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Bartels–Stewart algorithm

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are sparse or structured, so that linear solves and matrix vector multiplies involving them are efficient, iterative algorithms can potentially perform better. These include projection-based methods, which use
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The subroutines required for the Hessenberg-Schur variant of the Bartels–Stewart algorithm are implemented in the SLICOT library. These are used in the MATLAB control system toolbox.
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and S. Nash introduced an improved version of the algorithm, known as the Hessenberg–Schur algorithm. It remains a standard approach for solving
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method that could be systematically applied to solve such equations. The algorithm works by using the
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Golub, G.; Nash, S.; Loan, C. Van (1979). "A Hessenberg–Schur method for the problem AX + XB= C".
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into a triangular system that can then be solved using forward or backward substitution. In 1979,
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that can be solved using forward substitution. The advantage of this approach is that
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Simoncini, V. (2016). "Computational Methods for Linear Matrix Equations".
1060: 1820: 1803: 1917: 68:. Developed by R.H. Bartels and G.W. Stewart in 1971, it was the first 1908: 2051: 1872: 833:{\displaystyle (R-s_{kk}I)y_{k}=f_{k}+\sum _{j=k+1}^{n}s_{kj}y_{j},} 485:
are block-upper triangular matrices, with diagonal blocks of size
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cost of the Bartels–Stewart algorithm can be prohibitive. When
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will also be symmetric. This symmetry can be exploited so that
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has a unique solution. The Bartels–Stewart algorithm computes
1804:"Solution of the matrix equation AX + XB = C [F4]" 1582:
flops required to compute the real Schur decomposition of
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The Hessenberg–Schur algorithm replaces the decomposition
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should be concatenated and solved for simultaneously.
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is found more efficiently in step 3 of the algorithm.
