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Matrix Eigenvalue Problems

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Numerical Methods in Matrix Computations

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Abstract

The eigenvalues and eigenvectors of a matrix play an important role in many settings in physics and engineering.

The eigenvalue problem has a deceptively simple formulation, yet the determination of accurate solutions presents a wide variety of challenging problems.

—J. H. Wilkinson, The Algebraic Eigenvalue Problem, 1965.

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Notes

  1. 1.

    Wilkinson received the Chauvenet Prize of the Mathematical Association of America 1987 for this exposition of the ill-conditioning of polynomial zeros.

  2. 2.

    Marie Ennemond Camille Jordan (1838–1922), French mathematician, professor at École Polytechnique and Collége de France. Jordan made important contributions to finite group theory, linear and multilinear algebra, as well as differential equations. His paper on the canonical form was published in 1870.

  3. 3.

    Vera Nikolaevna Kublanovskaya (1920–2012), Russian mathematician, was one of the founders of modern linear algebra. She came from a small village on the Lake Beloye east of Leningrad and began studies in Leningrad to become a teacher. There she was encouraged to pursue a career in mathematics by D. K. Faddeev. After surviving the siege of Leningrad, she graduated in 1948 and joined the Steklov institute. Here she became responsible for selecting matrix algorithm for BESM, the first electronic computer in the USSR. She is most widely known as one of the inventors of the QR algorithm and her work on canonical forms.

  4. 4.

    Jacopo Francesco Riccati (1676–1754), Italian mathematician. His works on hydraulics and differential equations were used by the city of Venice in regulating the canals. The Riccati differential equation \(y^\prime = c_0(x) + c_1(x)y + c_2(x)y^2\) is named after him. The algebraic Riccati equation, which also is quadratic, is named in analogy to this.

  5. 5.

    Semyon Aranovich Geršgorin (1901–1933), Russian mathematician, who worked at the Leningrad Mechanical Engineering Institute. He published his circle theorem 1931 in [93, 1931].

  6. 6.

    John William Strutt (1842–1919) succeeded his father as third Baron Rayleigh in 1873. Unable to follow a conventional academic career, he performed scientific experiments at his private laboratory for many years. In his major text The Theory of Sound he studied the mechanics of a vibrating string and explained wave propagation. From 1879 to 1884 he held a position in experimental physics at the University of Cambridge. He held many official positions, including President of the London Mathematical Society and President of the Royal Society. In 1904 he and Sir William Ramsey were awarded the Nobel prize for the discovery of the inert gas argon.

  7. 7.

    This result, first published by Toeplitz [228, 1918], is not trivial. Householder [132, 1964], Sect. 3.3.2, gives a proof due to Hans Schneider (unpublished).

  8. 8.

    Helmut Wielandt (1910–2001) was a student of Schmidt and Schur in Berlin. His initial work was in group theory. In 1942 he became attached to the Aerodynamics Research Institute in Göttingen and started to work on vibration theory. He contributed greatly to matrix theory and did pioneering work on computational methods for the matrix eigenvalue problem; see [133, 1996].

  9. 9.

    Hermann Günter Grassmann (1809–1877), German mathematician, was born in Stettin. He studied theology, languages, and philosophy at University of Berlin. As a teacher at the Gymnasium in Stettin he took up mathematical research on his own. In 1844 he published a highly original textbook, in which the symbols representing geometric entities such as points, lines, and planes were manipulated using certain rules. Later his work became used in areas such as differential geometry and relativistic quantum mechanics. Sadly, the leading mathematicians of his time failed to recognize the importance of his work.

  10. 10.

    Heinz Rutishauser (1918–1970) Swiss mathematician, a pioneer in computing, and the originator of many important algorithms in Scientific Computing. In 1948 he joined the newly founded Institute for Applied Mathematics at ETH in Zürich. He spent 1949 at Harvard with Howard Aiken and at Princeton with John von Neumann to learn about electronic computers. Rutishauser was one of the leaders in the international development of the programming language Algol. His qd algorithm [206, 1954] had great impact on methods for eigenvalue calculations.

  11. 11.

    John Francis, born in London 1934, is an English computer scientist. In 1954 he worked for the National Research Development Corporation (NRDC). In 1955–1956 he attended Cambridge University, but did not complete a degree and returned to work for NRDC, now as an assistant to Christopher Strachey. Here he developed the QR algorithm, but by 1962 left the field of numerical analysis. He had no idea of the great impact the QR algorithm has had until contacted by Gene Golub and Frank Uhlig in 2007.

  12. 12.

    Ralph Byers (1955–2007) made a breakthrough in his PhD thesis from Cornell University in 1983 by finding a strongly stable numerical methods of complexity \(O(n^3)\) for Hamiltonian and symplectic eigenvalue problems. He was also instrumental in developing methods based on the matrix sign function for the solution of Riccati equations for large-scale control problems. His work on the multishift QR algorithm was rewarded with the SIAM Linear Algebra prize in 2003 and the SIAM Outstanding paper prize in 2005.

  13. 13.

    This name was chosen because of the connection with Schur’s work on bounded analytic functions.

  14. 14.

    Carl Gustaf Jacob Jacobi (1805–1851), German mathematician, started teaching mathematics at the University of Berlin already in 1825. In 1826 he moved to the University of Königsberg, where Bessel held a chair. In the summer of 1829 he visited Gauss in Göttingen and Legendre and Fourier in Paris and published a paper containing fundamental advances on the theory of elliptic functions. In 1832 he was promoted to full professor. Like Euler, Jacobi was a proficient calculator who drew a great deal of insight from immense computational work.

  15. 15.

    William Rowan Hamilton (1805–1865), Irish mathematician, professor at Trinity College, Dublin. He made important contributions to classical mechanics, optics, and algebra. His greatest contribution is the reformulation of classical Newton mechanics now called Hamiltonian mechanics. He is also known for his discovery of quaternions as an extension of (two-dimensional) complex numbers to four dimensions.

  16. 16.

    Laub [165, 1979] used a similar idea for computing the Hamiltonian Schur form using information from an unstructured Schur form.

  17. 17.

    Henry Eugène Padé (1863–1953), French mathematician and student of Charles Hermite, gave a systematic study of these approximations in his thesis 1892.

  18. 18.

    Henry Briggs (1561–1630), English mathematicians, fellow of St. John’s College, Oxford, was greatly interested in astronomy, which involved much heavy calculations. He learned about logarithms by reading Napier’s text from 1614. Briggs constructed logarithmic tables to 14 decimal places that were published in 1624.

  19. 19.

    John Napier (1550–1617) came from a wealthy Scottish family and devoted much time to running his estate and working on Protestant theology. He studied mathematics as a hobby and undertook long calculations. His work on logarithms was published in Latin 1614. An English translation by E. Wright appeared in 1616.

  20. 20.

    Wassily Leontief (1905–1999) was a Russian–American economist and Nobel laureate 1973. Educated first in St Petersburg, he left USSR in 1925 to earn his PhD in Berlin. In 1931 he went to the United States, where in 1932 he joined Harvard University. Around 1949 he used the computer Harvard Mark II to model 500 sectors of the US economy, one of the first uses of computers for modeling.

  21. 21.

    Oskar Perron (1880–1975), German mathematician held positions at Heidelberg and Munich. His work covered a wide range of topics. He also wrote important textbooks on continued fractions, algebra, and non-Euclidean geometry.

  22. 22.

    Named after Andrei Andreevic Markov (1856–1922). Markov attended lectures by Chebyshev at St Petersburg University, where graduated in 1884. His early work was in number theory, analysis, and continued fractions. He introduced Markov chains in 1908 (see also [175, 1912]), which started a new branch in probability theory.

  23. 23.

    It is known that if this condition holds, \(A\) belongs to a set that forms a multiplicative group under ordinary matrix multiplication.

