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Complete orthogonal decomposition

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auxiliary memory to compute, similar to other rank-revealing decompositions. Crucially however, if a row/column is added or removed or the matrix is perturbed by a rank-one matrix, its decomposition can be updated in
1307: 1410: 991: 1232: 963: 1697: 1358: 1198: 1152: 895: 839: 790: 638: 478: 364: 299: 270: 106: 403: 1126: 325: 925: 869: 1412:, is a special case of the UTV decomposition. Computing the SVD is slightly more expensive than the UTV decomposition, but has a stronger rank-revealing property. 569: 436: 153: 764: 1172: 983: 813: 658: 612: 589: 542: 502: 244: 224: 201: 177: 126: 63: 1818: 1843: 666: 1838: 1690: 32:, but typically somewhat cheaper to compute and in particular much cheaper and easier to update when the original matrix is slightly altered. 1240: 1869: 1797: 1683: 1613: 1494: 1469: 1828: 1755: 1588:
Adams, G.; Griffin, M.F.; Stewart, G.W. (1991). "Direction-of-arrival estimation using the rank-revealing URV decomposition".
1864: 1655: 1363: 1787: 1318: 29: 1741: 1792: 1706: 366: 1547: 1092:{\displaystyle {\begin{bmatrix}R_{11}^{*}\\R_{12}^{*}\end{bmatrix}}=V'{\begin{bmatrix}T^{*}\\0\end{bmatrix}}} 1431: 897: 1590:[Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing 1751: 1592:. Proc. Of IEEE Internat. Conf. On Acoustics, Speech, and Signal Processing. pp. 1385-1388 vol.2. 1746: 43: 25: 1725: 1426: 930: 1619: 1570: 1421: 792: 1324: 1177: 1131: 874: 818: 769: 617: 444: 330: 278: 249: 72: 1609: 1490: 1465: 1203: 372: 180: 304: 1761: 1601: 1593: 1562: 1528: 903: 847: 521: 547: 409: 131: 1802: 1314: 749: 484:. Depending on whether a left-triangular or right-triangular matrix is used in place of 1106: 1720: 1157: 968: 841: 798: 643: 597: 574: 527: 487: 229: 209: 186: 162: 156: 111: 66: 48: 40: 17: 1858: 1766: 36: 1574: 1623: 204: 1597: 735:{\displaystyle A\Pi =U{\begin{bmatrix}R_{11}&R_{12}\\0&0\end{bmatrix}}} 1566: 1532: 1782: 1637: 1546:
Fierro, Ricardo D.; Hansen, Per Christian; Hansen, Peter Søren Kirk (1999).
524:: one QR decomposition is applied to the matrix from the left, which yields 1675: 1519:
Decomposition Solver for Hierarchically Semiseparable Representations".
1302:{\displaystyle A=U{\begin{bmatrix}T&0\\0&0\end{bmatrix}}V^{*}} 965:
matrix. One then performs another QR decomposition on the adjoint of
1833: 1823: 1605: 1548:"UTV Tools: Matlab templates for rank-revealing UTV decompositions" 520:
The UTV decomposition is usually computed by means of a pair of
1679: 660:. One first performs a QR decomposition with column pivoting: 1515:
Chandrasekaran, S.; Gu, M.; Pals, T. (January 2006). "A Fast
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block is nonzero, making the decomposition rank-revealing.
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Golub, Gene; van Loon, Charles F. (15 October 1996).
