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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".
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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
1815:"Solution of the matrix equation AX + XB = C [F4]" 1593:
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 1065: 272:are distinct from the eigenvalues of 252:, and assume that the eigenvalues of 27:Algorithm in numerical linear algebra 1856: 1854: 1852: 1850: 1808: 1806: 24: 1638: 350:by applying the following steps: 25: 2171: 1847: 1803: 1727:iterations, methods based on the 1368:in step 1 with the decomposition 1221:Simplifications and special cases 38:is used to numerically solve the 1078:in step 1 require approximately 595:3. Solve the simplified system 200: 1120:{\displaystyle 10(m^{3}+n^{3})} 959:{\displaystyle s_{k-1,k}\neq 0} 1899: 1729:alternating direction implicit 1669: 1643: 1540: 1526: 1325:The Hessenberg–Schur algorithm 1204: 1172: 1163: 1137: 1114: 1088: 1005: 973: 759: 734: 446:{\displaystyle S=V^{T}B^{T}V.} 197:is of small to moderate size. 13: 1: 1796: 292:. Then, the matrix equation 7: 1617:Software and implementation 1469:{\displaystyle HY-YS^{T}=F} 1281:is symmetric, the solution 717:{\displaystyle s_{k-1,k}=0} 633:{\displaystyle RY-YS^{T}=F} 10: 2176: 2022:System of linear equations 1556:{\displaystyle (5/3)m^{3}} 1225:In the special case where 1056:{\displaystyle X=UYV^{T}.} 586:{\displaystyle F=U^{T}CV.} 394:{\displaystyle R=U^{T}AU,} 2091: 2073:Cache-oblivious algorithm 2055: 2014: 1993: 1819:Communications of the ACM 1508:{\displaystyle H=Q^{T}AQ} 1400:{\displaystyle H=Q^{T}AQ} 1361:{\displaystyle R=U^{T}AU} 1076:real Schur decompositions 672:{\displaystyle Y=U^{T}XV} 541:{\displaystyle 2\times 2} 515:{\displaystyle 1\times 1} 355:real Schur decompositions 85:real Schur decompositions 40:Sylvester matrix equation 36:Bartels–Stewart algorithm 18:Bartels-Stewart algorithm 2160:Numerical linear algebra 2124:General purpose software 1987:Numerical linear algebra 1875:10.1109/TAC.1979.1102170 1254:{\displaystyle B=-A^{T}} 32:numerical linear algebra 1786:{\displaystyle AX-XB=C} 1733:low rank approximations 1629:For large systems, the 1586:{\displaystyle 10m^{3}} 1563:flops, compared to the 1517:Householder reflections 1429:upper-Hessenberg matrix 323:{\displaystyle AX-XB=C} 158:{\displaystyle AX-XB=C} 72:{\displaystyle AX-XB=C} 1787: 1749: 1716: 1696: 1676: 1625:Alternative approaches 1607: 1587: 1557: 1509: 1470: 1421: 1401: 1362: 1315: 1295: 1275: 1255: 1211: 1121: 1057: 1012: 960: 915: 895: 875: 845: 814: 718: 673: 634: 587: 542: 516: 490: 470: 447: 395: 344: 324: 286: 266: 246: 191: 159: 121: 101: 73: 2119:Specialized libraries 2032:Matrix multiplication 2027:Matrix decompositions 1832:10.1145/361573.361582 1788: 1750: 1717: 1697: 1677: 1608: 1588: 1558: 1510: 1471: 1422: 1402: 1363: 1316: 1296: 1276: 1256: 1212: 1122: 1058: 1013: 961: 916: 896: 876: 874:{\displaystyle