References

  1. Abou-Kandil, H., Freiling, G., Ionescu, V., Jank, G.: Matrix Riccati Equations in Control and Systems Theory. Birkhäuser, Basel, Switzerland (2003)

    Google Scholar 

  2. Absil, P.-A., Mahony, R., Sepulchre, R.: Optimization Algorithms on Matrix Manifolds. Princeton University Press, Princeton (2007)

    Google Scholar 

  3. Absil, P.-A., Mahony, R., Sepulchre, R., Van Dooren, P.: A Grassmann-Rayleigh quotient iteration for computing invariant subspaces. SIAM Rev. 44(1), 57–73 (2002)

    MathSciNet  MATH  Google Scholar 

  4. Absil, P.-A., Mahony, R., Sepulchre, R., Van Dooren, P.: Cubically convergent iterations for invariant subspace computation. SIAM J. Matrix Anal. Appl. 26(1), 70–96 (2004)

    MathSciNet  MATH  Google Scholar 

  5. Al-Mohy, A.H., Higham, N.J.: A new scaling and squaring algorithm for the matrix exponential. SIAM J. Matrix Anal. Appl. 31(3), 970–989 (2009)

    MathSciNet  Google Scholar 

  6. Ammar, G.S., Gragg, W.B., Reichel, L.: On the eigenproblem for orthogonal matrices. In: Proceedings of the 25th IEEE Conference on Decision and Control, pp. 1963–1966. IEEE, Piscataway (1986)

    Google Scholar 

  7. Ammar, G.S., Reichel, L., Sorensen, D.C.: An implementation of a divide and conquer algorithm for the unitary eigenvalue problem. ACM Trans. Math. Softw. 18(3), 292–307 (1992)

    MATH  Google Scholar 

  8. Ammar, G.S., Reichel, L., Sorensen, D.C.: Algorithm 730. An implementation of a divide and conquer algorithm for the unitary eigenvalue problem. ACM Trans. Math. Softw. 20, 161 (1994)

    MATH  Google Scholar 

  9. Anderson, E., Bai, Z., Bischof, C.H., Blackford, L.S., Demmel, J.W., Dongarra, J.J., Du Croz, J.J., Greenbaum, A., Hammarling, S.J., McKenney, A., Sorensen, D.C.: LAPACK Users’ Guide, 3rd edn. SIAM, Philadelphia (1999)

    Google Scholar 

  10. Arnold, W., Laub, A.: Generalized eigenproblem algorithms for solving the algebraic Riccati equation. Proc. IEEE 72(12), 1746–1754 (1984)

    Google Scholar 

  11. Bai, Z., Demmel, J.W.: Computing the generalized singular value decomposition. SIAM J. Sci. Comput. 14(6), 1464–1486 (1993)

    MathSciNet  MATH  Google Scholar 

  12. Bai, Z., Demmel, J.W.: Using the matrix sign function to compute invariant subspaces. SIAM J. Matrix Anal. Appl. 19(1), 205–225 (1998)

    MathSciNet  MATH  Google Scholar 

  13. Bai, Z., Zha, H.: A new preprocessing algorithm for the computation of the generalized singular value decomposition. SIAM J. Sci. Comput. 14(4), 1007–1012 (1993)

    MathSciNet  MATH  Google Scholar 

  14. Bai, Z., Demmel, J.W., McKenney, A.: On computing condition numbers for the nonsymmetric eigenproblem. ACM Trans. Math. Softw. 19(1), 202–223 (1993)

    Google Scholar 

  15. Bai, Z., Demmel, J.W., Dongarra, J.J., Ruhe, A., van der Vorst, H.A.: Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide. SIAM, Philadelphia (2000)

    Google Scholar 

  16. Baker, G.A. Jr., Graves-Morris, P.: Padé Approximants. Encyclopedia Math. Appl. 59, 2nd edn. Cambridge University Press, Cambridge (1996)

    Google Scholar 

  17. Bartels, R.H., Stewart, G.W.: Algorithm 432: solution of the equation \(AX + XB = C\). Comm. ACM 15, 820–826 (1972)

    Google Scholar 

  18. Barth, W., Martin, R.S., Wilkinson, J.H.: Calculation of the eigenvalues of a symmetric tridiagonal matrix by the method of bisection. In: Wilkinson, J.H., Reinsch, C. (eds.) Handbook for Automatic Computation, vol. II, Linear Algebra, pp. 249–256. Springer, New York (1971) (Prepublished in Numer. Math. 9, 386–393, 1967)

    Google Scholar 

  19. Bauer, F.L.: Das Verfahren der Treppeniteration und verwandte Verfahren zur Lösung algebraischer Eigenwertprobleme. Z. Angew. Math. Phys. 8, 214–235 (1957)

    MathSciNet  MATH  Google Scholar 

  20. Berman, A., Plemmons, R.J.: Nonnegative Matrices in the Mathematical Sciences. Academic Press, New York (1979) (Republished in 1994 by SIAM, Philadelphia, with corrections and supplement)

    Google Scholar 

  21. Bhatia, R.: Matrix Analysis. Graduate Texts in Mathematics, vol. 169. Springer, New York (1997)

    Google Scholar 

  22. Bhatia, R.: Perturbation Bounds for Matrix Eigenvalues. Number 53 in Classics in Applied Mathematics. SIAM, Philadelphia (2007) (Revised edition of book published by Longman Scientific & Technical. Harlow, Essex, 1987)

    Google Scholar 

  23. Bini, D.A., Latouche, G., Meini, B.: Numerical Methods for Structured Markov Chains. Oxford University Press, Oxford (2005)

    MATH  Google Scholar 

  24. Björck, Å., Bowie, C.: An iterative algorithm for computing the best estimate of an orthogonal matrix. SIAM J. Numer. Anal. 8, 358–364 (1971)

    Google Scholar 

  25. Björck, Å., Hammarling, S.: A Schur method for the square root of a matrix. Linear Algebra Appl. 52(53), 127–140 (1983)

    MathSciNet  Google Scholar 

  26. Bojanczyk, A.W., Golub, G.H., Van Dooren, P.: The periodic Schur decomposition: algorithms and applications. Advanced Signal Processing Algorithms, Architectures, and Implementations. Proceedings of SPIE 1770, pp. 31–32. SPIE, Bellingham (1992)

    Google Scholar 

  27. Bojanovié, Z., Drmač, Z.: A contribution to the theory and practice of the block Kogbetliantz method for computing the SVD. BIT 52(4), 827–849 (2012)

    MathSciNet  Google Scholar 

  28. de Boor, C.: On Pták’s derivation of the Jordan normal form. Linear Algebra Appl. 310, 9–10 (2000)

    MathSciNet  MATH  Google Scholar 

  29. Braconnier, T., Higham, N.J.: Computing the field of values and pseudospectra using the Lanczos method with continuation. BIT 36(3), 422–440 (1996)

    MathSciNet  MATH  Google Scholar 

  30. Braman, K., Byers, R., Mathias, R.: The multishift QR algorithm. Part I. Maintaining well-focused shifts and level 3 performance. SIAM J. Matrix. Anal. Appl. 23(4), 929–947 (2002)

    MathSciNet  MATH  Google Scholar 

  31. Braman, K., Byers, R., Mathias, R.: The multishift QR algorithm. Part II. Aggressive early deflation. SIAM J. Matrix. Anal. Appl. 23(4), 948–973 (2002)

    MathSciNet  MATH  Google Scholar 

  32. Bryan, K., Leise, T.: The \({\$}\)25, 000, 000, 000 eigenvector: the linear algebra behind Google. SIAM Rev. 48(3), 569–581 (2006)

    Google Scholar 

  33. Bunch, J.R.: Stability of methods for solving Toeplitz systems of equations. SIAM J. Sci. Stat. Comp. 6(2), 349–364 (1985)

    MathSciNet  MATH  Google Scholar 

  34. Bunch, J.R., Nielsen, C.P., Sorensen, D.C.: Rank-one modifications of the symmetric tridiagonal eigenproblem. Numer. Math. 31(1), 31–48 (1978)

