Knowledge

Optimization problem

Source 📝

333: 763:, algorithms are designed to find near-optimal solutions to hard problems. The usual decision version is then an inadequate definition of the problem since it only specifies acceptable solutions. Even though we could introduce suitable decision problems, the problem is more naturally characterized as an optimization problem. 142: 328:{\displaystyle {\begin{aligned}&{\underset {x}{\operatorname {minimize} }}&&f(x)\\&\operatorname {subject\;to} &&g_{i}(x)\leq 0,\quad i=1,\dots ,m\\&&&h_{j}(x)=0,\quad j=1,\dots ,p\end{aligned}}} 708: 147: 947: 827: â€“ optimization problem with a finite number of variables and an infinite number of constraints, or an infinite number of variables and a finite number of constraints 940: 933: 610: 748:
that uses the fewest edges". This problem might have an answer of, say, 4. A corresponding decision problem would be "is there a path from
1137: 812: â€“ Cognitive heuristic of searching for an acceptable decision − the optimum need not be found, just a "good enough" solution. 1037: 1129: 887: 862: 1142: 783: 1376: 1162: 1079: 772: 726: 96: 1182: 906: 1147: 466: 460: 1187: 1177: 824: 20: 786: â€“ theorem that asserts that there exist nearly optimal solutions to some optimization problems 1167: 1152: 994: 439:, the problem is an unconstrained optimization problem. By convention, the standard form defines a 385: 136: 117: 1237: 1214: 1108: 1032: 849: 760: 108: 1355: 1316: 1232: 1157: 1084: 1069: 1022: 567: 62: 1301: 1293: 1289: 1285: 1281: 1277: 1089: 587: 581: 559: 79: 47: 1094: 960: 925: 8: 1027: 1017: 1012: 803: 778: 113: 84: 66: 756:
that uses 10 or fewer edges?" This problem can be answered with a simple 'yes' or 'no'.
1256: 974: 354: 1074: 883: 858: 501: 70: 55: 1227: 1172: 1063: 1058: 792: 713: 448: 61:
Optimization problems can be divided into two categories, depending on whether the
35: 1247: 1218: 1192: 1113: 1098: 1007: 979: 956: 344: 1334: 1242: 1103: 1002: 815: 131: 1370: 1118: 798: 100: 806: â€“ Discipline concerning the application of advanced analytical methods 1339: 716:
that asks whether there is a feasible solution for some particular measure
348: 1329: 1324: 1208: 809: 570: 92: 31: 27: 1252: 1222: 984: 818: â€“ type of computational problem represented by a binary relation 712:
For each combinatorial optimization problem, there is a corresponding
39: 1261: 1042: 88: 77:
An optimization problem with discrete variables is known as a
955: 703:{\displaystyle m(x,y)=g\left\{m(x,y'):y'\in f(x)\right\}.} 740:, an optimization problem might be "find a path from 613: 454: 145: 829:
Pages displaying wikidata descriptions as a fallback
820:
Pages displaying wikidata descriptions as a fallback
788:
Pages displaying wikidata descriptions as a fallback
