Knowledge (XXG)

Minimum cut

Source 📝

553: 20: 135:
In a weighted, undirected network, it is possible to calculate the cut that separates a particular pair of vertices from each other and has minimum possible weight. A system of cuts that solves this problem for every possible vertex pair can be collected into a structure known as the
128:, the minimum cut separates the source and sink vertices and minimizes the total sum of the capacities of the edges that are directed from the source side of the cut to the sink side of the cut. As shown in the 192:
problems are a family of combinatorial optimization problems in which a graph is to be partitioned into two or more parts with additional constraints such as balancing the sizes of the two sides of the cut.
23:
A graph and two of its cuts. The dotted line in red represents a cut with three crossing edges. The dashed line in green represents one of the minimum cuts of this graph, crossing only two edges.
345: 413: 237: 179: 369: 276: 506: 450: 194: 54:
Variations of the minimum cut problem consider weighted graphs, directed graphs, terminals, and partitioning the vertices into more than two sets.
478: 561: 132:, the weight of this cut equals the maximum amount of flow that can be sent from the source to the sink in the given network. 595: 590: 281: 40: 585: 57:
The weighted min-cut problem allowing both positive and negative weights can be trivially transformed into a weighted
213:
and edge weights are their distances. This is however often impractical due do the high computational complexity for
537: 243: 129: 454: 82: 74: 374: 250:
value. In this case, some algorithms used in maxflow problem could also be used to solve this question.
78: 81:
provides an efficient randomized method for finding the cut. In this case, the minimum cut equals the
348: 571:
incorrectly led you here, you may wish to change the link to point directly to the intended article.
198: 137: 529: 202: 104:, this problem can be solved in polynomial time, though the algorithm is not practical for large 48: 44: 473: 469: 521: 487: 429: 216: 206: 70: 505:
Dahlhaus, E.; Johnson, D. S.; Papadimitriou, C. H.; Seymour, P. D.; Yannakakis, M. (1994).
189: 73:, weighted graphs limited to non-negative weights can be solved in polynomial time by the 158: 354: 261: 51:
of the vertices of a graph into two disjoint subsets) that is minimal in some metric.
579: 151:, this problem can be solved in polynomial time. However, in general this problem is 89: 533: 210: 148: 125: 28: 424: 152: 100:
connected components by removing as few edges as possible. For a fixed value of
58: 564:
includes a list of related items that share the same name (or similar names).
525: 19: 552: 491: 116:
When two terminal nodes are given, they are typically referred to as the
247: 88:
A generalization of the minimum cut problem without terminals is the
432:, an analogous concept to minimum cuts for vertices instead of edges 209:
method, where the nodes are data samples assumed to be taken from a
143:
A generalization of the minimum cut problem with terminals is the
18: 347:
distinct minimum cuts. This bound is tight in the sense that a
96:, in which the goal is to partition the graph into at least 474:"A Polynomial Algorithm for the k-cut Problem for Fixed k" 568: 197:
can be viewed as a specific case of normalized min-cut
16:
Partition of a graph by removing fewest possible edges
377: 357: 284: 264: 219: 161: 77:. In the special case when the graph is unweighted, 340:{\displaystyle {\binom {n}{2}}={\frac {n(n-1)}{2}}} 407: 363: 339: 270: 231: 173: 301: 288: 558:Index of articles associated with the same name 246:, 2 nodes' Minimum cut value is equal to their 61:problem by flipping the sign in all weights. 