    MathSciNet  MATH  Google Scholar 

  35. Bunse-Gerstner, A., Byers, R., Mehrmann, V.: A chart of numerical methods for structured eigenvalue problems. SIAM J. Matrix. Anal. Appl. 13(2), 419–453 (1992)

    MathSciNet  MATH  Google Scholar 

  36. Byers, R.: A Hamiltonian QR algorithm. SIAM J. Sci. Statist. Comput. 7(1), 212–229 (1986)

    MathSciNet  MATH  Google Scholar 

  37. Byers, R., Xu, H.: A new scaling for Newton’s iteration for the polar decomposition and its backward stability. SIAM J. Matrix. Anal. Appl. 30(2), 822–843 (2008)

    Google Scholar 

  38. Cardoso, J.R., Leite, F.S.: Theoretical and numerical considerations about Padé approximants for the matrix logarithm. Linear Algebra Appl. 330, 31–42 (2001)

    MathSciNet  MATH  Google Scholar 

  39. Cardoso, J.R., Leite, F.S.: Padé and Gregory error estimates for the logarithm of block triangular matrices. Appl. Numer. Math. 56, 253–267 (2006)

    MathSciNet  MATH  Google Scholar 

  40. Chan, T.: An improved algorithm for computing the singular value decomposition. ACM Trans. Math. Softw. 8(1), 72–83 (1982)

    MATH  Google Scholar 

  41. Chandrasekaran, S., Ipsen, I.C.F.: Analysis of a QR algorithm for computing singular values. SIAM J. Matrix Anal. Appl. 16(2), 520–535 (1995)

    Google Scholar 

  42. Chatelin, F.: Simultaneous Newton’s corrections for the eigenproblem. In: Defect Correction Methods, Comput. Suppl. 5, pp. 67–74. Springer, Vienna (1984)

    Google Scholar 

  43. Chatelin, F.: Eigenvalues of Matrices. Number 71 in Classics in Applied Mathematics. SIAM, Philadelphia (2012) (Revised edition of book published by Wiley, Chichester 1993)

    Google Scholar 

  44. Crawford, C.R.: Reduction of a band-symmetric generalized eigenvalue problem. Comm. Assoc. Comput. Mach. 16, 41–44 (1973)

    MathSciNet  MATH  Google Scholar 

  45. Crawford, C.R., Moon, Y.S.: Finding a positive definite linear combination of two Hermitian matrices. Linear Algebra Appl. 51, 37–48 (1983)

    Google Scholar 

  46. Cuppen, J.J.M.: A divide and conquer method for the symmetric tridiagonal eigenproblem. Numer. Math. 36(2), 177–195 (1981)

    MathSciNet  MATH  Google Scholar 

  47. Dahlquist, G.: Stability and Error Bounds in the Numerical Integration of Ordinary Differential Equations. Ph.D. thesis, Department of Mathematics, Uppsala University, Uppsala (1958) (Also available as Trans. Royal Inst. Technology, Stockholm, No. 130)

    Google Scholar 

  48. Dahlquist, G., Björck, Å.: Numerical Methods in Scientific Computing, vol. I. SIAM, Philadelphia (2008)

    MATH  Google Scholar 

  49. David, R.J.A., Watkins, D.S.: Efficient implementation of the multishift QR algorithm for the unitary eigenvalue problem. SIAM J. Matrix Anal. Appl. 28(3), 623–633 (2006)

    MathSciNet  MATH  Google Scholar 

  50. Davies, P.I., Higham, N.J.: A Schur-Parlett algorithm for computing matrix functions. SIAM J. Matrix Anal. Appl. 25(2), 464–485 (2003)

    MathSciNet  MATH  Google Scholar 

  51. Davies, P.I., Higham, N.J., Tisseur, F.: Analysis of the Cholesky method with iterative refinement for solving the symmetric definite generalized eigenproblem. SIAM J. Matrix Anal. Appl. 23(2), 472–493 (2001)

    MathSciNet  MATH  Google Scholar 

  52. Davis, C., Kahan, W.M.: Some new bounds on perturbation of subspaces. Bull. Amer. Math. Soc. 75, 863–868 (1969)

    MathSciNet  MATH  Google Scholar 

  53. Davis, C., Kahan, W.M.: The rotation of eigenvectors by a perturbation. SIAM J. Numer. Anal. 7(1), 1–46 (1970)

    MathSciNet  MATH  Google Scholar 

  54. Demmel, J.W.: Three ways for refining estimates of invariant subspaces. Computing 38, 43–57 (1987)

    MathSciNet  MATH  Google Scholar 

  55. Demmel, J.W., Kågström, B.: The generalized Schur decomposition of an arbitrary pencil \(A - \lambda B\): Robust software with error bounds and applications. Part I: Theory and algorithms. ACM Trans. Math. Softw. 19(2), 160–174 (1980)

    Google Scholar 

  56. Demmel, J.W., Kågström, B.: The generalized Schur decomposition of an arbitrary pencil \(A - \lambda B\): Robust software with error bounds and applications. Part II: Software and algorithms. ACM Trans. Math. Softw. 19(2), 175–201 (1980)

    Google Scholar 

  57. Demmel, J.W., Kahan, W.: Accurate singular values of bidiagonal matrices. SIAM J. Sci. Stat. Comput. 11(5), 873–912 (1990)

    MathSciNet  MATH  Google Scholar 

  58. Demmel, J.W., Veselić, K.: Jacobi’s method is more accurate than QR. SIAM J. Matrix Anal. Appl. 13(4), 1204–1245 (1992)

    MathSciNet  MATH  Google Scholar 

  59. Demmel, J.W., Gu, M., Eisenstat, S., Slapnic̆ar, I., Veselić, K., Drmač, Z.: Computing the singular value decomposition with high relative accuracy. Linear Algebra Appl. 299, 21–80 (1999)

    MathSciNet  MATH  Google Scholar 

  60. Denman, E.D., Beavers, A.N.: The matrix sign function and computations in systems. Appl. Math. Comput. 2, 63–94 (1976)

    MathSciNet  MATH  Google Scholar 

  61. Dhillon, I.S.: A New \(O(n^2)\) Algorithm for the Symmetric Tridiagonal Eigenvalue/Eigenvector Problem. Ph.D. thesis, University of California, Berkeley (1997)

    Google Scholar 

  62. Dhillon, I.S.: Current inverse iteration software can fail. BIT 38(4), 685–704 (1998)

    Google Scholar 

  63. Dhillon, I.S., Parlett, B.N.: Orthogonal eigenvectors and relative gaps. SIAM J. Matrix Anal. Appl. 25(3), 858–899 (2004)

    MathSciNet  MATH  Google Scholar 

  64. Dhillon, I.S., Parlett, B.N.: Multiple representations to compute orthogonal eigenvectors of symmetric tridiagonal matrices. Linear Algebra Appl. 387, 1–28 (2004)

    MathSciNet  MATH  Google Scholar 

  65. Dhillon, I.S., Parlett, B.N., Vömel, C.: The design and implementation of the MRRR algorithm. ACM Trans. Math. Softw. 32, 533–560 (2006)

    MATH  Google Scholar 

  66. Dieci, L., Morini, B., Papini, A.: Computational techniques for real logarithms of matrices. SIAM J. Matrix. Anal. Approx. 17(3), 570–593 (1996)

    MathSciNet  MATH  Google Scholar 

  67. Dongarra, J.J., Sorensen, D.C.: A fully parallel algorithmic for the symmetric eigenvalue problem. SIAM J. Sci. Stat. Comput. 8(2), 139–154 (1987)

    MathSciNet  Google Scholar 

  68. Dongarra, J.J., Hammarling, S., Sorensen, D.C.: Block reduction of matrices to condensed forms for eigenvalue computation. J. Assoc. Comput. Mach. 27, 215–227 (1989)

