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Relaxation (approximation)

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The first property states that the original problem's feasible domain is a subset of the relaxed problem's feasible domain. The second property states that the original problem's objective-function is greater than or equal to the relaxed problem's objective-function.
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Murty, Katta G. (1983). "16 Iterative methods for linear inequalities and linear programs (especially 16.2 Relaxation methods, and 16.4 Sparsity-preserving iterative SOR algorithms for linear programming)".
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of a complicated problem in combinatorial optimization penalizes violations of some constraints, allowing an easier relaxed problem to be solved. Relaxation techniques complement or supplement
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algorithms of combinatorial optimization; linear programming and Lagrangian relaxations are used to obtain bounds in branch-and-bound algorithms for integer programming.
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of a difficult problem by a nearby problem that is easier to solve. A solution of the relaxed problem provides information about the original problem.
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Relaxation methods for finding feasible solutions to linear inequality systems arise in linear programming and in Lagrangian relaxation.
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Geoffrion, A. M. (1971). "Duality in Nonlinear Programming: A Simplified Applications-Oriented Development".
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problem removes the integrality constraint and so allows non-integer rational solutions. A
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Goffin, J.-L. (1980). "The relaxation method for solving systems of linear inequalities".
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Semicontinuity, Relaxation and Integral Representation in the Calculus of Variations
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George L. Nemhauser and Laurence A. Wolsey, Integer programming (pp. 447–527);
911: 844: 840: 55: 48: 1027: 997: 969: 954: 927: 1097: 29: 828: 810: 255:{\displaystyle z_{R}=\min\{c_{R}(x):x\in X_{R}\subseteq \mathbf {R} ^{n}\}} 915: 943:. Chichester: A Wiley-Interscience Publication. John Wiley & Sons. 923: 892: 66:(SOR); iterative methods of relaxation are used in solving problems in 54:
The modeling strategy of relaxation should not be confused with
157:{\displaystyle z=\min\{c(x):x\in X\subseteq \mathbf {R} ^{n}\}} 1077:
Relaxation in Optimization Theory and Variational Calculus
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is an optimal solution of the original problem, then
398: 359: 308: 274: 176: 99: 1050:, Nondifferentiable optimization (pp. 529–572); 776: 774: 1012:; Rinnooy Kan, A. H. G.; Todd, M. J., eds. (1989). 874:. Pitman Res. Notes in Math. 207. Harlow: Longmann. 856:
L. T. Gubin, Boris T. Polyak, and E. V. Raik (1969)