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yields the complete orthogonal (UTV) decomposition:
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Specifically, the complete orthogonal decomposition
1811: 1775: 1734: 1713: 1405:{\displaystyle S_{11}\geq S_{22}\geq \ldots \geq 0} 1464:(Third ed.). Johns Hopkins University Press. 1404: 1352: 1301: 1226: 1192: 1166: 1146: 1120: 1091: 977: 957: 919: 889: 863: 833: 807: 784: 758: 734: 652: 632: 606: 583: 563: 536: 496: 472: 430: 397: 358: 319: 293: 264: 238: 218: 195: 171: 147: 120: 100: 57: 327:, the complete orthogonal decomposition requires 1521:SIAM Journal on Matrix Analysis and Applications 1510: 1508: 1506: 1455: 1453: 1451: 1449: 1447: 544:, another applied from the right, which yields 1691: 8: 1638:"LAPACK – Complete Orthogonal Factorization" 1487:Numerical methods for least squares problems 1698: 1684: 1676: 246:can be chosen such that only its top-left 1384: 1371: 1365: 1344: 1326: 1293: 1253: 1242: 1205: 1179: 1159: 1133: 1108: 1068: 1056: 1031: 1026: 1012: 1007: 995: 993: 970: 932: 911: 905: 876: 855: 849: 820: 800: 771: 751: 706: 694: 682: 668: 645: 619: 599: 576: 555: 549: 529: 489: 464: 446: 411: 386: 374: 347: 332: 306: 280: 251: 231: 211: 188: 164: 139: 133: 113: 92: 74: 50: 1656:"Eigen::CompleteOrthogonalDecomposition" 1200:lower (left) triangular matrix. Setting 1829:Basic Linear Algebra Subprograms (BLAS) 1443: 571:, which "sandwiches" triangular matrix 480:, the decomposition is also known as 7: 1317:is by construction triangular, the 1213: 753: 673: 14: 1660:Eigen 3.3 reference documentation 22:complete orthogonal decomposition 1422:Rank-revealing QR decomposition 952: 940: 425: 416: 392: 379: 353: 337: 1: 1485:Björck, Åke (December 1996). 958:{\displaystyle r\times (n-r)} 1319:singular value decomposition 504:, it is also referred to as 30:singular value decomposition 1886: 1742:System of linear equations 1598:10.1109/icassp.1991.150681 1793:Cache-oblivious algorithm 1533:10.1137/S0895479803436652 1353:{\displaystyle A=USV^{*}} 1193:{\displaystyle r\times r} 1147:{\displaystyle n\times n} 890:{\displaystyle r\times r} 834:{\displaystyle m\times m} 785:{\displaystyle n\times n} 633:{\displaystyle m\times n} 473:{\displaystyle A=UTV^{*}} 367:floating point operations 359:{\displaystyle O(mn^{2})} 294:{\displaystyle m\times n} 265:{\displaystyle r\times r} 101:{\displaystyle A=UTV^{*}} 1870:Numerical linear algebra 1844:General purpose software 1707:Numerical linear algebra 1227:{\displaystyle V=\Pi V'} 398:{\displaystyle O(m^{2})} 226:, the triangular matrix 1567:10.1023/A:1019112103049 1432:Online machine learning 898:upper triangular matrix 320:{\displaystyle m\geq n} 28:. It is similar to the 1406: 1354: 1303: 1228: 1194: 1168: 1148: 1122: 1093: 979: 959: 921: 920:{\displaystyle R_{12}} 891: 865: 864:{\displaystyle R_{11}} 835: 809: 786: 760: 736: 654: 634: 608: 585: 565: 538: 498: 474: 432: 399: 360: 321: 295: 266: 240: 220: 197: 173: 149: 122: 102: 59: 1865:Matrix decompositions 1839:Specialized libraries 1752:Matrix multiplication 1747:Matrix decompositions 1407: 1355: 1304: 1229: 1195: 1169: 1149: 1123: 1094: 980: 960: 922: 892: 866: 836: 810: 787: 761: 737: 