    MathSciNet  MATH  Google Scholar 

  69. Drazin, M.P.: Pseudo inverses in associative rays and semigroups. Amer. Math. Monthly 65, 506–514 (1958)

    MathSciNet  MATH  Google Scholar 

  70. Drmač, Z.: Implementation of Jacobi rotations for accurate singular value computation in floating point arithmetic. SIAM J. Sci. Comput. 18(4), 1200–1222 (1997)

    MathSciNet  MATH  Google Scholar 

  71. Drmač, Z., Veselić, K.: New fast and accurate Jacobi SVD algorithm. i–ii. SIAM J. Matrix Anal. Appl. 29(4), 1322–1342, 1343–1362 (2008)

    Google Scholar 

  72. Edelman, A., Arias, T., Smith, S.T.: The geometry of algorithms with orthogonality constraints. SIAM J. Matrix. Anal. Appl. 20(2), 303–353 (1999)

    MathSciNet  Google Scholar 

  73. Eisenstat, S.C., Ipsen, I.C.F.: Relative perturbation techniques for singular value problems. SIAM J. Numer. Anal. 32(6), 1972–1988 (1995)

    MathSciNet  MATH  Google Scholar 

  74. Eldén, L.: Matrix Methods in Data Mining and Pattern Recognition. SIAM, Philadelphia (2007)

    MATH  Google Scholar 

  75. Fassbender, H., Mackey, D.S., Mackey, N.: Hamilton and Jacobi come full circle: Jacobi algorithms for structured Hamiltonian eigenproblems. Linear Algebra Appl. 332–334, 37–80 (2001)

    Google Scholar 

  76. Fernando, K.V.: Accurately counting singular values of bidiagonal matrices and eigenvalues of skew-symmetric tridiagonal matrices. SIAM J. Matrix Anal. Appl. 20(2), 373–399 (1998)

    MathSciNet  MATH  Google Scholar 

  77. Fernando, K.V., Parlett, B.N.: Accurate singular values and differential qd algorithms. Numer. Math. 67, 191–229 (1994)

    MathSciNet  MATH  Google Scholar 

  78. Fiedler, M.: Special Matrices and Their Applications in Numerical Mathematics, 2nd edn. Dover, Mineola (2008)

    MATH  Google Scholar 

  79. Fischer, E.: Über quadratische Formen mit reeller Koeffizienten. Monatshefte Math. Phys. 16, 234–249 (1905)

    MATH  Google Scholar 

  80. Fletcher, R., Sorensen, D.C.: An algorithmic derivation of the Jordan canonical form. Am. Math. Mon. 90, 12–16 (1983)

    Google Scholar 

  81. Forsythe, G.E., Henrici, P.: The cyclic Jacobi method for computing the principal values of a complex matrix. Trans. Amer. Math. Soc. 94, 1–23 (1960)

    MathSciNet  MATH  Google Scholar 

  82. Francis, J.G.F.: The QR transformation. Part I. Comput. J. 4, 265–271 (1961–1962)

    Google Scholar 

  83. Francis, J.G.F.: The QR transformation. Part II. Comput. J. 4, 332–345 (1961–1962)

    Google Scholar 

  84. Frank, W.L.: Computing eigenvalues of complex matrices by determinant evaluation and by methods of Danilewski and Wielandt. J. SIAM 6, 378–392 (1958)

    MATH  Google Scholar 

  85. Frobenius, G.: Über Matrizen aus nicht negativen Elementen. Sitzungsber. Königl. Preuss. Akad. Wiss., pp. 456–477. Berlin (1912)

    Google Scholar 

  86. Fröberg, C.-E.: Numerical Mathematics. Theory and Computer Applications. Benjamin/Cummings, Menlo Park (1985)

    Google Scholar 

  87. Gander, W.: Algorithms for the polar decomposition. SIAM J. Sc. Stat. Comput. 11(6), 1102–1115 (1990)

    MathSciNet  MATH  Google Scholar 

  88. Gander, W.: Zeros of determinants of \(\lambda \)-matrices. Proc. Appl. Math. Mech. 8(1), 10811–10814 (2008)

    Google Scholar 

  89. Gantmacher, F.R.: The Theory of Matrices, vol. II, ix+276 pp. Chelsea Publishing Co, New York (1959)

    Google Scholar 

  90. Garbow, B.S., Boyle, J.M., Dongarra, J.J., Stewart, G.W.: Matrix Eigensystems Routines: EISPACK Guide Extension, volume 51 of Lecture Notes in Computer Science. Springer, New York (1977)

    Google Scholar 

  91. Gardiner, J.D., Laub, A.J., Amato, J.J., Moler, C.B.: Solution of the Sylvester matrix equation \(AXB^T + CXD^T = E\). ACM Trans. Math. Softw. 18(2), 223–231 (1992)

    MathSciNet  MATH  Google Scholar 

  92. Gardiner, J.D., Wette, M.R., Laub, A.J., Amato, J.J., Moler, C.B.: Algorithm 705: a FORTRAN-77 software package for solving the Sylvester matrix equation \(AXB^T + CXD^T = E\). Comm. ACM 18(2), 232–238 (1992)

    MathSciNet  MATH  Google Scholar 

  93. Geršgorin, S.A.: Über die Abgrenzung der Eigenwerte einer Matrix. Akademia Nauk SSSR, Math. Nat. Sci. 6, 749–754 (1931)

    Google Scholar 

  94. Givens, W.G.: Numerical computation of the characteristic values of a real symmetric matrix. Technical Report ORNL-1574, Oak Ridge National Laboratory, Oak Ridge (1954)

    Google Scholar 

  95. Godunov, S.K., Ryabenkii, V.S.: Spectral portraits of matrices. Technical Report Preprint 3. Institute of Mathematics, Siberian Branch of USSR Academy of Sciences (1990) (In Russian)

    Google Scholar 

  96. Gohberg, I., Lancaster, P., Rodman, L.: Matrix Polynomials. Academic Press, New York (1982) (Republished in 1964 by SIAM, Philadelphia)

    Google Scholar 

  97. Goldstine, H.H.: A History of Numerical Analysis from the \(16\)th through the \(19\)th Century, vol. 2. Springer, New York (1977) (Stud. Hist. Math. Phys. Sci.)

    Google Scholar 

  98. Goldstine, H.H., Horwitz, L.P.: A procedure for the diagonalization of normal matrices. J. Assoc. Comput. Mach. 6, 176–195 (1959)

    Google Scholar 

  99. Goldstine, H.H., Murray, H.H., von Neumann, J.: The Jacobi method for real symmetric matrices. J. Assoc. Comput. Mach. 6, 59–96 (1959)

    Google Scholar 

  100. Golub, G.H.: Least squares, singular values and matrix approximations. Aplikace Matematiky 13, 44–51 (1968)

    MathSciNet  MATH  Google Scholar 

  101. Golub, G.H., Meyer, C.D. Jr.: Using the QR factorization and group inversion to compute, differentiate, and estimate the sensitivity of stationary probabilities for Markov chains. SIAM J. Alg. Disc. Meth. 7(2), 273–281 (1986)

    Google Scholar 

  102. Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. In: Wilkinson, J.H., Reinsch, C. (eds.) Handbook for Automatic Computation, vol. II, Linear Algebra, pp. 134–151. Springer, New York (1970) (Prepublished in Numer. Math. 14, 403–420, 1970)

    Google Scholar 

  103. Golub, G.H., Uhlig, F.: The QR algorithm 50 years later its genesis by John Francis and Vera Kublanovskaya and subsequent developments. IMS J. Numer. Anal. 29, 467–485 (2009)

    MathSciNet  MATH  Google Scholar 

  104. Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore (1983)

    MATH  Google Scholar 

  105. Golub, G.H., van der Vorst, H.A.: Eigenvalue computations in the 20th century. J. Comput. Appl. Math. 123, 35–65 (2000)