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This leads to a system of the form 1054: 261:are distinct from the eigenvalues of 241:, and assume that the eigenvalues of 16:Algorithm in numerical linear algebra 1845: 1843: 1841: 1839: 1797: 1795: 13: 1627: 339:by applying the following steps: 14: 2160: 1836: 1792: 1716:iterations, methods based on the 1357:in step 1 with the decomposition 1210:Simplifications and special cases 27:is used to numerically solve the 1067:in step 1 require approximately 584:3. Solve the simplified system 189: 1109:{\displaystyle 10(m^{3}+n^{3})} 948:{\displaystyle s_{k-1,k}\neq 0} 1888: 1718:alternating direction implicit 1658: 1632: 1529: 1515: 1314:The Hessenberg–Schur algorithm 1193: 1161: 1152: 1126: 1103: 1077: 994: 962: 748: 723: 435:{\displaystyle S=V^{T}B^{T}V.} 186:is of small to moderate size. 1: 1785: 281:. Then, the matrix equation 7: 1606:Software and implementation 1458:{\displaystyle HY-YS^{T}=F} 1270:is symmetric, the solution 706:{\displaystyle s_{k-1,k}=0} 622:{\displaystyle RY-YS^{T}=F} 10: 2165: 2011:System of linear equations 1545:{\displaystyle (5/3)m^{3}} 1214:In the special case where 1045:{\displaystyle X=UYV^{T}.} 575:{\displaystyle F=U^{T}CV.} 383:{\displaystyle R=U^{T}AU,} 2080: 2062:Cache-oblivious algorithm 2044: 2003: 1982: 1808:Communications of the ACM 1497:{\displaystyle H=Q^{T}AQ} 1389:{\displaystyle H=Q^{T}AQ} 1350:{\displaystyle R=U^{T}AU} 1065:real Schur decompositions 661:{\displaystyle Y=U^{T}XV} 530:{\displaystyle 2\times 2} 504:{\displaystyle 1\times 1} 344:real Schur decompositions 74:real Schur decompositions 29:Sylvester matrix equation 25:Bartels–Stewart algorithm 2149:Numerical linear algebra 2113:General purpose software 1976:Numerical linear algebra 1864:10.1109/TAC.1979.1102170 1243:{\displaystyle B=-A^{T}} 21:numerical linear algebra 1775:{\displaystyle AX-XB=C} 1722:low rank approximations 1618:For large systems, the 1575:{\displaystyle 10m^{3}} 1552:flops, compared to the 1506:Householder reflections 1418:upper-Hessenberg matrix 312:{\displaystyle AX-XB=C} 147:{\displaystyle AX-XB=C} 61:{\displaystyle AX-XB=C} 1776: 1738: 1705: 1685: 1665: 1614:Alternative approaches 1596: 1576: 1546: 1498: 1459: 1410: 1390: 1351: 1304: 1284: 1264: 1244: 1200: 1110: 1046: 1001: 949: 904: 884: 864: 834: 803: 707: 662: 623: 576: 531: 505: 479: 459: 436: 384: 333: 313: 275: 255: 235: 180: 148: 110: 90: 62: 2108:Specialized libraries 2021:Matrix multiplication 2016:Matrix decompositions 1821:10.1145/361573.361582 1777: 1739: 1706: 1686: 1666: 1597: 1577: 1547: 1499: 1460: 1411: 1391: 1352: 1305: 1285: 1265: 1245: 1201: 1111: 1047: 1002: 950: 905: 885: 865: 863:{\displaystyle y_{k}} 835: 777: 708: 663: 624: 577: 532: 506: 480: 460: 437: 385: 334: 314: 276: 256: 236: 181: 149: 111: 91: 63: 1748: 1728: 1695: 1675: 1622: 1586: 1556: 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89:{\displaystyle A} 2156: 2031:Matrix splitting 1969: 1962: 1955: 1946: 1945: 1939: 1938: 1920: 1892: 1886: 1885: 1875: 1847: 1834: 1833: 1823: 1799: 1781: 1779: 1778: 1773: 1743: 1741: 1740: 1735: 1710: 1708: 1707: 1702: 1690: 1688: 1687: 1682: 1670: 1668: 1667: 1662: 1657: 1656: 1644: 1643: 1631: 1630: 1601: 1599: 1598: 1593: 