655: 635: 609: 586: 566: 564:{\displaystyle V^{*}} 539: 499: 475: 441:Because of its form, 433: 431:{\displaystyle O(mn)} 400: 361: 322: 296: 275:For a matrix of size 267: 241: 221: 198: 174: 150: 148:{\displaystyle V^{*}} 123: 103: 60: 1555:Numerical Algorithms 1364: 1325: 1241: 1204: 1178: 1158: 1132: 1107: 992: 969: 931: 904: 875: 848: 819: 799: 770: 759:{\displaystyle \Pi } 750: 667: 644: 618: 598: 575: 548: 528: 488: 445: 410: 373: 331: 305: 279: 250: 230: 210: 187: 163: 132: 112: 73: 49: 26:matrix decomposition 1726:Numerical stability 1462:Matrix Computations 1427:Schur decomposition 1154:unitary matrix and 1036: 1017: 69:of three matrices, 1402: 1350: 1299: 1283: 1224: 1190: 1164: 1144: 1121:{\displaystyle V'} 1118: 1089: 1083: 1039: 1022: 1003: 975: 955: 917: 887: 861: 831: 805: 793:permutation matrix 782: 756: 732: 726: 650: 630: 604: 581: 561: 534: 494: 470: 428: 395: 356: 317: 291: 262: 236: 216: 193: 169: 145: 118: 98: 55: 1852: 1851: 1167:{\displaystyle T} 978:{\displaystyle R} 808:{\displaystyle U} 653:{\displaystyle r} 607:{\displaystyle A} 584:{\displaystyle T} 537:{\displaystyle U} 522:QR decompositions 510:URV decomposition 506:ULV decomposition 497:{\displaystyle T} 482:UTV decomposition 239:{\displaystyle T} 219:{\displaystyle r} 196:{\displaystyle A} 181:triangular matrix 172:{\displaystyle T} 121:{\displaystyle U} 58:{\displaystyle A} 1877: 1762:Matrix splitting 1700: 1693: 1686: 1677: 1670: 1669: 1667: 1666: 1652: 1646: 1645: 1634: 1628: 1627: 1585: 1579: 1578: 1561:(2/3): 165–194. 1552: 1543: 1537: 1536: 1512: 1501: 1500: 1482: 1476: 1475: 1457: 1411: 1409: 1408: 1403: 1389: 1388: 1376: 1375: 1359: 1357: 1356: 1351: 1349: 1348: 1308: 1306: 1305: 1300: 1298: 1297: 1288: 1287: 1233: 1231: 1230: 1225: 1223: 1199: 1197: 1196: 1191: 1173: 1171: 1170: 1165: 1153: 1151: 1150: 1145: 1127: 1125: 1124: 1119: 1117: 1098: 1096: 1095: 1090: 1088: 1087: 1073: 1072: 1055: 1044: 1043: 1035: 1030: 1016: 1011: 984: 982: 981: 976: 964: 962: 961: 956: 926: 924: 923: 918: 916: 915: 896: 894: 893: 888: 870: 868: 867: 862: 860: 859: 840: 838: 837: 832: 814: 812: 811: 806: 791: 789: 788: 783: 765: 763: 762: 757: 741: 739: 738: 733: 731: 730: 711: 710: 699: 698: 659: 657: 656: 651: 639: 637: 636: 631: 613: 611: 610: 605: 590: 588: 587: 582: 570: 568: 567: 562: 560: 559: 543: 541: 540: 535: 512:, respectively. 503: 501: 500: 495: 479: 477: 476: 471: 469: 468: 437: 435: 434: 429: 404: 402: 401: 396: 391: 390: 365: 363: 362: 357: 352: 351: 326: 324: 323: 318: 300: 298: 297: 292: 271: 269: 268: 263: 245: 243: 242: 237: 225: 223: 222: 217: 202: 200: 199: 194: 183:. For a matrix 178: 176: 175: 170: 157:unitary matrices 154: 152: 151: 146: 144: 143: 127: 125: 124: 119: 107: 105: 104: 99: 97: 96: 64: 62: 61: 56: 1885: 1884: 1880: 1879: 1878: 1876: 1875: 1874: 1855: 1854: 1853: 1848: 1807: 1803:Multiprocessing 1771: 1767:Sparse problems 1730: 1709: 1704: 1674: 1673: 1664: 1662: 1654: 1653: 1649: 1636: 1635: 1631: 1616: 1587: 1586: 1582: 1550: 1545: 1544: 1540: 1514: 1513: 1504: 1497: 1484: 1483: 1479: 1472: 1459: 1458: 1445: 1440: 1418: 1380: 1367: 1362: 1361: 1340: 1323: 1322: 1315:diagonal matrix 1289: 1282: 1281: 1276: 1270: 1269: 1264: 1254: 1239: 1238: 1216: 1202: 1201: 1176: 1175: 1156: 1155: 1130: 1129: 1110: 1105: 1104: 1082: 1081: 1075: 1074: 1064: 1057: 1048: 1038: 1037: 1019: 1018: 996: 990: 989: 967: 966: 929: 928: 907: 902: 901: 873: 872: 851: 846: 845: 817: 816: 797: 796: 768: 767: 748: 747: 725: 724: 719: 713: 712: 702: 700: 690: 683: 665: 664: 642: 641: 640:matrix of rank 616: 615: 596: 595: 591:in the middle. 