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.
Wilkinson received the Chauvenet Prize of the Mathematical Association of America 1987 for this exposition of the ill-conditioning of polynomial zeros.
- 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.
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.
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.
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.
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.
- 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.
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.
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.
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.
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.
This name was chosen because of the connection with Schur’s work on bounded analytic functions.
- 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.
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.
Laub [165, 1979] used a similar idea for computing the Hamiltonian Schur form using information from an unstructured Schur form.
- 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.
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.
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.
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.
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.
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.
It is known that if this condition holds, \(A\) belongs to a set that forms a multiplicative group under ordinary matrix multiplication.
References
Abou-Kandil, H., Freiling, G., Ionescu, V., Jank, G.: Matrix Riccati Equations in Control and Systems Theory. Birkhäuser, Basel, Switzerland (2003)
Absil, P.-A., Mahony, R., Sepulchre, R.: Optimization Algorithms on Matrix Manifolds. Princeton University Press, Princeton (2007)
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)
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)
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)
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)
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)
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)
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)
Arnold, W., Laub, A.: Generalized eigenproblem algorithms for solving the algebraic Riccati equation. Proc. IEEE 72(12), 1746–1754 (1984)
Bai, Z., Demmel, J.W.: Computing the generalized singular value decomposition. SIAM J. Sci. Comput. 14(6), 1464–1486 (1993)
Bai, Z., Demmel, J.W.: Using the matrix sign function to compute invariant subspaces. SIAM J. Matrix Anal. Appl. 19(1), 205–225 (1998)
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)
Bai, Z., Demmel, J.W., McKenney, A.: On computing condition numbers for the nonsymmetric eigenproblem. ACM Trans. Math. Softw. 19(1), 202–223 (1993)
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)
Baker, G.A. Jr., Graves-Morris, P.: Padé Approximants. Encyclopedia Math. Appl. 59, 2nd edn. Cambridge University Press, Cambridge (1996)
Bartels, R.H., Stewart, G.W.: Algorithm 432: solution of the equation \(AX + XB = C\). Comm. ACM 15, 820–826 (1972)
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)
Bauer, F.L.: Das Verfahren der Treppeniteration und verwandte Verfahren zur Lösung algebraischer Eigenwertprobleme. Z. Angew. Math. Phys. 8, 214–235 (1957)
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)
Bhatia, R.: Matrix Analysis. Graduate Texts in Mathematics, vol. 169. Springer, New York (1997)
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)
Bini, D.A., Latouche, G., Meini, B.: Numerical Methods for Structured Markov Chains. Oxford University Press, Oxford (2005)
Björck, Å., Bowie, C.: An iterative algorithm for computing the best estimate of an orthogonal matrix. SIAM J. Numer. Anal. 8, 358–364 (1971)
Björck, Å., Hammarling, S.: A Schur method for the square root of a matrix. Linear Algebra Appl. 52(53), 127–140 (1983)
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)
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)
de Boor, C.: On Pták’s derivation of the Jordan normal form. Linear Algebra Appl. 310, 9–10 (2000)
Braconnier, T., Higham, N.J.: Computing the field of values and pseudospectra using the Lanczos method with continuation. BIT 36(3), 422–440 (1996)
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)