8: 147:-terminal cut, or multi-terminal cut. In a 378: 376: 356: 310: 300: 287: 285: 283: 263: 218: 160: 195:Segmentation-based object categorization 442: 507:"The Complexity of Multiterminal Cuts" 7: 408:{\displaystyle {\frac {n(n-1)}{2}}} 205:. It can also be used as a generic 479:Mathematics of Operations Research 292: 14: 551: 396: 384: 328: 316: 278:vertices can at the most have 1: 69:The minimum cut problem in 612: 550: 526:10.1137/S0097539792225297 514:SIAM Journal on Computing 244:max-flow min-cut theorem 130:max-flow min-cut theorem 468:Goldschmidt, Olivier; 451:"4 Min-Cut Algorithms" 409: 365: 341: 272: 254:Number of minimum cuts 233: 232:{\displaystyle k>2} 175: 75:Stoer-Wagner algorithm 65:Without terminal nodes 24: 410: 371:vertices has exactly 366: 342: 273: 234: 176: 22: 596:Network flow problem 591:Graph theory objects 492:10.1287/moor.19.1.24 375: 355: 282: 262: 217: 159: 199:spectral clustering 174:{\displaystyle k=3} 112:With terminal nodes 586:Set index articles 470:Hochbaum, Dorit S. 405: 361: 337: 268: 229: 203:image segmentation 171: 79:Karger's algorithm 25: 562:set index article 403: 364:{\displaystyle n} 335: 299: 271:{\displaystyle n} 83:edge connectivity 603: 572: 555: 545: 544: 542: 536:. Archived from 511: 502: 496: 495: 465: 459: 458: 453:. Archived from 447: 430:Vertex separator 414: 412: 411: 406: 404: 399: 379: 370: 368: 367: 362: 346: 344: 343: 338: 336: 331: 311: 306: 305: 304: 291: 277: 275: 274: 269: 238: 236: 235: 230: 180: 178: 177: 172: 146: 107: 103: 99: 93: 611: 610: 606: 605: 604: 602: 601: 600: 576: 575: 574: 573: 566: 565: 559: 549: 548: 540: 509: 504: 503: 499: 467: 466: 462: 449: 448: 444: 439: 421: 380: 373: 372: 353: 352: 312: 286: 280: 279: 260: 259: 256: 215: 214: 190:Graph partition 187: 157: 156: 144: 114: 105: 101: 97: 91: 67: 17: 12: 11: 5: 609: 607: 599: 598: 593: 588: 578: 577: 557: 556: 547: 546: 543:on 2018-12-25. 520:(4): 864–894. 497: 460: 457:on 2016-08-05. 441: 440: 438: 435: 434: 433: 427: 420: 417: 415:minimum cuts. 402: 398: 395: 392: 389: 386: 383: 360: 349:(simple) cycle 334: 330: 327: 324: 321: 318: 315: 309: 303: 298: 295: 290: 267: 255: 252: 228: 225: 222: 186: 183: 170: 167: 164: 140:of the graph. 138:Gomory–Hu tree 113: 110: 85:of the graph. 66: 63: 15: 13: 10: 9: 6: 4: 3: 2: 608: 597: 594: 592: 589: 587: 584: 583: 581: 570: 569:internal link 563: 554: 539: 535: 531: 527: 523: 519: 515: 508: 501: 498: 493: 489: 485: 481: 480: 475: 471: 464: 461: 456: 452: 446: 443: 436: 431: 428: 426: 423: 422: 418: 416: 400: 393: 390: 387: 381: 358: 350: 332: 325: 322: 319: 313: 307: 296: 293: 265: 258:A graph with 253: 251: 249: 245: 240: 226: 223: 220: 212: 208: 204: 200: 196: 191: 184: 182: 168: 165: 162: 154: 150: 141: 139: 133: 131: 127: 123: 119: 111: 109: 95: 86: 84: 80: 76: 72: 64: 62: 60: 55: 52: 50: 46: 42: 38: 34: 30: 21: 538:the original 517: 513: 500: 483: 477: 463: 455:the original 445: 257: 241: 211:metric space 188: 185:Applications 149:planar graph 142: 134: 126:flow network 121: 117: 115: 87: 68: 56: 53: 36: 32: 29:graph theory 26: 425:Maximum cut 201:applied to 155:, even for 59:maximum cut 33:minimum cut 580:Categories 437:References 207:clustering 71:undirected 486:: 24–37. 391:− 323:− 49:partition 472:(1994). 419:See also 120:and the 90:minimum 534:1123876 248:maxflow 242:Due to 153:NP-hard 124:. In a 37:min-cut 567:If an 532:  118:source 560:This 541:(PDF) 530:S2CID 510:(PDF) 43:is a 41:graph 39:of a 224:> 122:sink 94:-cut 31:, a 522:doi 488:doi 351:on 108:. 47:(a 45:cut 35:or 27:In 582:: 528:. 518:23 516:. 512:. 484:19 482:. 476:. 239:. 181:. 524:: 494:. 490:: 401:2 397:) 394:1 388:n 385:( 382:n 359:n 333:2 329:) 326:1 320:n 317:( 314:n 308:= 302:) 297:2 294:n 289:( 266:n 227:2 221:k 169:3 166:= 163:k 145:k 106:k 102:k 98:k 92:k

Index


graph theory
graph
cut
partition
maximum cut
undirected
Stoer-Wagner algorithm
Karger's algorithm
edge connectivity
minimum k-cut
flow network
max-flow min-cut theorem
Gomory–Hu tree
planar graph
NP-hard
Graph partition
Segmentation-based object categorization
spectral clustering
image segmentation
clustering
metric space
max-flow min-cut theorem
maxflow
(simple) cycle
Maximum cut
Vertex separator
"4 Min-Cut Algorithms"
the original
Hochbaum, Dorit S.

Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.