573: 572: 551: 546: 545: 526: 525: 518: 486: 485: 460: 443: 442: 408: 407: 382: 371: 370: 343: 329: 328: 303: 302: 277: 276: 248: 247: 228: 227: 208: 207: 185: 184: 161: 160: 135: 130: 129: 110: 109: 88: 71: 70: 47: 46: 12: 11: 5: 1883: 1881: 1873: 1872: 1867: 1857: 1856: 1850: 1849: 1847: 1846: 1841: 1836: 1831: 1826: 1821: 1815: 1813: 1809: 1808: 1806: 1805: 1800: 1795: 1790: 1785: 1779: 1777: 1773: 1772: 1770: 1769: 1764: 1759: 1749: 1744: 1738: 1736: 1732: 1731: 1729: 1728: 1723: 1721:Floating point 1717: 1715: 1711: 1710: 1705: 1703: 1702: 1695: 1688: 1680: 1672: 1671: 1647: 1629: 1614: 1580: 1538: 1527:(3): 603–622. 1502: 1495: 1477: 1470: 1442: 1441: 1439: 1436: 1435: 1434: 1429: 1424: 1417: 1414: 1401: 1398: 1395: 1392: 1387: 1383: 1379: 1374: 1370: 1347: 1343: 1339: 1336: 1333: 1330: 1311: 1310: 1296: 1292: 1286: 1280: 1277: 1275: 1272: 1271: 1268: 1265: 1263: 1260: 1259: 1257: 1252: 1249: 1246: 1222: 1219: 1215: 1212: 1209: 1189: 1186: 1183: 1163: 1143: 1140: 1137: 1116: 1113: 1101: 1100: 1086: 1080: 1077: 1076: 1071: 1067: 1063: 1062: 1060: 1054: 1051: 1047: 1042: 1034: 1029: 1025: 1021: 1020: 1015: 1010: 1006: 1002: 1001: 999: 974: 954: 951: 948: 945: 942: 939: 936: 914: 910: 886: 883: 880: 858: 854: 842:unitary matrix 830: 827: 824: 804: 781: 778: 775: 755: 744: 743: 729: 723: 720: 718: 715: 714: 709: 705: 701: 697: 693: 689: 688: 686: 681: 678: 675: 672: 649: 629: 626: 623: 603: 580: 558: 554: 533: 517: 514: 493: 467: 463: 459: 456: 453: 450: 427: 424: 421: 418: 415: 394: 389: 385: 381: 378: 355: 350: 346: 342: 339: 336: 316: 313: 310: 290: 287: 284: 261: 258: 255: 235: 215: 192: 168: 142: 138: 117: 95: 91: 87: 84: 81: 78: 54: 18:linear algebra 13: 10: 9: 6: 4: 3: 2: 1882: 1871: 1868: 1866: 1863: 1862: 1860: 1845: 1842: 1840: 1837: 1835: 1832: 1830: 1827: 1825: 1822: 1820: 1817: 1816: 1814: 1810: 1804: 1801: 1799: 1796: 1794: 1791: 1789: 1786: 1784: 1781: 1780: 1778: 1774: 1768: 1765: 1763: 1760: 1757: 1753: 1750: 1748: 1745: 1743: 1740: 1739: 1737: 1733: 1727: 1724: 1722: 1719: 1718: 1716: 1712: 1708: 1701: 1696: 1694: 1689: 1687: 1682: 1681: 1678: 1661: 1657: 1651: 1648: 1643: 1639: 1633: 1630: 1625: 1621: 1617: 1615:0-7803-0003-3 1611: 1607: 1603: 1599: 1595: 1591: 1584: 1581: 1576: 1572: 1568: 1564: 1560: 1556: 1549: 1542: 1539: 1534: 1530: 1526: 1522: 1518: 1511: 1509: 1507: 1503: 1498: 1496:0-89871-360-9 1492: 1488: 1481: 1478: 1473: 1471:0-8018-5414-8 1467: 1463: 1456: 1454: 1452: 1450: 1448: 1444: 1437: 1433: 1430: 1428: 1425: 1423: 1420: 1419: 1415: 1413: 1399: 1396: 1393: 1390: 1385: 1381: 1377: 1372: 1368: 1345: 1341: 1337: 1334: 1331: 1328: 1320: 1316: 1294: 1290: 1284: 1278: 1273: 1266: 1261: 1255: 1250: 1247: 1244: 1237: 1236: 1235: 1220: 1217: 1210: 1207: 1187: 1184: 1181: 1161: 1141: 1138: 1135: 1114: 1111: 1084: 1078: 1069: 1065: 1058: 