1581: 1579: 1578: 1573: 1571: 1570: 1551: 1549: 1548: 1543: 1541: 1540: 1525: 1503: 1501: 1500: 1495: 1487: 1486: 1464: 1462: 1461: 1456: 1448: 1447: 1415: 1413: 1412: 1407: 1395: 1393: 1392: 1387: 1379: 1378: 1356: 1354: 1353: 1348: 1340: 1339: 1309: 1307: 1306: 1301: 1289: 1287: 1286: 1281: 1269: 1267: 1266: 1261: 1249: 1247: 1246: 1241: 1239: 1238: 1205: 1203: 1202: 1197: 1192: 1191: 1176: 1175: 1151: 1150: 1138: 1137: 1115: 1113: 1112: 1107: 1102: 1101: 1089: 1088: 1051: 1049: 1048: 1043: 1038: 1037: 1006: 1004: 1003: 1000:{\displaystyle } 998: 993: 992: 980: 979: 954: 952: 951: 946: 938: 937: 909: 907: 906: 901: 889: 887: 886: 881: 869: 867: 866: 861: 859: 858: 839: 837: 836: 831: 826: 825: 816: 815: 802: 797: 773: 772: 760: 759: 744: 743: 712: 710: 709: 704: 696: 695: 667: 665: 664: 659: 651: 650: 628: 626: 625: 620: 612: 611: 581: 579: 578: 573: 562: 561: 536: 534: 533: 528: 510: 508: 507: 502: 484: 482: 481: 476: 464: 462: 461: 456: 441: 439: 438: 433: 425: 424: 415: 414: 389: 387: 386: 381: 370: 369: 338: 336: 335: 330: 318: 316: 315: 310: 280: 278: 277: 272: 260: 258: 257: 252: 240: 238: 237: 232: 230: 229: 218: 185: 183: 182: 177: 153: 151: 150: 145: 115: 113: 112: 107: 95: 93: 92: 87: 67: 65: 64: 59: 2164: 2163: 2159: 2158: 2157: 2155: 2154: 2153: 2124: 2123: 2122: 2117: 2076: 2072:Multiprocessing 2040: 2036:Sparse problems 1999: 1978: 1973: 1943: 1942: 1893: 1889: 1848: 1837: 1800: 1793: 1788: 1749: 1746: 1745: 1729: 1726: 1725: 1714:Krylov subspace 1696: 1693: 1692: 1676: 1673: 1672: 1652: 1648: 1639: 1635: 1626: 1625: 1623: 1620: 1619: 1616: 1608: 1587: 1584: 1583: 1566: 1562: 1557: 1554: 1553: 1536: 1532: 1521: 1513: 1510: 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2139:Control theory 2136: 2119: 2118: 2116: 2115: 2110: 2105: 2100: 2095: 2090: 2084: 2082: 2078: 2077: 2075: 2074: 2069: 2064: 2059: 2054: 2048: 2046: 2042: 2041: 2039: 2038: 2033: 2028: 2018: 2013: 2007: 2005: 2001: 2000: 1998: 1997: 1992: 1990:Floating point 1986: 1984: 1980: 1979: 1972: 1971: 1964: 1957: 1949: 1941: 1940: 1903:(3): 377–441. 1887: 1858:(6): 909–913. 1835: 1814:(9): 820–826. 1790: 1789: 1787: 1784: 1771: 1768: 1765: 1762: 1759: 1756: 1753: 1733: 1700: 1680: 1660: 1655: 1651: 1647: 1642: 1638: 1634: 1629: 1615: 1612: 1607: 1604: 1591: 1569: 1565: 1561: 1539: 1535: 1531: 1528: 1524: 1520: 1517: 1493: 1490: 1485: 1481: 1477: 1474: 1454: 1451: 1446: 1442: 1438: 1435: 1432: 1429: 1405: 1385: 1382: 1377: 1373: 1369: 1366: 1346: 1343: 1338: 1334: 1330: 1327: 1315: 1312: 1299: 1279: 1259: 1237: 1233: 1229: 1226: 1223: 1211: 1208: 1195: 1190: 1186: 1182: 1179: 1174: 1170: 1166: 1163: 1160: 1157: 1154: 1149: 1145: 1141: 1136: 1132: 1128: 1125: 1105: 1100: 1096: 1092: 1087: 1083: 1079: 1076: 1056: 1053: 1041: 1036: 1032: 1028: 1025: 1022: 1019: 996: 991: 987: 983: 978: 975: 972: 968: 964: 944: 941: 936: 933: 930: 927: 924: 920: 899: 879: 857: 853: 