702: 327: 124: 106:A problem with continuous variables is known as a 907:"How Traffic Shaping Optimizes Network Bandwidth" 1368: 847: 848:Boyd, Stephen P.; Vandenberghe, Lieven (2004). 941: 16:Problem of finding the best feasible solution 857:. Cambridge University Press. p. 129. 595:The goal is then to find for some instance 948: 934: 203: 871: 877: 19:For broader coverage of this topic, see 1038:Locally convex topological vector space 878:Ausiello, Giorgio; et al. (2003), 1369: 929: 795: â€“ Type of computational problem 775: â€“ Type of computational problem 579:is the goal function, and is either 112:, in which an optimal value from a 13: 455:Combinatorial optimization problem 207: 204: 200: 197: 194: 191: 188: 185: 182: 14: 1388: 899: 528:is the set of feasible solutions; 882:(Corrected ed.), Springer, 116:must be found. They can include 1143:Ekeland's variational principle 784:Ekeland's variational principle 603:, that is, a feasible solution 299: 242: 125:Continuous optimization problem 841: 689: 683: 663: 646: 629: 617: 287: 281: 230: 224: 173: 167: 1: 834: 773:Counting problem (complexity) 725:. For example, if there is a 880:Complexity and Approximation 7: 1163:Hermite–Hadamard inequality 766: 10: 1393: 467:combinatorial optimization 461:Combinatorial optimization 458: 18: 1348: 1315: 1270: 1201: 1127: 1051: 993: 967: 825:Semi-infinite programming 358:to be minimized over the 21:Mathematical optimization 1349:Applications and related 1153:Fenchel-Young inequality 761:approximation algorithms 732:which contains vertices 535:and a feasible solution 451:the objective function. 139:optimization problem is 120:and multimodal problems. 1109:Legendre transformation 1033:Legendre transformation 109:continuous optimization 1377:Computational problems 1356:Convexity in economics 1290:(lower) ideally convex 1148:Fenchel–Moreau theorem 1138:CarathĂ©odory's theorem 704: 329: 1278:Convex series related 1178:Shapley–Folkman lemma 705: 566:, which is usually a 330: 99:must be found from a 80:discrete optimization 1168:Krein–Milman theorem 961:variational analysis 611: 445:maximization problem 441:minimization problem 406:equality constraints 143: 118:constrained problems 44:optimization problem 1158:Jensen's inequality 1028:Lagrange multiplier 1018:Convex optimization 1013:Convex metric space 851:Convex Optimization 804:Operations research 779:Design Optimization 114:continuous function 1286:(cs, bcs)-complete 1257:Algebraic interior 975:Convex combination 700: 531:given an instance 507:given an instance 447:can be treated by 355:objective function 325: 323: 159: 56:feasible solutions 54:solution from all 1364: 1363: 889:978-3-540-65431-5 864:978-0-521-83378-3 362:-variable vector 152: 1384: 1282:(cs, lcs)-closed 1228:Effective domain 1183:Robinson–Ursescu 1059:Convex conjugate 950: 943: 936: 927: 926: 922: 920: 918: 893: 892: 875: 869: 868: 856: 845: 830: 821: 793:Function problem 789: 759:In the field of 755: 751: 747: 743: 739: 735: 731: 724: 714:decision problem 709: 707: 706: 701: 696: 692: 676: 662: 606: 601:optimal solution 598: 590: 584: 578: 565: 557: 542: 538: 534: 527: 516: 499: 492: 472: 438: 423: 416: 403: 381: 365: 361: 351: 334: 332: 331: 326: 324: 280: 279: 269: 268: 267: 223: 222: 212: 210: 179: 162: 160: 149: 36:computer science 1392: 1391: 1387: 1386: 1385: 1383: 1382: 1381: 1367: 1366: 1365: 1360: 1344: 1311: 1266: 1197: 1123: 1114:Semi-continuity 1099:Convex function 1080:Logarithmically 1047: 1008:Convex geometry 989: 980:Convex function 963: 957:Convex analysis 954: 916: 914: 905: 902: 897: 896: 890: 876: 872: 865: 854: 846: 842: 837: 828: 819: 787: 769: 753: 749: 745: 741: 737: 733: 729: 723: 717: 669: 655: 642: 638: 612: 