y_{k}} 846: 788: 719: 674: 635: 588: 543: 517: 491: 471: 448: 396: 345: 325: 287: 267: 247: 192: 160: 122: 102: 74: 1759: 1739: 1706: 1686: 1633: 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B} 100:{\displaystyle A} 16:(Redirected from 2167: 2042:Matrix splitting 1980: 1973: 1966: 1957: 1956: 1950: 1949: 1931: 1903: 1897: 1896: 1886: 1858: 1845: 1844: 1834: 1810: 1792: 1790: 1789: 1784: 1754: 1752: 1751: 1746: 1721: 1719: 1718: 1713: 1701: 1699: 1698: 1693: 1681: 1679: 1678: 1673: 1668: 1667: 1655: 1654: 1642: 1641: 1612: 1610: 1609: 1604: 1592: 1590: 1589: 1584: 1582: 1581: 1562: 1560: 1559: 1554: 1552: 1551: 1536: 1514: 1512: 1511: 1506: 1498: 1497: 1475: 1473: 1472: 1467: 1459: 1458: 1426: 1424: 1423: 1418: 1406: 1404: 1403: 1398: 1390: 1389: 1367: 1365: 1364: 1359: 1351: 1350: 1320: 1318: 1317: 1312: 1300: 1298: 1297: 1292: 1280: 1278: 1277: 1272: 1260: 1258: 1257: 1252: 1250: 1249: 1216: 1214: 1213: 1208: 1203: 1202: 1187: 1186: 1162: 1161: 1149: 1148: 1126: 1124: 1123: 1118: 1113: 1112: 1100: 1099: 1062: 1060: 1059: 1054: 1049: 1048: 1017: 1015: 1014: 1011:{\displaystyle } 1009: 1004: 1003: 991: 990: 965: 963: 962: 957: 949: 948: 920: 918: 917: 912: 900: 898: 897: 892: 880: 878: 877: 872: 870: 869: 850: 848: 847: 842: 837: 836: 827: 826: 813: 808: 784: 783: 771: 770: 755: 754: 723: 721: 720: 715: 707: 706: 678: 676: 675: 670: 662: 661: 639: 637: 636: 631: 623: 622: 592: 590: 589: 584: 573: 572: 547: 545: 544: 539: 521: 519: 518: 513: 495: 493: 492: 487: 475: 473: 472: 467: 452: 450: 449: 444: 436: 435: 426: 425: 400: 398: 397: 392: 381: 380: 349: 347: 346: 341: 329: 327: 326: 321: 291: 289: 288: 283: 271: 269: 268: 263: 251: 249: 248: 243: 241: 240: 229: 196: 194: 193: 188: 164: 162: 161: 156: 126: 124: 123: 118: 106: 104: 103: 98: 78: 76: 75: 70: 21: 2175: 2174: 2170: 2169: 2168: 2166: 2165: 2164: 2135: 2134: 2133: 2128: 2087: 2083:Multiprocessing 2051: 2047:Sparse problems 2010: 1989: 1984: 1954: 1953: 1904: 1900: 1859: 1848: 1811: 1804: 1799: 1760: 1757: 1756: 1740: 1737: 1736: 1725:Krylov subspace 1707: 1704: 1703: 1687: 1684: 1683: 1663: 1659: 1650: 1646: 1637: 1636: 1634: 1631: 1630: 1627: 1619: 1598: 1595: 1594: 1577: 1573: 1568: 1565: 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23: 22: 15: 12: 11: 5: 2173: 2163: 2162: 2157: 2152: 2150:Control theory 2147: 2130: 2129: 2127: 2126: 2121: 2116: 2111: 2106: 2101: 2095: 2093: 2089: 2088: 2086: 2085: 2080: 2075: 2070: 2065: 2059: 2057: 2053: 2052: 2050: 2049: 2044: 2039: 2029: 2024: 2018: 2016: 2012: 2011: 2009: 2008: 2003: 2001:Floating point 1997: 1995: 1991: 1990: 1983: 1982: 1975: 1968: 1960: 1952: 1951: 1914:(3): 377–441. 1898: 1869:(6): 909–913. 1846: 1825:(9): 820–826. 