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♯P-complete

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243:) problems. Determining the satisfiability of a boolean formula in DNF is easy: such a formula is satisfiable if and only if it contains a satisfiable conjunction (one that does not contain a variable and its negation), whereas counting the number of satisfying assignments is #P-complete. Furthermore, deciding 2-satisfiability is easy compared to counting the number of satisfying assignments. 287:
showed that every #P-complete problem either has an FPRAS, or is essentially impossible to approximate; if there is any polynomial-time algorithm which consistently produces an approximation of a #P-complete problem which is within a polynomial ratio in the size of the input of the exact answer, then
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to it. A counting reduction is a pair of polynomial-time transformations from inputs of the other problem to inputs of the given problem and from outputs of the given problem to outputs of the other problem, allowing the other problem to be solved using any subroutine for the given problem. A Turing
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problem: a series of variables that are each individually constrained, but have no relationships with each other. The solutions can be efficiently counted, by multiplying the number of options for each variable in isolation. Thus, this problem is in
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can be found in polynomial time, but counting all perfect matchings is #P-complete. The perfect matching counting problem was the first counting problem corresponding to an easy P problem shown to be #P-complete, in a 1979 paper by
275:, or "FPRAS," which, informally, will produce with high probability an approximation to an arbitrary degree of accuracy, in time that is polynomial with respect to both the size of the problem and the degree of accuracy required. 92:
reduction is an algorithm for the other problem that makes a polynomial number of calls to a subroutine for the given problem and, outside of those calls, uses polynomial time. In some cases
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that return good approximations to some #P-complete problems with high probability. This is one of the demonstrations of the power of probabilistic algorithms.
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by implying that P and NP are equal. No such algorithm is known, nor is a proof known that such an algorithm does not exist.
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problems. A polynomial-time algorithm for solving a #P-complete problem, if it existed, would solve the
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How many different variable assignments will satisfy a given general boolean formula? (
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which also defined the class #P and the #P-complete problems for the first time.
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is easy in contrast to counting the number of topological sortings. A single
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as well. As a non-example, consider the case of counting solutions to a
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problems (pronounced "sharp P complete" or "number P complete") form a
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Mark R. Jerrum; Leslie G. Valiant; Vijay V. Vazirani (1986).
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How many different variable assignments will satisfy a given
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How many different variable assignments will satisfy a given
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fully polynomial-time randomized approximation scheme
83:-hard, meaning that every other problem in #P has a 824: 788: 732: 625: 505: 219:. This would be surprising, as it would imply that 288:that algorithm can be used to construct an FPRAS. 156:of a given matrix whose entries are 0 or 1? (See 198:These are all necessarily members of the class 239:Some #P-complete problems correspond to easy ( 483: 100:#P-complete problems are at least as hard as 8: 382:"The Complexity of Computing the Permanent" 490: 476: 468: 116:Examples of #P-complete problems include: 430: 397: 235:Easy problems with hard counting versions 171:colors are there for a particular graph 296: 186:, or, equivalently, how many different 342:(1991). "Counting linear extensions". 30:The correct title of this article is 7: 211:, but cannot be #P-complete unless 304:Valiant, Leslie G. (August 1979). 89:polynomial-time counting reduction 25: 852:Probabilistically checkable proof 271:Many #P-complete problems have a 70:non-deterministic Turing machine 158:#P-completeness of 01-permanent 55:computational complexity theory 1: 432:10.1016/0304-3975(86)90174-x 419:Theoretical Computer Science 399:10.1016/0304-3975(79)90044-6 386:Theoretical Computer Science 449:Vazirani, Vijay V. (2003). 903: 868:List of complexity classes 380:Leslie G. Valiant (1979). 34:. The substitution of the 29: 865: 313:SIAM Journal on Computing 152:What is the value of the 857:Interactive proof system 451:Approximation Algorithms 392:(2). Elsevier: 189–201. 