609: 608: 604: 596: 586: 580: 576: 563: 544: 540: 536: 532: 518: 508: 497: 474: 473:is a quadruple 470: 463: 457: 429: 418: 411: 396: 391: 374: 369: 363: 359: 339: 322: 321: 275: 271: 265: 264: 218: 214: 211: 181: 177: 176: 161: 151: 146: 144: 141: 140: 127: 50:of finding the 24: 17: 12: 11: 5: 1390: 1380: 1379: 1362: 1361: 1359: 1358: 1352: 1350: 1346: 1345: 1343: 1342: 1337: 1335:Strong duality 1332: 1327: 1321: 1319: 1313: 1312: 1310: 1309: 1274: 1272: 1268: 1267: 1265: 1264: 1259: 1250: 1245: 1243:John ellipsoid 1240: 1235: 1230: 1225: 1211: 1205: 1203: 1199: 1198: 1196: 1195: 1190: 1185: 1180: 1175: 1170: 1165: 1160: 1155: 1150: 1145: 1140: 1134: 1132: 1130:results (list) 1125: 1124: 1122: 1121: 1116: 1111: 1106: 1104:Invex function 1101: 1092: 1087: 1082: 1077: 1072: 1066: 1061: 1055: 1053: 1049: 1048: 1046: 1045: 1040: 1035: 1030: 1025: 1020: 1015: 1010: 1005: 1003:Choquet theory 999: 997: 991: 990: 988: 987: 982: 977: 971: 969: 968:Basic concepts 965: 964: 953: 952: 945: 938: 930: 924: 923: 913:. 12 July 2016 901: 900:External links 898: 895: 894: 888: 870: 863: 839: 838: 836: 833: 832: 831: 822: 816:Search problem 813: 807: 801: 796: 790: 781: 776: 768: 765: 721: 699: 695: 691: 688: 685: 682: 679: 675: 672: 668: 665: 661: 658: 654: 651: 648: 645: 641: 637: 634: 631: 628: 625: 622: 619: 616: 593: 592: 574: 529: 505: 459:Main article: 456: 453: 426: 425: 409: 394: 389: 372: 367: 320: 317: 314: 311: 308: 305: 302: 298: 295: 292: 289: 286: 283: 278: 274: 270: 266: 263: 260: 257: 254: 251: 248: 245: 241: 238: 235: 232: 229: 226: 221: 217: 213: 209: 206: 202: 199: 196: 193: 190: 187: 184: 180: 178: 175: 172: 169: 166: 163: 158: 155: 150: 148: 126: 123: 122: 121: 104: 83:, in which an 15: 9: 6: 4: 3: 2: 1389: 1378: 1375: 1374: 1372: 1357: 1354: 1353: 1351: 1347: 1341: 1338: 1336: 1333: 1331: 1328: 1326: 1323: 1322: 1320: 1318: 1314: 1307: 1305: 1299: 1297: 1291: 1287: 1283: 1279: 1276: 1275: 1273: 1269: 1263: 1260: 1258: 1254: 1251: 1249: 1246: 1244: 1241: 1239: 1236: 1234: 1231: 1229: 1226: 1224: 1220: 1216: 1212: 1210: 1207: 1206: 1204: 1200: 1194: 1191: 1189: 1186: 1184: 1181: 1179: 1176: 1174: 1173:Mazur's lemma 1171: 1169: 1166: 1164: 1161: 1159: 1156: 1154: 1151: 1149: 1146: 1144: 1141: 1139: 1136: 1135: 1133: 1131: 1126: 1120: 1119:Subderivative 1117: 1115: 1112: 1110: 1107: 1105: 1102: 1100: 1096: 1093: 1091: 1088: 1086: 1083: 1081: 1078: 1076: 1073: 1071: 1067: 1065: 1062: 1060: 1057: 1056: 1054: 1050: 1044: 1041: 1039: 1036: 1034: 1031: 1029: 1026: 1024: 1021: 1019: 1016: 1014: 1011: 1009: 1006: 1004: 1001: 1000: 998: 996: 995:Topics (list) 992: 986: 983: 981: 978: 976: 973: 972: 970: 966: 962: 958: 951: 946: 944: 939: 937: 932: 931: 928: 912: 908: 904: 903: 891: 885: 881: 874: 866: 860: 853: 852: 844: 840: 826: 823: 817: 814: 811: 808: 805: 802: 800: 799:Glove problem 797: 794: 791: 785: 782: 780: 777: 774: 771: 770: 764: 762: 757: 728: 720: 715: 710: 697: 693: 686: 680: 677: 673: 670: 666: 659: 656: 652: 649: 643: 639: 635: 632: 626: 623: 620: 614: 602: 589: 583: 575: 572: 569: 561: 555: 551: 547: 530: 525: 521: 515: 511: 506: 504:of instances; 503: 496: 495: 494: 490: 486: 482: 478: 468: 462: 452: 450: 446: 442: 436: 432: 421: 414: 410: 407: 401: 397: 390: 388: 387: 379: 