966:Programmation mathĂ©matique: ThĂ©orie et algorithmes 691: 662: 608: 581: 541: 457: 411: 371: 345: 293: 254: 156: 798: 771: 1095: 190: 106: 941:Mathematical programming: Theory and algorithms 1041:, Polyhedral combinatorics (pp. 371–446); 702: 619:If in addition to the previous assumptions, 249: 193: 167:is another minimization problem of the form 151: 109: 878: 766: 458:{\displaystyle x^{*}\in X\subseteq X_{R}} 1074: 869: 762: 760: 758: 1096: 1055: 938: 899: 816: 804: 780: 980: 792: 755: 1058:Optimization in operations research 986:. New York: John Wiley & Sons. 13: 903:Mathematics of Operations Research 677: 14: 1125: 346:{\displaystyle c_{R}(x)\leq c(x)} 963:Translated by Steven Vajda from 294:{\displaystyle X_{R}\supseteq X} 239: 141: 850: 834: 822: 740: 692:{\displaystyle \forall x\in X} 657: 651: 642: 636: 582:{\displaystyle x^{*}\in X_{R}} 523: 510: 494: 481: 340: 334: 325: 319: 212: 206: 121: 115: 1: 1079:. Berlin: Walter de Gruyter. 863: 819:, Section 4.3.7, pp. 120–123. 709:Linear programming relaxation 663:{\displaystyle c_{R}(x)=c(x)} 387: 81: 90:of the minimization problem 7: 589:provides an upper bound on 10: 1130: 1104:Relaxation (approximation) 1056:Rardin, Ronald L. (1998). 703:Some relaxation techniques 265:with these two properties 64:successive over-relaxation 1109:Mathematical optimization 18:mathematical optimization 733: 719:Semidefinite relaxation 968:. Paris: Dunod. 1983. 693: 664: 610: 583: 543: 459: 413: 373: 372:{\displaystyle x\in X} 347: 295: 256: 158: 68:differential equations 28:. A relaxation is an 1075:RoubĂ­ÄŤek, T. (1997). 870:Buttazzo, G. (1989). 714:Lagrangian relaxation 694: 665: 611: 609:{\displaystyle z_{R}} 584: 544: 460: 414: 412:{\displaystyle x^{*}} 374: 348: 296: 257: 159: 45:Lagrangian relaxation 916:10.1287/moor.5.3.388 724:Surrogate relaxation 674: 623: 593: 553: 469: 423: 396: 357: 306: 272: 174: 97: 72:linear least-squares 20:and related fields, 939:Minoux, M. (1986). 795:, pp. 453–464. 41:integer programming 984:Linear programming 689: 660: 606: 579: 539: 455: 409: 369: 343: 291: 252: 154: 76:linear programming 37:linear programming 1086:978-3-11-014542-7 1067:978-0-02-398415-0 1060:. Prentice Hall. 1048:Claude LemarĂ©chal 1039:W. R. Pulleyblank 1023:978-0-444-87284-5 993:978-0-471-09725-9 950:978-0-471-90170-9 56:iterative methods 39:relaxation of an 26:modeling strategy 1121: 1090: 1071: 1035: 1010:Nemhauser, G. L. 1005: 977: 962: 935: 896: 875: 857: 854: 848: 845:Isaac Schoenberg 841:Theodore Motzkin 838: 832: 826: 820: 814: 808: 802: 796: 790: 784: 778: 769: 767:Geoffrion (1971) 764: 747: 744: 698: 696: 695: 690: 669: 667: 666: 661: 635: 634: 615: 613: 612: 607: 605: 604: 588: 586: 585: 580: 578: 577: 565: 564: 548: 546: 545: 540: 538: 537: 522: 521: 509: 508: 493: 492: 464: 462: 461: 456: 454: 453: 435: 434: 418: 416: 415: 410: 408: 407: 378: 376: 375: 370: 352: 350: 349: 344: 318: 317: 300: 298: 297: 292: 284: 283: 261: 259: 258: 253: 248: 247: 242: 233: 232: 205: 204: 186: 185: 163: 161: 160: 155: 150: 149: 144: 49:branch and bound 1129: 1128: 1124: 1123: 1122: 1120: 1119: 1118: 1094: 1093: 1087: 1068: 1024: 994: 964: 951: 866: 861: 860: 855: 851: 839: 835: 827: 823: 815: 811: 803: 799: 791: 787: 779: 772: 765: 756: 751: 750: 745: 741: 736: 705: 675: 672: 671: 630: 626: 624: 621: 620: 600: 596: 594: 591: 590: 573: 569: 560: 556: 554: 551: 550: 533: 529: 517: 513: 504: 500: 488: 484: 470: 467: 466: 449: 445: 430: 426: 424: 421: 420: 403: 399: 397: 394: 393: 390: 358: 355: 354: 313: 309: 307: 304: 303: 279: 275: 273: 270: 269: 243: 238: 237: 228: 224: 200: 196: 181: 177: 175: 172: 171: 145: 140: 139: 98: 95: 94: 84: 35:For example, a 12: 11: 5: 1127: 1117: 1116: 1114:Approximations 1111: 1106: 1092: 1091: 1085: 1072: 1066: 1053: 1052: 1051: 1045: 1042: 1022: 1006: 992: 978: 949: 936: 910:(3): 388–414. 