841: 840: 829: 824: 820: 814: 811: 807: 801: 796: 793: 790: 787: 784: 780: 776: 771: 767: 763: 758: 754: 750: 747: 742: 739: 735: 731: 728: 725: 702: 699: 694: 691: 688: 685: 682: 678: 657: 654: 649: 645: 641: 638: 618: 615: 610: 606: 602: 599: 596: 593: 571: 568: 565: 560: 556: 552: 549: 526: 523: 520: 500: 497: 494: 474: 454: 443: 442: 431: 428: 423: 419: 413: 409: 405: 402: 391: 390: 379: 376: 373: 368: 364: 360: 357: 342:1.Compute the 328: 308: 305: 302: 299: 296: 293: 290: 270: 250: 228: 225: 222: 217: 212: 209: 206: 203: 191: 188: 175: 143: 140: 137: 134: 131: 128: 125: 105: 85: 57: 54: 51: 48: 45: 42: 39: 15: 9: 6: 4: 3: 2: 2161: 2150: 2147: 2145: 2142: 2140: 2137: 2135: 2132: 2131: 2129: 2114: 2111: 2109: 2106: 2104: 2101: 2099: 2096: 2094: 2091: 2089: 2086: 2085: 2083: 2079: 2073: 2070: 2068: 2065: 2063: 2060: 2058: 2055: 2053: 2050: 2049: 2047: 2043: 2037: 2034: 2032: 2029: 2026: 2022: 2019: 2017: 2014: 2012: 2009: 2008: 2006: 2002: 1996: 1993: 1991: 1988: 1987: 1985: 1981: 1977: 1970: 1965: 1963: 1958: 1956: 1951: 1950: 1947: 1936: 1932: 1928: 1924: 1919: 1914: 1910: 1906: 1902: 1898: 1891: 1883: 1879: 1874: 1869: 1865: 1861: 1857: 1853: 1846: 1844: 1842: 1840: 1831: 1827: 1822: 1817: 1813: 1809: 1805: 1798: 1796: 1791: 1783: 1769: 1766: 1763: 1760: 1757: 1754: 1751: 1744:when solving 1731: 1723: 1719: 1715: 1698: 1678: 1653: 1649: 1645: 1640: 1636: 1611: 1603: 1589: 1567: 1563: 1559: 1537: 1533: 1526: 1522: 1518: 1508:at a cost of 1507: 1491: 1488: 1483: 1479: 1475: 1472: 1452: 1449: 1444: 1440: 1436: 1433: 1430: 1427: 1419: 1403: 1383: 1380: 1375: 1371: 1367: 1364: 1344: 1341: 1336: 1332: 1328: 1325: 1311: 1297: 1277: 1257: 1235: 1231: 1227: 1224: 1221: 1207: 1188: 1184: 1180: 1177: 1172: 1168: 1164: 1158: 1155: 1147: 1143: 1139: 1134: 1130: 1123: 1098: 1094: 1090: 1085: 1081: 1074: 1066: 1062: 1052: 1039: 1034: 1030: 1026: 1023: 1020: 1017: 1008: 989: 985: 981: 976: 973: 970: 966: 942: 939: 934: 931: 928: 925: 922: 918: 897: 890:th column of 877: 855: 851: 827: 822: 818: 812: 809: 805: 799: 794: 791: 788: 785: 782: 778: 774: 769: 765: 761: 756: 752: 745: 740: 737: 733: 729: 726: 716: 715: 714: 700: 697: 692: 689: 686: 683: 680: 676: 655: 652: 647: 643: 639: 636: 616: 613: 608: 604: 600: 597: 594: 591: 582: 569: 566: 563: 558: 554: 550: 547: 538: 524: 521: 518: 498: 495: 492: 472: 452: 445:The matrices 429: 426: 421: 417: 411: 407: 403: 400: 393: 392: 377: 374: 371: 366: 362: 358: 355: 348: 347: 346: 345: 340: 326: 306: 303: 300: 297: 294: 291: 288: 268: 248: 226: 223: 220: 210: 207: 204: 201: 190:The algorithm 187: 173: 165: 161: 157: 141: 138: 135: 132: 129: 126: 123: 116:to transform 103: 83: 75: 71: 55: 52: 49: 46: 43: 40: 37: 30: 26: 22: 1983:Key concepts 1918:11585/586011 1900: 1896: 1890: 1855: 1851: 1811: 1807: 1617: 1609: 1317: 1213: 1061:QR algorithm 1058: 1009: 842: 583: 539: 444: 341: 193: 24: 18: 1897:SIAM Review 