266:probabilistic algorithms 338:Brightwell, Graham R.; 94:parsimonious reductions 811:Arithmetical hierarchy 192:directed acyclic graph 190:are there for a given 182:are there for a given 145:are there for a given 40:technical restrictions 806:Grzegorczyk hierarchy 801:Exponential hierarchy 733:Considered infeasible 425:. Elsevier: 169–188. 245:Topologically sorting 188:topological orderings 184:partially ordered set 796:Polynomial hierarchy 626:Suspected infeasible 453:. Berlin: Springer. 825:Families of classes 506:Considered feasible 178:How many different 106:P versus NP problem 887:Complexity classes 499:Complexity classes 358:10.1007/BF00383444 61:The problem is in 874: 873: 816:Boolean hierarchy 789:Class hierarchies 180:linear extensions 143:perfect matchings 16:(Redirected from 894: 492: 485: 478: 469: 464: 437: 436: 434: 410: 404: 403: 401: 377: 371: 369: 335: 329: 328: 310: 301: 249:perfect matching 204:1-satisfiability 136:2-satisfiability 85:Turing reduction 51:complexity class 27:Complexity class 21: 18:Sharp P complete 902: 901: 897: 896: 895: 893: 892: 891: 877: 876: 875: 870: 861: 820: 784: 728: 722:PSPACE-complete 621: 501: 496: 461: 448: 445: 443:Further reading 440: 412: 411: 407: 379: 378: 374: 337: 336: 332: 325:10.1137/0208032 308: 303: 302: 298: 294: 262: 241:polynomial time 237: 165:graph colorings 147:bipartite graph 114: 79:The problem is 67:polynomial-time 43: 28: 23: 22: 15: 12: 11: 5: 900: 898: 890: 889: 879: 878: 872: 871: 866: 863: 862: 860: 859: 854: 849: 844: 839: 834: 828: 826: 822: 821: 819: 818: 813: 808: 803: 798: 792: 790: 786: 785: 783: 782: 777: 772: 767: 762: 757: 752: 747: 742: 736: 734: 730: 729: 727: 726: 725: 724: 714: 709: 708: 707: 697: 692: 687: 682: 677: 672: 667: 662: 661: 660: 658:co-NP-complete 655: 650: 645: 635: 629: 627: 623: 622: 620: 619: 614: 609: 604: 599: 594: 589: 588: 587: 577: 572: 567: 562: 561: 560: 550: 545: 540: 535: 530: 525: 520: 515: 509: 507: 503: 502: 497: 495: 494: 487: 480: 472: 466: 465: 459: 444: 441: 439: 438: 405: 372: 352:(3): 225–242. 340:Winkler, Peter 330: 319:(3): 410–421. 295: 293: 290: 261: 258: 254:Leslie Valiant 236: 233: 196: 195: 176: 161: 150: 139: 132: 125: 113: 110: 98: 97: 74: 73: 26: 24: 14: 13: 10: 9: 6: 4: 3: 2: 899: 888: 885: 884: 882: 869: 864: 858: 855: 853: 850: 848: 845: 843: 840: 838: 835: 833: 830: 829: 827: 823: 817: 814: 812: 809: 807: 804: 802: 799: 797: 794: 793: 791: 787: 781: 778: 776: 773: 771: 768: 766: 763: 761: 758: 756: 753: 751: 748: 746: 743: 741: 738: 737: 735: 731: 723: 720: 719: 718: 715: 713: 710: 706: 703: 702: 701: 698: 696: 693: 691: 688: 686: 683: 681: 678: 676: 673: 671: 668: 666: 663: 659: 656: 654: 651: 649: 646: 644: 641: 640: 639: 636: 634: 631: 630: 628: 624: 618: 615: 613: 610: 608: 605: 603: 600: 598: 595: 593: 590: 586: 583: 582: 581: 578: 576: 573: 571: 568: 566: 563: 559: 556: 555: 554: 551: 549: 546: 544: 541: 539: 536: 534: 531: 529: 526: 524: 521: 519: 516: 514: 511: 510: 508: 504: 500: 493: 488: 486: 481: 479: 474: 473: 470: 462: 460:3-540-65367-8 456: 452: 447: 446: 442: 433: 428: 424: 420: 416: 409: 406: 400: 395: 391: 387: 383: 376: 373: 367: 363: 359: 355: 351: 347: 346: 341: 334: 331: 326: 322: 318: 314: 307: 300: 297: 291: 289: 286: 282: 278: 274: 269: 267: 260:Approximation 259: 257: 255: 250: 246: 242: 234: 232: 230: 226: 222: 218: 214: 210: 205: 201: 193: 189: 185: 181: 177: 174: 170: 166: 162: 159: 155: 151: 148: 144: 140: 137: 133: 130: 126: 123: 119: 118: 117: 111: 109: 107: 103: 95: 90: 86: 82: 78: 77: 76: 71: 68: 64: 60: 59: 58: 56: 52: 48: 41: 37: 33: 19: 704: 450: 422: 418: 408: 389: 385: 375: 349: 343: 333: 316: 312: 299: 270: 263: 238: 197: 172: 168: 115: 99: 75: 46: 44: 31: 705:#P-complete 643:NP-complete 558:NL-complete 102:NP-complete 47:#P-complete 32:#P-complete 760:ELEMENTARY 585:P-complete 292:References 264:There are 38:is due to 755:2-EXPTIME 366:119697949 163:How many 154:permanent 141:How many 881:Category 750:EXPSPACE 745:NEXPTIME 513:DLOGTIME 285:Vazirani 138:problem? 131:formula? 112:Examples 740:EXPTIME 648:NP-hard 281:Valiant 847:NSPACE 842:DSPACE 717:PSPACE 457:  364:  283:, and 277:Jerrum 167:using 837:NTIME 832:DTIME 653:co-NP 362:S2CID 345:Order 309:(PDF) 665:TFNP 455:ISBN 122:#SAT 45:The 780:ALL 680:QMA 670:FNP 612:APX 607:BQP 602:BPP 592:ZPP 523:ACC 427:doi 394:doi 354:doi 321:doi 129:DNF 87:or 53:in 883:: 775:RE 765:PR 712:IP 700:#P 695:PP 690:⊕P 685:PH 675:AM 638:NP 633:UP 617:FP 597:RP 575:CC 570:SC 565:NC 553:NL 548:FL 543:RL 538:SL 528:TC 518:AC 423:43 421:. 417:. 388:. 384:. 360:. 348:. 315:. 311:. 279:, 231:. 229:PH 225:NP 217:FP 213:#P 209:#P 200:#P 160:.) 81:#P 63:#P 770:R 580:P 533:L 491:e 484:t 477:v 463:. 435:. 429:: 402:. 396:: 390:8 370:. 368:. 356:: 350:8 327:. 323:: 317:8 227:= 223:= 221:P 215:= 194:? 175:? 173:G 169:k 149:? 124:) 72:. 42:. 36:# 20:)

Index

Sharp P complete
#
technical restrictions
complexity class
computational complexity theory
#P
polynomial-time
non-deterministic Turing machine
#P
Turing reduction
polynomial-time counting reduction
parsimonious reductions
NP-complete
P versus NP problem
#SAT
DNF
2-satisfiability
perfect matchings
bipartite graph
permanent
#P-completeness of 01-permanent
graph colorings
linear extensions
partially ordered set
topological orderings
directed acyclic graph
#P
1-satisfiability
#P
#P

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