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Conjugate residual method

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1641: 928: 791: 1636:{\displaystyle {\begin{aligned}&\mathbf {x} _{0}:={\text{Some initial guess}}\\&\mathbf {r} _{0}:=\mathbf {M} ^{-1}(\mathbf {b} -\mathbf {Ax} _{0})\\&\mathbf {p} _{0}:=\mathbf {r} _{0}\\&{\text{Iterate, with }}k{\text{ starting at }}0:\\&\qquad \alpha _{k}:={\frac {\mathbf {r} _{k}^{\mathrm {T} }\mathbf {A} \mathbf {r} _{k}}{(\mathbf {Ap} _{k})^{\mathrm {T} }\mathbf {M} ^{-1}\mathbf {Ap} _{k}}}\\&\qquad \mathbf {x} _{k+1}:=\mathbf {x} _{k}+\alpha _{k}\mathbf {p} _{k}\\&\qquad \mathbf {r} _{k+1}:=\mathbf {r} _{k}-\alpha _{k}\mathbf {M} ^{-1}\mathbf {Ap} _{k}\\&\qquad \beta _{k}:={\frac {\mathbf {r} _{k+1}^{\mathrm {T} }\mathbf {A} \mathbf {r} _{k+1}}{\mathbf {r} _{k}^{\mathrm {T} }\mathbf {A} \mathbf {r} _{k}}}\\&\qquad \mathbf {p} _{k+1}:=\mathbf {r} _{k+1}+\beta _{k}\mathbf {p} _{k}\\&\qquad \mathbf {Ap} _{k+1}:=\mathbf {A} \mathbf {r} _{k+1}+\beta _{k}\mathbf {Ap} _{k}\\&\qquad k:=k+1\\\end{aligned}}} 137: 786:{\displaystyle {\begin{aligned}&\mathbf {x} _{0}:={\text{Some initial guess}}\\&\mathbf {r} _{0}:=\mathbf {b} -\mathbf {Ax} _{0}\\&\mathbf {p} _{0}:=\mathbf {r} _{0}\\&{\text{Iterate, with }}k{\text{ starting at }}0:\\&\qquad \alpha _{k}:={\frac {\mathbf {r} _{k}^{\mathrm {T} }\mathbf {Ar} _{k}}{(\mathbf {Ap} _{k})^{\mathrm {T} }\mathbf {Ap} _{k}}}\\&\qquad \mathbf {x} _{k+1}:=\mathbf {x} _{k}+\alpha _{k}\mathbf {p} _{k}\\&\qquad \mathbf {r} _{k+1}:=\mathbf {r} _{k}-\alpha _{k}\mathbf {Ap} _{k}\\&\qquad \beta _{k}:={\frac {\mathbf {r} _{k+1}^{\mathrm {T} }\mathbf {Ar} _{k+1}}{\mathbf {r} _{k}^{\mathrm {T} }\mathbf {Ar} _{k}}}\\&\qquad \mathbf {p} _{k+1}:=\mathbf {r} _{k+1}+\beta _{k}\mathbf {p} _{k}\\&\qquad \mathbf {Ap} _{k+1}:=\mathbf {Ar} _{k+1}+\beta _{k}\mathbf {Ap} _{k}\\&\qquad k:=k+1\end{aligned}}} 75: 933: 142: 1679: 909: 823: 129: 850: 922:
By making a few substitutions and variable changes, a preconditioned conjugate residual method may be derived in the same way as done for the conjugate gradient method:
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must be symmetric positive definite. Note that the residual vector here is different from the residual vector without preconditioning.
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Note: the above algorithm can be transformed so to make only one symmetric matrix-vector multiplication in each iteration.
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has been deemed converged. The only difference between this and the conjugate gradient method is the calculation of
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The conjugate residual method differs from the closely related
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This method is used to solve linear equations of the form
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Given an (arbitrary) initial estimate of the solution
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763: 684: 614: 507: 440: 376: 270: 1156: 1137: 1018: 992: 342: 323: 1: 1697:(2nd ed.), page 194, SIAM. 845:{\displaystyle \alpha _{k}} 26:systems of linear equations 1735: 872:{\displaystyle \beta _{k}} 97:conjugate gradient method 34:conjugate gradient method 18:conjugate residual method 1719:Numerical linear algebra 1068: starting at  256: starting at  1675: 1637: 905: 873: 846: 819: 787: 125: 71: 30:Krylov subspace method 1676: 1638: 906: 874: 847: 820: 788: 126: 84:is an invertible and 72: 1653: 929: 883: 856: 829: 800: 138: 106: 46: 1436: 1392: 1117: 1060:Iterate, with  590: 548: 305: 248:Iterate, with  1671: 1633: 1631: 1418: 1368: 1099: 954:Some initial guess 901: 869: 842: 815: 783: 781: 572: 524: 287: 163:Some initial guess 121: 67: 1703:978-0-89871-534-7 1455: 1198: 1069: 1061: 955: 607: 369: 257: 