160:C. Van Loan 2134:Algorithms 2128:Categories 2025:algorithms 1786:References 1059:Using the 955:, columns 2052:CPU cache 1927:0036-1445 1882:0018-9286 1873:1813/7472 1830:0001-0782 1758:− 1434:− 1228:− 982:∣ 974:− 940:≠ 926:− 779:∑ 730:− 684:− 598:− 522:× 496:× 295:− 224:× 211:∈ 130:− 44:− 2144:Matrices 2081:Software 2045:Hardware 2004:Problems 1935:17271167 1396:, where 629:, where 156:G. Golub 1010:4. Set 910:. When 870:is the 713:, then 540:2. Set 2103:LAPACK 2093:MATLAB 1933:  1925:  1880:  1828:  1416:is an 1063:, the 843:where 23:, the 2088:ATLAS 1931:S2CID 166:when 2067:SIMD 1923:ISSN 1878:ISSN 1826:ISSN 1691:and 1250:and 465:and 194:Let 96:and 2057:TLB 1913:hdl 1905:doi 1868:hdl 1860:doi 1816:doi 1782:. 1724:to 1602:. 1206:. 1159:2.5 511:or 76:of 19:In 2130:: 1929:. 1921:. 1911:. 1901:58 1899:. 1876:. 1866:. 1856:24 1854:. 1838:^ 1824:. 1812:15 1810:. 1806:. 1794:^ 1560:10 1124:10 1075:10 537:. 158:, 2027:) 2023:( 1968:e 1961:t 1954:v 1937:. 1915:: 1907:: 1884:. 1870:: 1862:: 1832:. 1818:: 1770:C 1767:= 1764:B 1761:X 1755:X 1752:A 1732:X 1699:B 1679:A 1659:) 1654:3 1650:n 1646:+ 1641:3 1637:m 1633:( 1628:O 1590:A 1568:3 1564:m 1538:3 1534:m 1530:) 1527:3 1523:/ 1519:5 1516:( 1492:Q 1489:A 1484:T 1480:Q 1476:= 1473:H 1453:F 1450:= 1445:T 1441:S 1437:Y 1431:Y 1428:H 1404:H 1384:Q 1381:A 1376:T 1372:Q 1368:= 1365:H 1345:U 1342:A 1337:T 1333:U 1329:= 1326:R 1298:Y 1278:X 1258:C 1236:T 1232:A 1225:= 1222:B 1194:) 1189:2 1185:m 1181:n 1178:+ 1173:2 1169:n 1165:m 1162:( 1156:+ 1153:) 1148:3 1144:n 1140:+ 1135:3 1131:m 1127:( 1104:) 1099:3 1095:n 1091:+ 1086:3 1082:m 1078:( 1040:. 1035:T 1031:V 1027:Y 1024:U 1021:= 1018:X 995:] 990:k 986:y 977:1 971:k 967:y 963:[ 943:0 935:k 932:, 929:1 923:k 919:s 898:Y 878:k 856:k 852:y 828:, 823:j 819:y 813:j 810:k 806:s 800:n 795:1 792:+ 789:k 786:= 783:j 775:+ 770:k 766:f 762:= 757:k 753:y 749:) 746:I 741:k 738:k 734:s 727:R 724:( 701:0 698:= 693:k 690:, 687:1 681:k 677:s 656:V 653:X 648:T 644:U 640:= 637:Y 617:F 614:= 609:T 605:S 601:Y 595:Y 592:R 570:. 567:V 564:C 559:T 555:U 551:= 548:F 525:2 519:2 499:1 493:1 473:S 453:R 430:. 427:V 422:T 418:B 412:T 408:V 404:= 401:S 378:, 375:U 372:A 367:T 363:U 359:= 356:R 327:X 307:C 304:= 301:B 298:X 292:X 289:A 269:B 249:A 227:n 221:m 216:R 208:C 205:, 202:X 174:X 142:C 139:= 136:B 133:X 127:X 124:A 104:B 84:A 56:C 53:= 50:B 47:X 41:X 38:A

Index

numerical linear algebra
Sylvester matrix equation
numerically stable
real Schur decompositions
G. Golub
C. Van Loan
Sylvester equations
real Schur decompositions
QR algorithm
real Schur decompositions
upper-Hessenberg matrix
Householder reflections
Krylov subspace
alternating direction implicit
low rank approximations


"Solution of the matrix equation AX + XB = C [F4]"
doi
10.1145/361573.361582
ISSN
0001-0782




doi
10.1109/TAC.1979.1102170
hdl
1813/7472

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