1052: 1049: 1045: 1040: 1032: 1027: 1023: 1013: 1008: 1004: 997: 988: 987: 986: 972: 949: 946: 943: 937: 934: 912: 908: 899: 884: 881: 878: 856: 852: 843: 828: 825: 822: 802: 794: 779: 776: 773: 727: 721: 716: 707: 703: 695: 691: 684: 679: 676: 670: 663: 662: 661: 647: 627: 624: 621: 601: 592: 578: 556: 552: 531: 523: 515: 513: 511: 507: 491: 483: 465: 461: 457: 454: 451: 448: 439: 422: 419: 413: 387: 383: 376: 368: 348: 344: 340: 334: 314: 311: 308: 288: 285: 282: 273: 259: 256: 253: 233: 213: 206: 190: 182: 166: 158: 140: 136: 115: 93: 89: 85: 82: 79: 76: 68: 52: 45: 42: 39:an arbitrary 38: 33: 31: 27: 23: 19: 1714:Key concepts 1663:. Retrieved 1659: 1650: 1641: 1632: 1589: 1583: 1558: 1554: 1541: 1524: 1520: 1516: 1486: 1480: 1461: 1312: 1102: 745: 593: 519: 516:Construction 509: 505: 481: 440: 438:operations. 274: 34: 21: 15: 301:, assuming 1859:Categories 1756:algorithms 1665:2023-08-07 1642:netlib.org 1438:References 1313:Since any 37:factorizes 1783:CPU cache 1397:≥ 1394:… 1391:≥ 1378:≥ 1346:∗ 1295:∗ 1214:Π 1185:× 1139:× 1070:∗ 1033:∗ 1014:∗ 947:− 938:× 882:× 826:× 777:× 754:Π 674:Π 625:× 557:∗ 466:∗ 312:≥ 286:× 257:× 141:∗ 94:∗ 1812:Software 1776:Hardware 1735:Problems 1606:1903/555 1575:19861950 1489:. SIAM. 1416:See also 1360:, where 1221:′ 1115:′ 1053:′ 108:, where 1624:9201732 67:product 65:into a 41:complex 1834:LAPACK 1824:MATLAB 1622:  1612:  1573:  1493:  1468:  1174:is an 1103:where 746:where 44:matrix 20:, the 1819:ATLAS 1620:S2CID 1571:S2CID 1551:(PDF) 1128:is a 927:is a 871:is a 815:is a 766:is a 614:be a 179:is a 24:is a 1798:SIMD 1610:ISBN 1491:ISBN 1466:ISBN 900:and 594:Let 369:and 205:rank 159:and 155:are 128:and 1788:TLB 1602:hdl 1594:doi 1563:doi 1529:doi 1517:ULV 508:or 203:of 16:In 1861:: 1658:. 1640:. 1618:. 1608:. 1600:. 1569:. 1559:20 1557:. 1553:. 1525:28 1523:. 1505:^ 1446:^ 1386:22 1373:11 1321:, 1028:12 1009:11 985:: 913:12 857:11 844:, 795:, 708:12 696:11 1758:) 1754:( 1699:e 1692:t 1685:v 1668:. 1644:. 1626:. 1604:: 1596:: 1577:. 1565:: 1535:. 1531:: 1499:. 1474:. 1400:0 1382:S 1369:S 1342:V 1338:S 1335:U 1332:= 1329:A 1309:. 1291:V 1285:] 1279:0 1274:0 1267:0 1262:T 1256:[ 1251:U 1248:= 1245:A 1218:V 1211:= 1208:V 1188:r 1182:r 1162:T 1142:n 1136:n 1112:V 1099:, 1085:] 1079:0 1066:T 1059:[ 1050:V 1046:= 1041:] 1024:R 1005:R 998:[ 973:R 953:) 950:r 944:n 941:( 935:r 909:R 885:r 879:r 853:R 829:m 823:m 803:U 780:n 774:n 742:, 728:] 722:0 717:0 704:R 692:R 685:[ 680:U 677:= 671:A 648:r 628:n 622:m 602:A 579:T 553:V 532:U 492:T 462:V 458:T 455:U 452:= 449:A 426:) 423:n 420:m 417:( 414:O 393:) 388:2 384:m 380:( 377:O 354:) 349:2 345:n 341:m 338:( 335:O 315:n 309:m 289:n 283:m 260:r 254:r 234:T 214:r 191:A 167:T 137:V 116:U 90:V 86:T 83:U 80:= 77:A 53:A

Index

linear algebra
matrix decomposition
singular value decomposition
factorizes
complex
matrix
product
unitary matrices
triangular matrix
rank
floating point operations
QR decompositions
permutation matrix
unitary matrix
upper triangular matrix
diagonal matrix
singular value decomposition
Rank-revealing QR decomposition
Schur decomposition
Online machine learning





ISBN
0-8018-5414-8
ISBN
0-89871-360-9

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