    MathSciNet  MATH  Google Scholar 

  106. Golub, G.H., Nash, S.G., Van Loan, C.F.: A Hessenberg-Schur method for the matrix problem \(AX + XB = C\). IEEE Trans. Automat. Control. AC 24, 909–913 (1972)

    Google Scholar 

  107. Gragg, W.B.: The QR algorithm for unitary Hessenberg matrices. J. Comp. Appl. Math. 16, 1–8 (1986)

    MathSciNet  MATH  Google Scholar 

  108. Granat, R., Kågström, B.: Parallel solvers for Sylvester-type matrix equations with applications in condition estimation. Part I. ACM Trans. Math. Softw. 37(3), 32:1–32:32 (2010)

    Google Scholar 

  109. Granat, R., Kågström, B., Kressner, D.: A novel parallel QR algorithm for hybrid distributed memory HPC systems. SIAM J. Sci. Stat. Comput. 32(1), 2345–2378 (2010)

    MATH  Google Scholar 

  110. Grcar, J.F.: Operator coefficient methods for linear equations. Report SAND 89–8691, Sandia National Laboratory (1989)

    Google Scholar 

  111. Großer, B., Lang, B.: An \(O(n^2)\) algorithm for the bidiagonal SVD. Linear Algebra Appl. 358, 45–70 (2003)

    MathSciNet  MATH  Google Scholar 

  112. Großer, B., Lang, B.: On symmetric eigenproblems induced by the bidiagonal SVD. SIAM J. Matrix. Anal. Appl. 26(3), 599–620 (2005)

    MathSciNet  MATH  Google Scholar 

  113. Gu, M., Eisenstat, S.C.: A divide-and-conquer algorithm for the symmetric tridiagonal eigenproblem. SIAM J. Matrix. Anal. Appl. 16(1), 172–191 (1995)

    Google Scholar 

  114. Gu, M., Eisenstat, S.C.: A divide-and-conquer algorithm for the bidiagonal SVD. SIAM J. Matrix. Anal. Appl. 16(1), 79–92 (1995)

    Google Scholar 

  115. Gu, M., Guzzo, R., Chi, X.B., Cao, X.Q.: A stable divide-and-conquer algorithm for the unitary eigenproblem. SIAM J. Matrix. Anal. Appl. 25(2), 385–404 (2003)

    Google Scholar 

  116. Gu, M., Demmel, J.W., Dhillon, I.: Efficient computation of the singular value decomposition with applications to least squares problems. Technical Report TR/PA/02/33, Department of Mathematics and Lawrence Berkeley Laboratory, University of California, Berkeley (1994)

    Google Scholar 

  117. Guo, C.-H., Higham, N.J., Tisseur, F.: An improved arc algorithm for detecting definite Hermitian pairs. SIAM J. Matrix Anal. Appl. 31(3), 1131–1151 (2009)

    Google Scholar 

  118. Hammarling, S.J.: Numerical solution of the stable non-negative definite Lyapunov equation. IMA J. Numer. Anal. 2, 303–323 (1982)

    MathSciNet  MATH  Google Scholar 

  119. Hari, V., Veselić, K.: On Jacobi’s method for singular value decompositions. SIAM J. Sci. Stat. Comput. 8(5), 741–754 (1987)

    MATH  Google Scholar 

  120. Henrici, P.: Bounds for iterates, inverses, spectral variation and fields of values of non-normal matrices. Numer. Math. 4, 24–40 (1962)

    MathSciNet  MATH  Google Scholar 

  121. Hestenes, M.R.: Inversion of matrices by biorthogonalization and related results. J. Soc. Indust. Appl. Math. 6, 51–90 (1958)

    MathSciNet  MATH  Google Scholar 

  122. Higham, N.J.: Computing the polar decomposition–with applications. SIAM J. Sci. Stat. Comput. 7(4), 1160–1174 (1986)

    MathSciNet  MATH  Google Scholar 

  123. Higham, N.J.: Computing real square roots of a real matrix. Linear Algebra Appl. 88(89), 405–430 (1987)

    MathSciNet  Google Scholar 

  124. Higham, N.J.: Evaluating Padé approximants of the matrix logarithm. SIAM J. Matrix Anal. Appl. 22(4), 1126–1135 (2001)

    MathSciNet  MATH  Google Scholar 

  125. Higham, N.J.: The matrix computation toolbox for MATLAB (Version 1.0). Numerical Analysis Report 410. Department of Mathematics, The University of Manchester (2002)

    Google Scholar 

  126. Higham, N.J.: \(J\)-Orthogonal matrices: properties and generation. SIAM Rev. 45(3), 504–519 (2003)

    MathSciNet  MATH  Google Scholar 

  127. Higham, N.J.: The scaling and squaring method for the matrix exponential function. SIAM J. Matrix Anal. Appl. 26(4), 1179–1193 (2005)

    MathSciNet  MATH  Google Scholar 

  128. Higham, N.J.: Functions of Matrices. Theory and Computation. SIAM, Philadelphia (2008)

    MATH  Google Scholar 

  129. Hoffman, A.J., Wielandt, H.W.: The variation of the spectrum of a normal matrix. Duke Math. J. 20, 37–39 (1953)

    MathSciNet  MATH  Google Scholar 

  130. Horn, R.A., Johnson, C.R.: Topics in Matrix Analysis. Cambridge University Press, Cambridge, UK (1991)

    Google Scholar 

  131. Horn, R.A., Johnson, C.R.: Matrix Analysis, 2nd edn. Cambridge University Press, Cambridge, UK (2012)

    Google Scholar 

  132. Householder, A.S.: The Theory of Matrices in Numerical Analysis, xi+257 pp. Dover, New York (1975) (Corrected republication of work first published in 1964 by Blaisdell Publ. Co, New York)

    Google Scholar 

  133. Huppert, B., Schneider, H. (eds.): Wielandt, Helmut: Matematische werke/Mathematical works, vol.2, Linear Algebra and Analysis. Walter de Gruyter, Berlin (1996)

    Google Scholar 

  134. Iannazzo, B.: On the Newton method for the matrix \(p\)th root. SIAM J. Matrix Anal. Appl. 28(2), 503–523 (2006)

    MathSciNet  MATH  Google Scholar 

  135. Ipsen, I.: Computing an eigenvector with inverse iteration. SIAM Rev. 39, 254–291 (1997)

    MathSciNet  MATH  Google Scholar 

  136. Ipsen, I.: Accurate eigenvalues for fast trains. SIAM News 37, 1–3 (2004) (9 Nov 2004)

    Google Scholar 

  137. Jacobi, C.G.J.: Über ein leichtes Verfahren der in der Theorie der Sekulärstörungen vorkommenden Gleichungen numerisch aufzulösen. Crelle’s J. 30, 51–94 (1846)

    MATH  Google Scholar 

  138. Jarlebring, E., Voss, H.: Rational Krylov methods for nonlinear eigenvalue problems, an iterative method. Appl. Math. 50(6), 543–554 (2005)

    MathSciNet  MATH  Google Scholar 

  139. Jessup, E.R., Sorensen, D.C.: A parallel algorithm for computing the singular value decomposition of a matrix. SIAM J. Matrix. Anal. Appl. 15(2), 530–548 (1994)

    MathSciNet  MATH  Google Scholar 

  140. Jordan, C.: Mémoires sur les formes bilinéaires. J. Meth. Pures. Appl. Déuxieme Série. 19, 35–54 (1874)

    Google Scholar 

  141. Kågström, B.: Numerical computation of matrix functions. Technical Report UMINF-58.77, Department of Information Processing, University of Umeå, Umeå (1977)

    Google Scholar 

  142. Kågström, B., Ruhe, A.: An algorithm for numerical computation of the Jordan normal form of a complex matrix. ACM Trans. Math. Softw. 6(3), 398–419 (1980)

    MATH  Google Scholar 

  143. Kågström, B., Ruhe, A.: Algorithm 560 JNF: an algorithms for numerical computation of the Jordan normal form of a complex matrix. ACM Trans. Math. Softw. 6(3), 437–443 (1980)