1801: 1800: 1798: 1795: 1782: 1779: 1776: 1773: 1770: 1767: 1764: 1744: 1711: 1691: 1671: 1666: 1662: 1658: 1653: 1649: 1645: 1640: 1626: 1623: 1618: 1615: 1602: 1580: 1576: 1572: 1550: 1546: 1542: 1539: 1535: 1531: 1528: 1504: 1501: 1496: 1492: 1488: 1485: 1465: 1462: 1457: 1453: 1449: 1446: 1443: 1440: 1416: 1396: 1393: 1388: 1384: 1380: 1377: 1357: 1354: 1349: 1345: 1341: 1338: 1326: 1323: 1310: 1290: 1270: 1248: 1244: 1240: 1237: 1234: 1222: 1219: 1206: 1201: 1197: 1193: 1190: 1185: 1181: 1177: 1174: 1171: 1168: 1165: 1160: 1156: 1152: 1147: 1143: 1139: 1136: 1116: 1111: 1107: 1103: 1098: 1094: 1090: 1087: 1067: 1064: 1052: 1047: 1043: 1039: 1036: 1033: 1030: 1007: 1002: 998: 994: 989: 986: 983: 979: 975: 955: 952: 947: 944: 941: 938: 935: 931: 910: 890: 868: 864: 852: 851: 840: 835: 831: 825: 822: 818: 812: 807: 804: 801: 798: 795: 791: 787: 782: 778: 774: 769: 765: 761: 758: 753: 750: 746: 742: 739: 736: 713: 710: 705: 702: 699: 696: 693: 689: 668: 665: 660: 656: 652: 649: 629: 626: 621: 617: 613: 610: 607: 604: 582: 579: 576: 571: 567: 563: 560: 537: 534: 531: 511: 508: 505: 485: 465: 454: 453: 442: 439: 434: 430: 424: 420: 416: 413: 402: 401: 390: 387: 384: 379: 375: 371: 368: 353:1.Compute the 339: 319: 316: 313: 310: 307: 304: 301: 281: 261: 239: 236: 233: 228: 223: 220: 217: 214: 202: 199: 186: 154: 151: 148: 145: 142: 139: 136: 116: 96: 68: 65: 62: 59: 56: 53: 50: 26: 9: 6: 4: 3: 2: 2172: 2161: 2158: 2156: 2153: 2151: 2148: 2146: 2143: 2142: 2140: 2125: 2122: 2120: 2117: 2115: 2112: 2110: 2107: 2105: 2102: 2100: 2097: 2096: 2094: 2090: 2084: 2081: 2079: 2076: 2074: 2071: 2069: 2066: 2064: 2061: 2060: 2058: 2054: 2048: 2045: 2043: 2040: 2037: 2033: 2030: 2028: 2025: 2023: 2020: 2019: 2017: 2013: 2007: 2004: 2002: 1999: 1998: 1996: 1992: 1988: 1981: 1976: 1974: 1969: 1967: 1962: 1961: 1958: 1947: 1943: 1939: 1935: 1930: 1925: 1921: 1917: 1913: 1909: 1902: 1894: 1890: 1885: 1880: 1876: 1872: 1868: 1864: 1857: 1855: 1853: 1851: 1842: 1838: 1833: 1828: 1824: 1820: 1816: 1809: 1807: 1802: 1794: 1780: 1777: 1774: 1771: 1768: 1765: 1762: 1755:when solving 1742: 1734: 1730: 1726: 1709: 1689: 1664: 1660: 1656: 1651: 1647: 1622: 1614: 1600: 1578: 1574: 1570: 1548: 1544: 1537: 1533: 1529: 1519:at a cost of 1518: 1502: 1499: 1494: 1490: 1486: 1483: 1463: 1460: 1455: 1451: 1447: 1444: 1441: 1438: 1430: 1414: 1394: 1391: 1386: 1382: 1378: 1375: 1355: 1352: 1347: 1343: 1339: 1336: 1322: 1308: 1288: 1268: 1246: 1242: 1238: 1235: 1232: 1218: 1199: 1195: 1191: 1188: 1183: 1179: 1175: 1169: 1166: 1158: 1154: 1150: 1145: 1141: 1134: 1109: 1105: 1101: 1096: 1092: 1085: 1077: 1073: 1063: 1050: 1045: 1041: 1037: 1034: 1031: 1028: 1019: 1000: 996: 992: 987: 984: 981: 977: 953: 950: 945: 942: 939: 936: 933: 929: 908: 901:th column of 888: 866: 862: 838: 833: 829: 823: 820: 816: 810: 805: 802: 799: 796: 793: 789: 785: 780: 776: 772: 767: 763: 756: 751: 748: 744: 740: 737: 727: 726: 725: 711: 708: 703: 700: 697: 694: 691: 687: 666: 663: 658: 654: 650: 647: 627: 624: 619: 615: 611: 608: 605: 602: 593: 580: 577: 574: 569: 565: 561: 558: 549: 535: 532: 529: 509: 506: 503: 483: 463: 456:The matrices 440: 437: 432: 428: 422: 418: 414: 411: 404: 403: 388: 385: 382: 377: 373: 369: 366: 359: 358: 357: 356: 351: 337: 317: 314: 311: 308: 305: 302: 299: 279: 259: 237: 234: 231: 221: 218: 215: 212: 201:The algorithm 198: 184: 176: 172: 168: 152: 149: 146: 143: 140: 137: 134: 127:to transform 114: 94: 86: 82: 66: 63: 60: 57: 54: 51: 48: 41: 37: 33: 19: 1994:Key concepts 1929:11585/586011 1911: 1907: 1901: 1866: 1862: 1822: 1818: 1628: 1620: 1328: 1224: 1072:QR algorithm 1069: 1020: 853: 594: 550: 455: 352: 204: 35: 29: 1908:SIAM Review 171:C. Van Loan 2145:Algorithms 2139:Categories 2036:algorithms 1797:References 1070:Using the 966:, columns 2063:CPU cache 1938:0036-1445 1893:0018-9286 1884:1813/7472 1841:0001-0782 1769:− 1445:− 1239:− 993:∣ 985:− 951:≠ 937:− 790:∑ 741:− 695:− 609:− 533:× 507:× 306:− 235:× 222:∈ 141:− 55:− 2155:Matrices 2092:Software 2056:Hardware 2015:Problems 1946:17271167 1407:, where 640:, where 167:G. Golub 1021:4. Set 921:. When 881:is the 724:, then 551:2. Set 2114:LAPACK 2104:MATLAB 1944:  1936:  1891:  1839:  1427:is an 1074:, the 854:where 34:, the 2099:ATLAS 1942:S2CID 177:when 2078:SIMD 1934:ISSN 1889:ISSN 1837:ISSN 1702:and 1261:and 476:and 205:Let 107:and 2068:TLB 1924:hdl 1916:doi 1879:hdl 1871:doi 1827:doi 1793:. 1735:to 1613:. 1217:. 1170:2.5 522:or 87:of 30:In 2141:: 1940:. 1932:. 1922:. 1912:58 1910:. 1887:. 1877:. 1867:24 1865:. 1849:^ 1835:. 1823:15 1821:. 1817:. 1805:^ 1571:10 1135:10 1086:10 548:. 169:, 2038:) 2034:( 1979:e 1972:t 1965:v 1948:. 1926:: 1918:: 1895:. 1881:: 1873:: 1843:. 1829:: 1781:C 1778:= 1775:B 1772:X 1766:X 1763:A 1743:X 1710:B 1690:A 1670:) 1665:3 1661:n 1657:+ 1652:3 1648:m 1644:( 1639:O 1601:A 1579:3 1575:m 1549:3 1545:m 1541:) 1538:3 1534:/ 1530:5 1527:( 1503:Q 1500:A 1495:T 1491:Q 1487:= 1484:H 1464:F 1461:= 1456:T 1452:S 1448:Y 1442:Y 1439:H 1415:H 1395:Q 1392:A 1387:T 1383:Q 1379:= 1376:H 1356:U 1353:A 1348:T 1344:U 1340:= 1337:R 1309:Y 1289:X 1269:C 1247:T 1243:A 1236:= 1233:B 1205:) 1200:2 1196:m 1192:n 1189:+ 1184:2 1180:n 1176:m 1173:( 1167:+ 1164:) 1159:3 1155:n 1151:+ 1146:3 1142:m 1138:( 1115:) 1110:3 1106:n 1102:+ 1097:3 1093:m 1089:( 1051:. 1046:T 1042:V 1038:Y 1035:U 1032:= 1029:X 1006:] 1001:k 997:y 988:1 982:k 978:y 974:[ 954:0 946:k 943:, 940:1 934:k 930:s 909:Y 889:k 867:k 863:y 839:, 834:j 830:y 824:j 821:k 817:s 811:n 806:1 803:+ 800:k 797:= 794:j 786:+ 781:k 777:f 773:= 768:k 764:y 760:) 757:I 752:k 749:k 745:s 738:R 735:( 712:0 709:= 704:k 701:, 698:1 692:k 688:s 667:V 664:X 659:T 655:U 651:= 648:Y 628:F 625:= 620:T 616:S 612:Y 606:Y 603:R 581:. 578:V 575:C 570:T 566:U 562:= 559:F 536:2 530:2 510:1 504:1 484:S 464:R 441:. 438:V 433:T 429:B 423:T 419:V 415:= 412:S 389:, 386:U 383:A 378:T 374:U 370:= 367:R 338:X 318:C 315:= 312:B 309:X 303:X 300:A 280:B 260:A 238:n 232:m 227:R 219:C 216:, 213:X 185:X 153:C 150:= 147:B 144:X 138:X 135:A 115:B 95:A 67:C 64:= 61:B 58:X 52:X 49:A 20:)

Index

Bartels-Stewart algorithm
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

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