249: 164: 24:used for solving 1726: 1680: 1678: 1677: 1672: 1670: 1669: 1661: 1642: 1640: 1639: 1634: 1632: 1611: 1607: 1606: 1601: 1592: 1591: 1579: 1578: 1567: 1561: 1553: 1552: 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918:Preconditioning 886: 881: 880: 859: 854: 853: 832: 827: 826: 803: 798: 797: 780: 779: 759: 758: 743: 733: 709: 685: 680: 679: 667: 657: 636: 615: 610: 609: 591: 571: 549: 523: 508: 503: 502: 487: 477: 462: 441: 436: 435: 423: 413: 398: 377: 372: 371: 353: 341: 326: 322: 306: 286: 271: 266: 265: 242: 241: 229: 214: 210: 209: 194: 171: 167: 166: 146: 136: 135: 109: 104: 103: 44: 43: 12: 11: 5: 1732: 1730: 1722: 1721: 1711: 1710: 1707: 1706: 1686: 1683: 1668: 1665: 1660: 1648:preconditioner 1644: 1643: 1628: 1625: 1622: 1619: 1616: 1612: 1610: 1605: 1600: 1597: 1590: 1586: 1582: 1577: 1574: 1571: 1566: 1560: 1556: 1551: 1548: 1545: 1540: 1537: 1531: 1529: 1524: 1519: 1512: 1508: 1504: 1499: 1496: 1493: 1488: 1483: 1478: 1475: 1472: 1467: 1461: 1459: 1451: 1446: 1440: 1433: 1427: 1422: 1413: 1410: 1407: 1402: 1396: 1389: 1383: 1380: 1377: 1372: 1364: 1359: 1355: 1350: 1348: 1343: 1338: 1335: 1328: 1325: 1320: 1313: 1309: 1305: 1300: 1295: 1290: 1285: 1282: 1279: 1274: 1268: 1266: 1261: 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633: 628: 625: 622: 601: 579: 565: 562: 559: 537: 534: 531: 518: 513: 509: 497: 482: 478: 474: 469: 459: 454: 451: 448: 430: 418: 414: 410: 405: 395: 390: 387: 384: 363: 336: 316: 294: 281: 276: 272: 262: 259: 251: 236: 226: 221: 204: 191: 183: 178: 158: 153: 134: 133: 132: 116: 100: 98: 93: 91: 87: 83: 59: 42: 41: 40: 37: 35: 31: 27: 23: 19: 1694: 1645: 921: 913: 795: 101: 94: 92:is nonzero. 89: 81: 79: 38: 17: 15: 1691:Yousef Saad 1685:References 1664:− 1585:β 1507:β 1354:β 1324:− 1308:α 1304:− 1244:α 1175:− 1085:α 1001:− 985:− 861:β 834:α 735:β 659:β 510:β 479:α 475:− 415:α 273:α 192:− 28:. It's a 1713:Category 1701:  88:, and 80:where 1699:ISBN 1646:The 852:and 16:The 1715:: 1693:, 1618::= 1555::= 1482::= 1363::= 1289::= 1225::= 1094::= 1039::= 975::= 950::= 768::= 707::= 634::= 519::= 460::= 396::= 282::= 227::= 184::= 159::= 1705:. 1667:1 1659:M 1627:1 1624:+ 1621:k 1615:k 1604:k 1599:p 1596:A 1589:k 1581:+ 1576:1 1573:+ 1570:k 1565:r 1559:A 1550:1 1547:+ 1544:k 1539:p 1536:A 1523:k 1518:p 1511:k 1503:+ 1498:1 1495:+ 1492:k 1487:r 1477:1 1474:+ 1471:k 1466:p 1450:k 1445:r 1439:A 1432:T 1426:k 1421:r 1412:1 1409:+ 1406:k 1401:r 1395:A 1388:T 1382:1 1379:+ 1376:k 1371:r 1358:k 1342:k 1337:p 1334:A 1327:1 1319:M 1312:k 1299:k 1294:r 1284:1 1281:+ 1278:k 1273:r 1260:k 1255:p 1248:k 1240:+ 1235:k 1230:x 1220:1 1217:+ 1214:k 1209:x 1193:k 1188:p 1185:A 1178:1 1170:M 1162:T 1157:) 1151:k 1146:p 1143:A 1138:( 1131:k 1126:r 1120:A 1113:T 1107:k 1102:r 1089:k 1075:: 1072:0 1064:k 1049:0 1044:r 1034:0 1029:p 1019:) 1014:0 1009:x 1006:A 997:b 993:( 988:1 980:M 970:0 965:r 945:0 940:x 897:k 892:p 889:A 865:k 838:k 811:k 806:x 777:1 774:+ 771:k 765:k 754:k 749:p 746:A 739:k 731:+ 726:1 723:+ 720:k 715:r 712:A 702:1 699:+ 696:k 691:p 688:A 675:k 670:p 663:k 655:+ 650:1 647:+ 644:k 639:r 629:1 626:+ 623:k 618:p 602:k 597:r 594:A 586:T 580:k 575:r 566:1 563:+ 560:k 555:r 552:A 544:T 538:1 535:+ 532:k 527:r 514:k 498:k 493:p 490:A 483:k 470:k 465:r 455:1 452:+ 449:k 444:r 431:k 426:p 419:k 411:+ 406:k 401:x 391:1 388:+ 385:k 380:x 364:k 359:p 356:A 348:T 343:) 337:k 332:p 329:A 324:( 317:k 312:r 309:A 301:T 295:k 290:r 277:k 263:: 260:0 252:k 237:0 232:r 222:0 217:p 205:0 200:x 197:A 188:b 179:0 174:r 154:0 149:x 117:0 112:x 90:b 82:A 64:b 60:= 56:x 51:A

Index

numeric method
systems of linear equations
Krylov subspace method
conjugate gradient method
Hermitian matrix
conjugate gradient method
preconditioner
Yousef Saad
ISBN
978-0-89871-534-7
Category
Numerical linear algebra

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