    MATH  Google Scholar 

  144. Kågström, B., Kressner, D., Quintana-Ortí, E.S., Quintana-Ortí, G.: Blocked algorithms for the reduction to Hessenberg-triangular form revisited. BIT 48(3), 563–584 (2008)

    MathSciNet  MATH  Google Scholar 

  145. Kahan, W.M.: Accurate eigenvalues of a symmetric tridiagonal matrix. Technical Report No. CS-41, Revised June1968, Computer Science Department, Stanford University (1966)

    Google Scholar 

  146. Kahan, W.M.: Inclusion theorems for clusters of eigenvalues of Hermitian matrices. Technical Report CS42. Computer Science Department, University of Toronto, Toronto (1967)

    Google Scholar 

  147. Kato, T.: Perturbation Theory for Linear Operators, 2nd edn. Springer, New York (1976)

    Google Scholar 

  148. Kenney, C.S., Laub, A.J.: Condition estimates for matrix functions. SIAM J. Matrix. Anal. Approx. 10(2), 191–209 (1989)

    MathSciNet  MATH  Google Scholar 

  149. Kenney, C.S., Laub, A.J.: Padé error estimates for the logarithm of a matrix. Int. J. Control. 10, 707–730 (1989)

    MathSciNet  Google Scholar 

  150. Kenney, C.S., Laub, A.J.: Rational iterative methods for the matrix sign function. SIAM J. Matrix. Anal. Approx. 12(2), 273–291 (1991)

    MathSciNet  MATH  Google Scholar 

  151. Kenney, C.S., Laub, A.J.: On scaling Newton’s method for polar decomposition and the matrix sign function. SIAM J. Matrix. Anal. Approx. 13(3), 688–706 (1992)

    MathSciNet  MATH  Google Scholar 

  152. Kiełbasiński, A., Ziȩtak, K.: Numerical behavior of Higham’s scaled method for polar decomposition. Numer. Algorithms 32(2–3), 105–140 (2003)

    MathSciNet  MATH  Google Scholar 

  153. Knight, P.A., Ruiz, D.: A fast method for matrix balancing. IMA J. Numer. Anal. 37, 1–19 (2012)

    Google Scholar 

  154. Kogbetliantz, E.G.: Solution of linear equations by diagonalization of coefficients matrix. Quart. Appl. Math. 13, 123–132 (1955)

    MathSciNet  MATH  Google Scholar 

  155. Kressner, D.: Numerical Methods for General and Structured Eigenvalue Problems. Number 46 in Lecture Notes in Computational Science and Engineering. Springer, Berlin (2005)

    Google Scholar 

  156. Kressner, D.: The periodic QR algorithms is a disguised QR algorithm. Linear Algebra Appl. 417, 423–433 (2005)

    MathSciNet  Google Scholar 

  157. Kressner, D.: The effect of aggressive early deflation on the convergence of the QR algorithm. SIAM J. Matrix Anal. Appl. 30(2), 805–821 (2008)

    MathSciNet  MATH  Google Scholar 

  158. Kressner, D., Schröder, C., Watkins, D.S.: Implicit QR algorithms for palindromic and even eigenvalue problems. Numer. Algor. 51, 209–238 (2009)

    MATH  Google Scholar 

  159. Kublanovskaya, V.N.: On some algorithms for the solution of the complete eigenvalue problem. Z. Vychisl. Mat. i Mat. Fiz. 1, 555–570 (1961) (In Russian. Translation in. USSR Comput. Math. Math. Phys. 1, 637–657 1962)

    Google Scholar 

  160. Kublanovskaya, V.N.: On a method of solving the complete eigenvalue problem for a degenerate matrix. Z. Vychisl. Mat. i Mat. Fiz. 6, 611–620 (1966) (In Russian. Translation in. USSR Comput. Math. Math. Phys. 6, 1–14 (1968))

    Google Scholar 

  161. Kublanovskaya, V.N.: On an application of Newton’s method to the determination of eigenvalues of \(\lambda \)-matrices. Dokl. Akad. Nauk. SSSR 188, 1240–1241 (1969) (In Russian)

    Google Scholar 

  162. Lancaster, P.: Lambda-Matrices and Vibrating Systems. Pergamon Press, Oxford (1966) (Republished in 2002 by Dover, Mineola)

    Google Scholar 

  163. Lancaster, P., Rodman, L.: The Algebraic Riccati Equation. Oxford University Press, Oxford (1995)

    Google Scholar 

  164. Lancaster, P., Tismenetsky, M.: The Theory of Matrices. With Applications. Academic Press, New York (1985)

    Google Scholar 

  165. Laub, A.: A Schur method for solving algebraic Riccati equations. IEEE Trans. Automatic Control, AC 24, 913–921 (1979)

    Google Scholar 

  166. Li, R.-C.: Solving secular equations stably and efficiently. Technical Report UCB/CSD-94-851, Computer Science Department, University of California, Berkeley (1994)

    Google Scholar 

  167. Li, R.-C.: Relative perturbation theory: I. Eigenvalue and singular value variations. SIAM J. Matrix Anal. Appl. 19(4), 956–982 (1998a)

    Google Scholar 

  168. Li, R.-C.: Relative perturbation theory: II. Eigenspace and singular subspace variations. SIAM J. Matrix Anal. Appl. 20(2), 471–492 (1998b)

    Google Scholar 

  169. Li, S., Gu, M., Parlett, B.N.: A modified dqds algorithm. Submitted (2012)

    Google Scholar 

  170. Lozinskii, S.M.: Error estimate for the numerical integration of ordinary differential equations. Izv. Vysš. Učebn. Zaved Matematika 6, 52–90 (1958). in Russian

    Google Scholar 

  171. Lundström, E., Eldén, L.: Adaptive eigenvalue computations using Newton’s method on the Grassmann manifold. SIAM J. Matrix. Anal. Appl. 23(3), 819–839 (2002)

    MATH  Google Scholar 

  172. Mackey, D.S., Mackey, N., Tisseur, F.: Structured tools for structured matrices. Electron. J. Linear Algebra 332–334, 106–145 (2003)

    MathSciNet  Google Scholar 

  173. Mackey, D.S., Mackey, N., Mehl, C., Mehrmann, V.: Structured polynomial eigenvalue problems: good vibrations from good linearizations. SIAM J. Matrix Anal. Appl. 28(4), 1029–1951 (2006)

    MathSciNet  MATH  Google Scholar 

  174. Mackey, D.S., Mackey, N., Mehl, C., Mehrmann, V.: Numerical methods for palindromic eigenvalue problems: computing the anti-triangular Schur form. Numer. Linear Algebra Appl. 16(1), 63–86 (2009)

    MathSciNet  MATH  Google Scholar 

  175. Markov, A.A.: Wahrscheinlichheitsrechnung, 2nd edn. Leipzig, Liebmann (1912)

    Google Scholar 

  176. Martin, R.S., Wilkinson, J.H.: Reduction of the symmetric eigenproblem \(Ax = \lambda Bx\) and related problems to standard form. Numer. Math. 11, 99–110 (1968) (Also in [339, pp. 303–314])

    Google Scholar 

  177. Mehrmann, V.: Autonomous linear quadratic control problems, theory and numerical solution. In: Lecture Notes in Control and Information Sciences, vol. 163. Springer, Heidelberg (1991)

    Google Scholar 

  178. Mehrmann, V., Voss, H.: Nonlinear eigenvalue problems: a challenge for modern eigenvalue methods. Technical Report UCB/CSD-94-851. Institut für Mathematik, TU Berlin, Berlin (2004)

    Google Scholar 

  179. Meini, B.: The matrix square root from a new functional perspective: theoretical results and computational issues. SIAM J. Matrix Anal. Appl. 26(2), 362–376 (2004)

    MathSciNet  MATH  Google Scholar 

  180. Meyer, C.D.: The role of the group generalized inverse in the theory of finite Markov chains. SIAM Rev. 17, 443–464 (1975)

    MathSciNet  MATH  Google Scholar 

  181. Meyer, C.D.: Matrix Analysis and Applied Linear Algebra. SIAM, Philadelphia (2000)

    Google Scholar 

  182. Meyer, C.D., Plemmons, R.J. (eds.): Linear Algebra, Markov Chains, and Queuing Models. Springer, Berlin (1993)

    Google Scholar 

  183. Moler, C.B.: Cleve’s corner: the world’s largest matrix computation: Google’s PageRank is an eigenvector of 2.7 billion. MATLAB News and Notes, pp. 12–13 (2002)

    Google Scholar 

  184. Moler, C.B., Stewart, G.W.: An algorithm for generalized eigenvalue problems. SIAM J. Numer. Anal. 10, 241–256 (1973)

    MathSciNet  MATH  Google Scholar 

  185. Moler, C.B., Van Loan, C.F.: Nineteen dubious ways to compute the exponential of a matrix. SIAM Rev. 20(4), 801–836 (1978)

    MathSciNet  MATH  Google Scholar 

  186. Moler, C.B., Van Loan, C.F.: Nineteen dubious ways to compute the exponential of a matrix, twentyfive years later. SIAM Rev. 45(1), 3–49 (2003)

    MathSciNet  MATH  Google Scholar 

  187. Osborne, E.E.: On pre-conditioning of matrices. J. Assoc. Comput. Mach. 7, 338–345 (1960)

    MathSciNet  MATH  Google Scholar 

  188. Ostrowski, A.M.: On the convergence of the Rayleigh quotient iteration for computation of the characteristic roots and vectors I-VI. Arch. Rational Mech. Anal. 1, 233–241, 2, 423–428, 3, 325–340, 3, 341–347, 3, 472–481, 4, 153–165 (1958–1959)

    Google Scholar 

  189. Paige, C.C., Saunders, M.A.: Toward a generalized singular value decomposition. SIAM J. Numer. Anal. 18, 398–405 (1981)

    MathSciNet  MATH  Google Scholar 

  190. Paige, C.C., Wei, M.: History and generality of the CS decomposition. Linear Algebra Appl. 208(209), 303–326 (1994)

    MathSciNet  Google Scholar 

  191. Parlett, B.N.: A recurrence among the elements of functions of triangular matrices. Linear Algebra Appl. 14, 117–121 (1976)

    MathSciNet  MATH  Google Scholar 

  192. Parlett, B.N.: Problem, The Symmetric Eigenvalue. SIAM, Philadelphia (1998) (Republished amended version of original published by Prentice-Hall, Englewood Cliffs, 1980)

    Google Scholar 

  193. Parlett, B.N.: The Symmetric Eigenvalue Problem. Prentice-Hall, Englewood Cliffs (1980) (Amended version republished in 1998 by SIAM, Philadelphia)

    Google Scholar 

  194. Parlett, B.N.: The new qd algorithm. Acta Numerica 4, 459–491 (1995)

    MathSciNet  Google Scholar 

  195. Parlett, B.N., Marques, O.A.: An implementation of the dqds algorithm (positive case). Linear Algebra Appl. 309, 217–259 (2000)

    MathSciNet  MATH  Google Scholar 

  196. Parlett, B.N., Reinsch, C.: Balancing a matrix for calculation of eigenvalues and eigenvectors. Numer. Math. 13, 293–304 (1969)

    MathSciNet  MATH  Google Scholar 

  197. Perron, O.: Zur Theorie der Matrizen. Math. Ann. 64, 248–263 (1907)

    MathSciNet  MATH  Google Scholar 

  198. Peters, G., Wilkinson, J.H.: The calculation of specified eigenvectors by inverse iteration. In: Wilkinson, J.H., Reinsch, C. (eds.) Handbook for Automatic Computation. Vol. II, Linear Algebra, pp. 134–151. Springer, New York (1971)

    Google Scholar 

  199. Pisarenko, V.F.: The retrieval of harmonics from a covariance function. Geophys. J. Roy. Astron. Soc. 33, 347–366 (1973)

    MATH  Google Scholar 

  200. Pták, V.: A remark on the Jordan normal form of matrices. Linear Algebra Appl. 310, 5–7 (2000)

    MathSciNet  MATH  Google Scholar 

  201. Reichel, L., Trefethen, L.N.: Eigenvalues and pseudo-eigenvalues of Toeplitz matrices. Linear Algebra Appl. 162–164, 153–185 (1992)

    MathSciNet  Google Scholar 

  202. Ruhe, A.: On the quadratic convergence of the Jacobi method for normal matrices. BIT 7(4), 305–313 (1967)

    MathSciNet  MATH  Google Scholar 

  203. Ruhe, A.: An algorithm for numerical determination of the structure of a general matrix. BIT 10, 196–216 (1970)

    MathSciNet  MATH  Google Scholar 

  204. Ruhe, A.: Algorithms for the nonlinear algebraic eigenvalue problem. SIAM J. Numer. Anal. 10(4), 674–689 (1973)

    MathSciNet  MATH  Google Scholar 

  205. Ruhe, A.: Closest normal matrix finally found. BIT 27(4), 585–594 (1987)

    MathSciNet  MATH  Google Scholar 

  206. Rutishauser, H.: Der Quotienten-Differenzen-Algoritmus. Z. Angew. Math. Phys. 5, 233–251 (1954)

    MathSciNet  MATH  Google Scholar 

  207. Rutishauser, H.: Solution of eigenvalue problems with the LR-transformation. Nat. Bur. Standards Appl. Math. Ser. 49, 47–81 (1958)

    MathSciNet  Google Scholar 

  208. Rutishauser, H.: Über eine kubisch konvergente Variante der LR-Transformation. Z. Angew. Math. Meth. 40, 49–54 (1960)

    MathSciNet  MATH  Google Scholar 

  209. Rutishauser, H.: The Jacobi method for real symmetric matrices. In: Wilkinson, J.H., Reinsch, C. (eds.) Handbook for Automatic Computation, vol. II, Linear Algebra, pp. 134–151. Springer, New York (1966) (Prepublished in Numer. Math. 9, 1–10, 1966)

    Google Scholar 

  210. Rutishauser, H.: Simultaneous iteration method for symmetric matrices. In: Wilkinson, J.H., Reinsch, C. (eds.) Handbook for Automatic Computation, vol. II, Linear Algebra, pp. 134–151. Springer, New York (1970) (Prepublished in Numer. Math. 16, 205–223, 1970)

    Google Scholar 

  211. Saad, Y.: Numerical Methods for Large Eigenvalue Problems. Halstead Press, New York (1992)

    MATH  Google Scholar 

  212. Schulz, G.: Iterativ Berechnung der reciproken Matrize. Z. Angew. Math. Mech 13, 57–59 (1933)

    MATH  Google Scholar 

  213. Schur, I.: Über die characteristischen Würzeln einer linearen Substitution mit einer Anwendung auf die Theorie der Integral Gleichungen. Math. Ann. 66, 448–510 (1909)

    MathSciNet  Google Scholar 

  214. Simonsson, L.: Subspace Computations via Matrix Decompositions and Geometric Optimization. Ph.D. thesis, Linköping Studies in Science and Technology No. 1052, Linköping (2006)

    Google Scholar 

  215. Singer, S., Singer, S.: Skew-symmetric differential qd algorithm. Appl. Numer. Anal. Comp. Math. 2(1), 134–151 (2005)

    MATH  Google Scholar 

  216. Smith, B.T., Boyle, J.M., Dongarra, J.J., Garbow, B.S., Ikebe, Y., Klema, V.C., Moler, C.B.: Matrix Eigensystems Routines–EISPACK Guide, vol. 6 of Lecture Notes in Computer Science, 2nd edn. Springer, New York (1976)

    Google Scholar 

  217. Söderström, T., Stewart, G.W.: On the numerical properties of an iterative method for computing the Moore-Penrose generalized inverse. SIAM J. Numer. Anal. 11(1), 61–74 (1974)

    Google Scholar 

  218. Stewart, G.W.: Error and perturbation bounds for subspaces associated with certain eigenvalue problems. SIAM Rev. 15(4), 727–764 (1973)

    MathSciNet  MATH  Google Scholar 

  219. Stewart, G.W.: Introduction to Matrix Computations. Academic Press, New York (1973)

    MATH  Google Scholar 

  220. Stewart, G.W.: On the perturbation of pseudoinverses, projections and linear least squares problems. SIAM Rev. 19(4), 634–662 (1977)

    MathSciNet  MATH  Google Scholar 

  221. Stewart, G.W.: Matrix Algorithms Volume II: Eigensystems. SIAM, Philadelphia (2001)

    Google Scholar 

  222. Stewart, G.W., Sun, J.-G.: Matrix Perturbation Theory. Academic Press, New York (1990)

    Google Scholar 

  223. Strang, G.: Linear Algebra and Its Applications, 4th edn. SIAM, Philadelphia (2009)

    Google Scholar 

  224. Sutton, B.D.: Computing the complete CS decomposition. Numer. Algor. 50, 33–65 (2009)

    MathSciNet  MATH  Google Scholar 

  225. Sylvester, J.J.: Sur la solution du cas plus général des équations linéaires en quantités binaires, c’est-a-dire en quarternions ou en matrices d’un ordre quelconque. sur l’équationes linéaire trinôme en matrices d’un ordre quelconque. Comptes Rendus Acad. Sci. 99, 117–118, 409–412, 432–436, 527–529 (1884)

    Google Scholar 

  226. Tisseur, F.: Newton’s method in floating point arithmetic and iterative refinement of generalized eigenvalue problems. SIAM J. Matrix Anal. Appl. 22(4), 1038–1057 (2001)

    MathSciNet  MATH  Google Scholar 

  227. Tisseur, F., Meerbergen, K.: The quadratic eigenvalue problem. SIAM Rev. 43(2), 235–286 (2001)

    MathSciNet  MATH  Google Scholar 

  228. Toeplitz, O.: Das algebraische Analogen zu einem Satze von Fejér. Math. Z. 2, 187–197 (1918)

    MathSciNet  MATH  Google Scholar 

  229. Trefethen, L.N.: Pseudospectra of matrices. In: Griffiths, D.F., Watson, G.A. (eds.) Numerical Analysis 1991: Proceedings of the 14th Dundee Biennial Conference. Pitman Research Notes in Mathematics, pp. 234–266. Longman Scientific and Technical, Harlow (1992)

    Google Scholar 

  230. Trefethen, L.N.: Pseudospectra of linear operators. SIAM Rev. 39(3), 383–406 (1997)

    MathSciNet  MATH  Google Scholar 

  231. Trefethen, L.N.: Computation of pseudospectra. Acta Numerica 8, 247–295 (1999)

    MathSciNet  Google Scholar 

  232. Trefethen, L.N., Embree, M.: Spectra and Pseudospectra: The Behavior of Nonnormal Matrices and Operators. Princeton University Press, Princeton (2006)

    Google Scholar 

  233. Van Huffel, S., Vandewalle, J.: The Total Least Squares Problem; Computational Aspects and Analysis. SIAM, Philadelphia (1991)

    MATH  Google Scholar 

  234. Van Loan, C.F.: Generalizing the singular value decomposition. SIAM J. Numer. Anal. 13, 76–83 (1976)

    MathSciNet  MATH  Google Scholar 

  235. Van Loan, C.F.: A symplectic method for approximating all the eigenvalues of a Hamiltonian matrix. Linear Algebra Appl. 61, 233–252 (1982)

    Google Scholar 

  236. Van Zee, F.G., van de Geijn, R.A., Quintana-Ortí, G.: Restructuring the QR algorithm for high-performance application of Givens rotations. FLAME Working Note 60. Department of Computer Science, The University of Texas at Austin, Austin (2011)

    Google Scholar 

  237. Varah, J.M.: A practical examination of some numerical methods for linear discrete ill-posed problems. SIAM Rev. 21, 100–111 (1979)

    MathSciNet  MATH  Google Scholar 

  238. Varga, R.S.: Geršgorin and his circles. In: Number 36 in Series in Computational Mathematics. Springer, Berlin (2004)

    Google Scholar 

  239. Veselić, K.: A Jacobi eigenreduction algorithm for definite matrix pairs. Numer. Math. 64(4), 241–269 (1993)

    MathSciNet  MATH  Google Scholar 

  240. Voss, H.: An Arnoldi method for nonlinear eigenvalue problems. BIT 44(2), 387–401 (2004)

    MathSciNet  MATH  Google Scholar 

  241. Ward, R.C.: Numerical computation of the matrix exponential with accuracy estimate. SIAM J. Numer. Anal. 14(4), 600–610 (1977)

    MathSciNet  MATH  Google Scholar 

  242. Ward, R.C.: Eigensystem computation for skew-symmetric matrices and a class of symmetric matrices. ACM Trans. Math. Softw. 4(3), 278–285 (1978)

    MATH  Google Scholar 

  243. Watkins, D.S.: Understanding the QR algorithm. SIAM Rev. 24, 427–440 (1982)

    MathSciNet  MATH  Google Scholar 

  244. Watkins, D.S.: Fundamentals of Matrix Computation, 2nd edn. Wiley-InterScience, New York (2002)

    Google Scholar 

  245. Watkins, D.S.: Product eigenvalue problems. SIAM Rev. 47(3), 3–40 (2005)

    MathSciNet  MATH  Google Scholar 

  246. Watkins, D.S.: The Matrix Eigenvalue Problem: \(GR\) and Krylov Subspace Methods. SIAM, Philadelphia (2007)

    Google Scholar 

  247. Watkins, D.S.: The QR algorithm revisited. SIAM Rev 50(1), 133–145 (2008)

    MathSciNet  MATH  Google Scholar 

  248. Watkins, D.S.: Francis’s algorithm. Amer. Math. Monthly 118(5), 387–403 (2011)

    MathSciNet  MATH  Google Scholar 

  249. Wielandt, H.: Das Iterationsverfahren bei nicht selbstadjungierten linearen Eigenwertaufgaben. Math. Z. 50, 93–143 (1944)

    MathSciNet  MATH  Google Scholar 

  250. Wilkinson, J.H.: The Algebraic Eigenvalue Problem. Clarendon Press, Oxford (1965)

    MATH  Google Scholar 

  251. Wilkinson, J.H.: Global convergence of tridiagonal QR algorithm with origin shifts. Linear Algebra Appl. 1, 409–420 (1968)

    Google Scholar 

  252. Wilkinson, J.H.: The perfidious polynomial. In: Golub, G.H. (ed.) Studies in Numerical Analysis, pp. 1–28. American Mathematical Society, Providence (1984)

    Google Scholar 

  253. Wilkinson, J.H., Reinsch, C. (eds.) Handbook for Automatic Computation, vol. II Linear Algebra. Springer, New York (1971)

    Google Scholar 

  254. Willems, P.R., Lang, B., Vömel, C.: Computing the bidiagonal SVD using multiple relatively robust representations. SIAM J. Matrix Anal. Appl. 28(4), 907–926 (2006)

    MathSciNet  MATH  Google Scholar 

  255. Wright, T.G.: Algorithms and Software for Pseudospectra, Ph.D. thesis, Numerical Analysis Group, vi+150 pp. Oxford University Computing Laboratory, Oxford (2002)

    Google Scholar 

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Björck, Å. (2015). Matrix Eigenvalue Problems. In: Numerical Methods in Matrix Computations. Texts in Applied Mathematics, vol 59. Springer, Cham. https://doi.org/10.1007/978-3-319-05089-8_3

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