897: 876: 865: 862: 859: 858: 849: 833: 821: 809: 797: 785: 770: 753: 752: 749: 748: 738: 737: 735: 732: 731: 730: 721: 716: 711: 704: 701: 688: 685: 682: 679: 659: 656: 653: 650: 647: 644: 641: 638: 633: 629: 603: 599: 576: 572: 568: 563: 559: 536: 532: 528: 525: 520: 516: 512: 507: 503: 499: 496: 491: 487: 483: 480: 477: 474: 452: 448: 444: 441: 438: 433: 429: 406: 402: 389: 386: 381: 380: 368: 365: 362: 342: 339: 336: 333: 330: 327: 324: 321: 316: 312: 301: 290: 287: 282: 278: 263: 262: 251: 246: 241: 236: 231: 227: 223: 220: 217: 214: 211: 208: 203: 199: 195: 192: 189: 184: 180: 165: 164: 153: 148: 143: 138: 135: 132: 129: 126: 123: 120: 117: 114: 111: 108: 105: 102: 83: 80: 9: 6: 4: 3: 2: 1126: 1115: 1112: 1110: 1107: 1105: 1102: 1101: 1099: 1088: 1082: 1078: 1073: 1069: 1063: 1059: 1054: 1049: 1046: 1043: 1040: 1037: 1036: 1033: 1029: 1025: 1019: 1015: 1011: 1007: 1003: 999: 995: 989: 985: 979: 975: 971: 967: 960: 956: 952: 946: 942: 937: 933: 929: 925: 921: 917: 913: 909: 905: 904: 898: 894: 890: 886: 882: 877: 873: 868: 867: 853: 846: 842: 837: 830: 825: 818: 817:Minoux (1986) 813: 806: 805:Minoux (1986) 801: 794: 789: 782: 781:Goffin (1980) 777: 775: 768: 763: 761: 759: 754: 743: 739: 729: 725: 722: 720: 717: 715: 712: 710: 707: 706: 700: 686: 683: 680: 654: 648: 645: 639: 631: 627: 617: 601: 597: 574: 570: 566: 561: 557: 549:. Therefore, 534: 530: 526: 518: 514: 505: 501: 497: 489: 485: 478: 475: 472: 450: 446: 442: 439: 436: 431: 427: 404: 400: 385: 366: 363: 360: 337: 331: 328: 322: 314: 310: 302: 288: 285: 280: 276: 268: 267: 266: 244: 234: 229: 225: 221: 218: 215: 209: 201: 197: 187: 182: 178: 170: 169: 168: 146: 136: 133: 130: 127: 124: 118: 112: 103: 100: 93: 92: 91: 89: 79: 77: 73: 69: 65: 61: 57: 52: 50: 46: 42: 38: 33: 31: 30:approximation 27: 23: 19: 1076: 1057: 1014:Optimization 1013: 983: 965: 940: 907: 901: 884: 880: 871: 852: 836: 829:Shmuel Agmon 824: 812: 800: 793:Murty (1983) 788: 742: 618: 391: 382: 264: 166: 87: 85: 53: 34: 21: 15: 887:(1): 1–37. 881:SIAM Review 62:, such as 1098:Categories 864:References 388:Properties 88:relaxation 82:Definition 60:relaxation 22:relaxation 684:∈ 678:∀ 567:∈ 562:∗ 527:≥ 519:∗ 498:≥ 490:∗ 443:⊆ 437:∈ 432:∗ 405:∗ 364:∈ 329:≤ 286:⊇ 235:⊆ 222:∈ 137:⊆ 131:∈ 353:for all 1032:1105099 1002:0720547 974:2571910 959:0868279 932:0594854 924:3689446 893:2028848 728:duality 1083:  1064:  1030:  1020:  1000:  990:  972:  957:  947:  930:  922:  891:  847:(1954) 831:(1954) 74:, and 920:JSTOR 889:JSTOR 734:Notes 24:is a 1081:ISBN 1062:ISBN 1018:ISBN 988:ISBN 945:ISBN 843:and 726:and 465:and 912:doi 392:If 191:min 107:min 58:of 16:In 1100:: 1028:MR 1026:. 998:MR 996:. 970:MR 955:MR 953:. 928:MR 926:. 918:. 906:. 885:13 883:. 773:^ 757:^ 670:, 616:. 86:A 70:, 1089:. 1070:. 1034:. 1004:. 976:. 961:. 934:. 914:: 908:5 895:. 807:. 783:. 687:X 681:x 658:) 655:x 652:( 649:c 646:= 643:) 640:x 637:( 632:R 628:c 602:R 598:z 575:R 571:X 558:x 535:R 531:z 524:) 515:x 511:( 506:R 502:c 495:) 486:x 482:( 479:c 476:= 473:z 451:R 447:X 440:X 428:x 401:x 379:. 367:X 361:x 341:) 338:x 335:( 332:c 326:) 323:x 320:( 315:R 311:c 289:X 281:R 277:X 250:} 245:n 240:R 230:R 226:X 219:x 216:: 213:) 210:x 207:( 202:R 198:c 194:{ 188:= 183:R 179:z 152:} 147:n 142:R 134:X 128:x 125:: 122:) 119:x 116:( 113:c 110:{ 104:= 101:z

Index

mathematical optimization
modeling strategy
approximation
linear programming
integer programming
Lagrangian relaxation
branch and bound
iterative methods
relaxation
successive over-relaxation
differential equations
linear least-squares
linear programming
Linear programming relaxation
Lagrangian relaxation
Semidefinite relaxation
Surrogate relaxation
duality



Geoffrion (1971)


Goffin (1980)
Murty (1983)
Minoux (1986)
Minoux (1986)
Shmuel Agmon
Theodore Motzkin

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