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)
Bryan, K., Leise, T.: The \({\$}\)25, 000, 000, 000 eigenvector: the linear algebra behind Google. SIAM Rev. 48(3), 569–581 (2006)
Bunch, J.R.: Stability of methods for solving Toeplitz systems of equations. SIAM J. Sci. Stat. Comp. 6(2), 349–364 (1985)
Bunch, J.R., Nielsen, C.P., Sorensen, D.C.: Rank-one modifications of the symmetric tridiagonal eigenproblem. Numer. Math. 31(1), 31–48 (1978)
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)
Byers, R.: A Hamiltonian QR algorithm. SIAM J. Sci. Statist. Comput. 7(1), 212–229 (1986)
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)
Cardoso, J.R., Leite, F.S.: Theoretical and numerical considerations about Padé approximants for the matrix logarithm. Linear Algebra Appl. 330, 31–42 (2001)
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)
Chan, T.: An improved algorithm for computing the singular value decomposition. ACM Trans. Math. Softw. 8(1), 72–83 (1982)
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)
Chatelin, F.: Simultaneous Newton’s corrections for the eigenproblem. In: Defect Correction Methods, Comput. Suppl. 5, pp. 67–74. Springer, Vienna (1984)
Chatelin, F.: Eigenvalues of Matrices. Number 71 in Classics in Applied Mathematics. SIAM, Philadelphia (2012) (Revised edition of book published by Wiley, Chichester 1993)
Crawford, C.R.: Reduction of a band-symmetric generalized eigenvalue problem. Comm. Assoc. Comput. Mach. 16, 41–44 (1973)
Crawford, C.R., Moon, Y.S.: Finding a positive definite linear combination of two Hermitian matrices. Linear Algebra Appl. 51, 37–48 (1983)
Cuppen, J.J.M.: A divide and conquer method for the symmetric tridiagonal eigenproblem. Numer. Math. 36(2), 177–195 (1981)
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)
Dahlquist, G., Björck, Å.: Numerical Methods in Scientific Computing, vol. I. SIAM, Philadelphia (2008)
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)
Davies, P.I., Higham, N.J.: A Schur-Parlett algorithm for computing matrix functions. SIAM J. Matrix Anal. Appl. 25(2), 464–485 (2003)
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)
Davis, C., Kahan, W.M.: Some new bounds on perturbation of subspaces. Bull. Amer. Math. Soc. 75, 863–868 (1969)
Davis, C., Kahan, W.M.: The rotation of eigenvectors by a perturbation. SIAM J. Numer. Anal. 7(1), 1–46 (1970)
Demmel, J.W.: Three ways for refining estimates of invariant subspaces. Computing 38, 43–57 (1987)
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)
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)
Demmel, J.W., Kahan, W.: Accurate singular values of bidiagonal matrices. SIAM J. Sci. Stat. Comput. 11(5), 873–912 (1990)
Demmel, J.W., Veselić, K.: Jacobi’s method is more accurate than QR. SIAM J. Matrix Anal. Appl. 13(4), 1204–1245 (1992)
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)
Denman, E.D., Beavers, A.N.: The matrix sign function and computations in systems. Appl. Math. Comput. 2, 63–94 (1976)
Dhillon, I.S.: A New \(O(n^2)\) Algorithm for the Symmetric Tridiagonal Eigenvalue/Eigenvector Problem. Ph.D. thesis, University of California, Berkeley (1997)
Dhillon, I.S.: Current inverse iteration software can fail. BIT 38(4), 685–704 (1998)
Dhillon, I.S., Parlett, B.N.: Orthogonal eigenvectors and relative gaps. SIAM J. Matrix Anal. Appl. 25(3), 858–899 (2004)
Dhillon, I.S., Parlett, B.N.: Multiple representations to compute orthogonal eigenvectors of symmetric tridiagonal matrices. Linear Algebra Appl. 387, 1–28 (2004)
Dhillon, I.S., Parlett, B.N., Vömel, C.: The design and implementation of the MRRR algorithm. ACM Trans. Math. Softw. 32, 533–560 (2006)
Dieci, L., Morini, B., Papini, A.: Computational techniques for real logarithms of matrices. SIAM J. Matrix. Anal. Approx. 17(3), 570–593 (1996)
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)
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)
Drazin, M.P.: Pseudo inverses in associative rays and semigroups. Amer. Math. Monthly 65, 506–514 (1958)
Drmač, Z.: Implementation of Jacobi rotations for accurate singular value computation in floating point arithmetic. SIAM J. Sci. Comput. 18(4), 1200–1222 (1997)
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)
Edelman, A., Arias, T., Smith, S.T.: The geometry of algorithms with orthogonality constraints. SIAM J. