Knowledge

Active-set method

Source 📝

565:
Consider the problem of Linearly Constrained Convex Quadratic Programming. Under reasonable assumptions (the problem is feasible, the system of constraints is regular at every point, and the quadratic objective is strongly convex), the active-set method terminates after finitely many steps, and
464:, as the solution is not necessarily on one of the edges of the bounding polygon, an estimation of the active set gives us a subset of inequalities to watch while searching the solution, which reduces the complexity of the search. 130: 50:
constraints. The active constraints are then expressed as equality constraints, thereby transforming an inequality-constrained problem into a simpler equality-constrained subproblem.
452:
The active set is particularly important in optimization theory, as it determines which constraints will influence the final result of optimization. For example, in solving the
369: 289: 206: 443: 400: 320: 240: 161: 626: 697: 541: 678: 636: 535: 566:
yields a global solution to the problem. Theoretically, the active-set method may perform a number of iterations exponential in
529: 59: 650: 35: 553: 547: 53:
An optimization problem is defined using an objective function to minimize or maximize, and a set of constraints
47: 43: 461: 325: 631:. Sigma Series in Applied Mathematics. Vol. 3. Berlin: Heldermann Verlag. pp. xlviii+629 pp. 495: 245: 169: 405: 453: 604: 674: 632: 646: 378: 298: 218: 670: 642: 136: 571: 146: 28: 691: 457: 17: 472:
In general an active-set algorithm has the following structure:
504:
a subset of the constraints with negative Lagrange multipliers
488:
the equality problem defined by the active set (approximately)
574:. However, its practical behaviour is typically much better. 628:
Linear complementarity, linear and nonlinear programming
408: 381: 328: 301: 248: 221: 172: 149: 62: 125:{\displaystyle g_{1}(x)\geq 0,\dots ,g_{k}(x)\geq 0} 437: 394: 363: 314: 283: 234: 200: 155: 143:to search for the optimal solution. Given a point 124: 27:"Active set" redirects here. For the band, see 590: 446: 8: 371:Equality constraints are always active. The 42:is an algorithm used to identify the active 665:Nocedal, Jorge; Wright, Stephen J. (2006). 460:that intersect at the solution point. In 426: 413: 407: 386: 380: 346: 333: 327: 306: 300: 266: 253: 247: 226: 220: 177: 171: 148: 101: 67: 61: 605:"Optimization III: Convex Optimization" 583: 542:Sequential linear-quadratic programming 445:that are active at the current point ( 163:in the feasible region, a constraint 7: 698:Optimization algorithms and methods 554:Generalized reduced gradient method 669:(2nd ed.). Berlin, New York: 456:problem, the active set gives the 364:{\displaystyle g_{i}(x_{0})>0.} 25: 521:Methods that can be described as 536:Sequential quadratic programming 402:is made up of those constraints 603:Nemirovsky and Ben-Tal (2023). 476:Find a feasible starting point 432: 419: 352: 339: 284:{\displaystyle g_{i}(x_{0})=0} 272: 259: 201:{\displaystyle g_{i}(x)\geq 0} 189: 183: 113: 107: 79: 73: 1: 530:Successive linear programming 438:{\displaystyle g_{i}(x_{0})} 714: 510:for infeasible constraints 139:, that is, the set of all 26: 591:Nocedal & Wright 2006 447:Nocedal & Wright 2006 548:Reduced gradient method 667:Numerical Optimization 439: 396: 365: 316: 285: 236: 202: 157: 126: 625:Murty, K. G. (1988). 462:quadratic programming 440: 397: 395:{\displaystyle x_{0}} 366: 317: 315:{\displaystyle x_{0}} 286: 237: 235:{\displaystyle x_{0}} 203: 158: 127: 496:Lagrange multipliers 406: 379: 326: 299: 246: 219: 170: 147: 60: 593:, pp. 467–480 523:active-set methods 468:Active-set methods 454:linear programming 435: 392: 361: 312: 281: 232: 198: 153: 122: 680:978-0-387-30303-1 498:of the active set 482:"optimal enough" 156:{\displaystyle x} 40:active-set method 16:(Redirected from 705: 684: 661: 659: 658: 649:. Archived from 612: 611: 609: 600: 594: 588: 449:, p. 308). 444: 442: 441: 436: 431: 430: 418: 417: 401: 399: 398: 393: 391: 390: 370: 368: 367: 362: 351: 350: 338: 337: 321: 319: 318: 313: 311: 310: 290: 288: 287: 282: 271: 270: 258: 257: 241: 239: 238: 233: 231: 230: 207: 205: 204: 199: 182: 181: 162: 160: 159: 154: 135:that define the 131: 129: 128: 123: 106: 105: 72: 71: 34:In mathematical 21: 713: 712: 708: 707: 706: 704: 703: 702: 688: 687: 681: 671:Springer-Verlag 664: 656: 654: 639: 624: 621: 616: 615: 607: 602: 601: 597: 589: 585: 580: 563: 470: 422: 409: 404: 403: 382: 377: 376: 342: 329: 324: 323: 302: 297: 296: 262: 249: 244: 243: 222: 217: 216: 173: 168: 167: 145: 144: 137:feasible region 97: 63: 58: 57: 32: 23: 22: 15: 12: 11: 5: 711: 709: 701: 700: 690: 689: 686: 685: 679: 662: 637: 620: 617: 614: 613: 595: 582: 581: 579: 576: 572:simplex method 562: 559: 558: 557: 551: 545: 539: 533: 519: 518: 513: 512: 511: 505: 499: 489: 477: 469: 466: 434: 429: 425: 421: 416: 412: 389: 385: 360: 357: 354: 349: 345: 341: 336: 332: 309: 305: 280: 277: 274: 269: 265: 261: 256: 252: 229: 225: 209: 208: 197: 194: 191: 188: 185: 180: 176: 152: 133: 132: 121: 118: 115: 112: 109: 104: 100: 96: 93: 90: 87: 84: 81: 78: 75: 70: 66: 29:The Active Set 24: 14: 13: 10: 9: 6: 4: 3: 2: 710: 699: 696: 695: 693: 682: 676: 672: 668: 663: 653:on 2010-04-01 652: 648: 644: 640: 638:3-88538-403-5 634: 630: 629: 623: 622: 618: 606: 599: 596: 592: 587: 584: 577: 575: 573: 569: 560: 555: 552: 549: 546: 543: 540: 537: 534: 531: 528: 527: 526: 524: 517: 514: 509: 506: 503: 500: 497: 493: 490: 487: 484: 483: 481: 478: 475: 474: 473: 467: 465: 463: 459: 455: 450: 448: 427: 423: 414: 410: 387: 383: 374: 358: 355: 347: 343: 334: 330: 307: 303: 294: 278: 275: 267: 263: 254: 250: 227: 223: 214: 195: 192: 186: 178: 174: 166: 165: 164: 150: 142: 138: 119: 116: 110: 102: 98: 94: 91: 88: 85: 82: 76: 68: 64: 56: 55: 54: 51: 49: 45: 41: 37: 30: 19: 666: 655:. Retrieved 651:the original 627: 619:Bibliography 598: 586: 567: 564: 522: 520: 515: 507: 501: 491: 485: 480:repeat until 479: 471: 451: 372: 292: 212: 210: 140: 134: 52: 46:in a set of 39: 36:optimization 33: 570:, like the 561:Performance 458:hyperplanes 44:constraints 657:2010-04-03 578:References 516:end repeat 373:active set 211:is called 48:inequality 18:Active set 525:include: 193:≥ 117:≥ 92:… 83:≥ 692:Category 293:inactive 647:0949214 492:compute 677:  645:  635:  544:(SLQP) 508:search 502:remove 291:, and 213:active 38:, the 608:(PDF) 556:(GRG) 538:(SQP) 532:(SLP) 486:solve 675:ISBN 633:ISBN 550:(RG) 494:the 356:> 375:at 322:if 295:at 242:if 215:at 694:: 673:. 643:MR 641:. 359:0. 683:. 660:. 610:. 568:m 433:) 428:0 424:x 420:( 415:i 411:g 388:0 384:x 353:) 348:0 344:x 340:( 335:i 331:g 308:0 304:x 279:0 276:= 273:) 268:0 264:x 260:( 255:i 251:g 228:0 224:x 196:0 190:) 187:x 184:( 179:i 175:g 151:x 141:x 120:0 114:) 111:x 108:( 103:k 99:g 95:, 89:, 86:0 80:) 77:x 74:( 69:1 65:g 31:. 20:)

Index

Active set
The Active Set
optimization
constraints
inequality
feasible region
Nocedal & Wright 2006
linear programming
hyperplanes
quadratic programming
Lagrange multipliers
Successive linear programming
Sequential quadratic programming
Sequential linear-quadratic programming
Reduced gradient method
Generalized reduced gradient method
simplex method
Nocedal & Wright 2006
"Optimization III: Convex Optimization"
Linear complementarity, linear and nonlinear programming
ISBN
3-88538-403-5
MR
0949214
the original
Springer-Verlag
ISBN
978-0-387-30303-1
Category
Optimization algorithms and methods

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