
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in 1997. Strongly polynomial dual network simplex algorithms for the same problem, but with a higher dependence on the numbers of edges and vertices in the graph, have been known for longer.
187: 104: 36:. The network simplex method works very well in practice, typically 200 to 300 times faster than the simplex method applied to general linear program of same dimensions. 502: 124: 467: 219: 414: 235: 492: 432: 482: 317:
Tarjan, Robert E. (1997-08-01). "Dynamic trees as search trees via euler tours, applied to the network simplex algorithm".
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Orlin, James B. (1997-08-01). "A polynomial time primal network simplex algorithm for minimum cost flows".
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The network simplex algorithm can be used to solve many practical problems including,
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provided the first polynomial algorithm with runtime of
136: 112: 55: 32:. The algorithm is usually formulated in terms of a 181: 118: 98: 454:Section 14, p B-113 shows an example execution 8: 382: 285: 135: 111: 66: 54: 256: 503:Computational problems in graph theory 7: 126:is maximum cost of any edges. Later 468:Optimization algorithms and methods 182:{\displaystyle O(VE\log V\log(VC))} 236:System of distinct representatives 14: 99:{\displaystyle O(V^{2}E\log(VC))} 220:Hitchcock transportation problem 176: 173: 164: 140: 93: 90: 81: 59: 1: 519: 229:Chains and antichains in 34:minimum-cost flow problem 22:network simplex algorithm 18:mathematical optimization 493:Polynomial-time problems 452:Solving Network Problems 370:Mathematical Programming 319:Mathematical Programming 266:Mathematical Programming 413:Chvatal, Vasek (1983). 240:Covers and matching in 423:. Macmillan. pp.  231:partially ordered sets 183: 120: 100: 28:specialization of the 483:Mathematical problems 363:; Plotkin, Serge A.; 215:Transshipment problem 184: 121: 101: 478:Network flow problem 134: 110: 53: 473:Linear programming 420:Linear Programming 393:10.1007/bf01580615 331:10.1007/BF02614369 278:10.1007/BF02614365 225:Assignment problem 179: 116: 96: 130:improved this to 119:{\displaystyle C} 30:simplex algorithm 510: 498:Graph algorithms 439: 438: 410: 404: 403: 386: 377:(1–3): 255–276, 357: 351: 350: 314: 308: 307: 289: 261: 242:bipartite graphs 188: 186: 185: 180: 125: 123: 122: 117: 105: 103: 102: 97: 71: 70: 518: 517: 513: 512: 511: 509: 508: 507: 458: 457: 448: 443: 442: 435: 412: 411: 407: 384:10.1.1.297.5730 361:Orlin, James B. 359: 358: 354: 316: 315: 311: 263: 262: 258: 253: 246:Caterer problem 208: 199: 132: 131: 108: 107: 62: 51: 50: 42: 26:graph theoretic 12: 11: 5: 516: 514: 506: 505: 500: 495: 490: 488:Network theory 485: 480: 475: 470: 460: 459: 456: 455: 447: 446:External links 444: 441: 440: 433: 405: 352: 325:(2): 169–177. 309: 272:(2): 109–129. 255: 254: 252: 249: 248: 247: 244: 238: 233: 227: 222: 217: 207: 204: 198: 195: 178: 175: 172: 169: 166: 163: 160: 157: 154: 151: 148: 145: 142: 139: 115: 95: 92: 89: 86: 83: 80: 77: 74: 69: 65: 61: 58: 41: 38: 13: 10: 9: 6: 4: 3: 2: 515: 504: 501: 499: 496: 494: 491: 489: 486: 484: 481: 479: 476: 474: 471: 469: 466: 465: 463: 453: 450: 449: 445: 436: 434:9780716715870 430: 426: 422: 421: 416: 409: 406: 402: 398: 394: 390: 385: 380: 376: 372: 371: 366: 362: 356: 353: 348: 344: 340: 336: 332: 328: 324: 320: 313: 310: 305: 301: 297: 293: 288: 283: 279: 275: 271: 267: 260: 257: 250: 245: 243: 239: 237: 234: 232: 228: 226: 223: 221: 218: 216: 213: 212: 211: 205: 203: 196: 194: 192: 191:dynamic trees 170: 167: 161: 158: 155: 152: 149: 146: 143: 137: 129: 113: 87: 84: 78: 75: 72: 67: 63: 56: 48: 39: 37: 35: 31: 27: 23: 19: 419: 408: 374: 368: 355: 322: 318: 312: 269: 265: 259: 209: 206:Applications 200: 43: 21: 15: 365:Tardos, Éva 287:1721.1/2584 462:Categories 251:References 379:CiteSeerX 339:0025-5610 296:0025-5610 162:⁡ 153:⁡ 79:⁡ 347:18977577 197:Overview 425:320–351 401:5838223 304:3107792 40:History 431:  399:  381:  345:  337:  302:  294:  189:using 128:Tarjan 106:where 20:, the 397:S2CID 343:S2CID 300:S2CID 47:Orlin 24:is a 429:ISBN 415:"20" 335:ISSN 292:ISSN 389:doi 327:doi 282:hdl 274:doi 159:log 150:log 76:log 16:In 464:: 427:. 417:. 395:, 387:, 375:60 373:, 341:. 333:. 323:78 321:. 298:. 290:. 280:. 270:78 268:. 437:. 391:: 349:. 329:: 306:. 284:: 276:: 177:) 174:) 171:C 168:V 165:( 156:V 147:E 144:V 141:( 138:O 114:C 94:) 91:) 88:C 85:V 82:( 73:E 68:2 64:V 60:( 57:O

Index

mathematical optimization
graph theoretic
simplex algorithm
minimum-cost flow problem
Orlin
Tarjan
dynamic trees
Transshipment problem
Hitchcock transportation problem
Assignment problem
partially ordered sets
System of distinct representatives
bipartite graphs
doi
10.1007/BF02614365
hdl
1721.1/2584
ISSN
0025-5610
S2CID
3107792
doi
10.1007/BF02614369
ISSN
0025-5610
S2CID
18977577
Orlin, James B.
Tardos, Éva
Mathematical Programming

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