375: 368: 357: 356: 350: 346: 342: 338: 337: 336: 318: 315: 312: 309: 306: 303: 300: 296: 293: 290: 284: 276: 272: 261: 258: 255: 252: 249: 246: 243: 239: 236: 233: 227: 219: 215: 170: 164: 156: 153: 138: 134: 133: 132:standard form 119: 115: 111: 110: 105: 102: 101:countable set 98: 94: 90: 86: 82: 81: 76: 75: 74: 72: 68: 64: 59: 57: 53: 49: 45: 41: 37: 33: 29: 22: 1340:Weak duality 1303: 1295: 1215:Orthogonally 915:. Retrieved 910: 879: 873: 850: 843: 758: 718: 711: 600: 594: 558:denotes the 553: 549: 545: 523: 519: 513: 509: 488: 484: 480: 476: 465:Formally, a 464: 444: 440: 434: 430: 427: 419: 412: 405: 399: 392: 383: 377: 370: 353: 340: 130: 128: 107: 78: 60: 51: 43: 25: 1330:Duality gap 1325:Dual system 1209:Convex hull 917:13 February 810:Satisficing 404:are called 386:constraints 384:inequality 382:are called 93:permutation 87:such as an 32:engineering 28:mathematics 1253:Radial set 1223:Convex set 985:Convex set 835:References 137:continuous 67:continuous 1238:Hypograph 678:∈ 313:… 256:… 234:≤ 63:variables 40:economics 1371:Category 1262:Zonotope 1233:Epigraph 767:See also 674:′ 660:′ 568:positive 493:, where 469:problem 449:negating 343: : 154:minimize 71:discrete 1317:Duality 1219:Pseudo- 1193:Ursescu 1090:Pseudo- 1064:Concave 1043:Simplex 1023:Duality 560:measure 352:is the 89:integer 48:problem 46:is the 1300:, and 1271:Series 1188:Simons 1095:Quasi- 1085:Proper 1070:Closed 886:  861:  335:where 85:object 1128:Main 855:(pdf) 727:graph 607:with 500:is a 408:, and 402:) = 0 380:) ≀ 0 135:of a 97:graph 42:, an 1248:Lens 1202:Sets 1052:Maps 959:and 919:2017 884:ISBN 859:ISBN 736:and 571:real 443:. A 417:and 129:The 65:are 52:best 38:and 1302:(Hw 911:IPC 752:to 744:to 599:an 588:max 585:or 582:min 562:of 539:of 502:set 437:= 0 428:If 422:≄ 0 415:≄ 0 95:or 73:: 69:or 26:In 1373:: 1294:(H 1292:, 1288:, 1284:, 1221:) 1217:, 1097:) 1075:K- 909:. 552:, 543:, 517:, 512:∈ 487:, 483:, 479:, 433:= 347:→ 91:, 58:. 34:, 30:, 1308:) 1306:) 1304:x 1298:) 1296:x 1280:( 1255:/ 1213:( 1068:( 949:e 942:t 935:v 921:. 867:. 754:v 750:u 746:v 742:u 738:v 734:u 730:G 722:0 719:m 698:. 694:} 690:) 687:x 684:( 681:f 671:y 667:: 664:) 657:y 653:, 650:x 647:( 644:m 640:{ 636:g 633:= 630:) 627:y 624:, 621:x 618:( 615:m 605:y 597:x 591:. 577:g 573:. 564:y 556:) 554:y 550:x 548:( 546:m 541:x 537:y 533:x 526:) 524:x 522:( 520:f 514:I 510:x 498:I 491:) 489:g 485:m 481:f 477:I 475:( 471:A 435:p 431:m 424:. 420:p 413:m 400:x 398:( 395:j 393:h 378:x 376:( 373:i 371:g 366:, 364:x 360:n 349:ℝ 345:ℝ 341:f 319:p 316:, 310:, 307:1 304:= 301:j 297:, 294:0 291:= 288:) 285:x 282:( 277:j 273:h 262:m 259:, 253:, 250:1 247:= 244:i 240:, 237:0 231:) 228:x 225:( 220:i 216:g 208:o 205:t 201:t 198:c 195:e 192:j 189:b 186:u 183:s 174:) 171:x 168:( 165:f 157:x 103:. 23:.

Index

Mathematical optimization
mathematics
engineering
computer science
economics
problem
feasible solutions
variables
continuous
discrete
discrete optimization
object
integer
permutation
graph
countable set
continuous optimization
continuous function
constrained problems
standard form
continuous
ℝ
ℝ
objective function
constraints
negating
Combinatorial optimization
combinatorial optimization
set
measure

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

↑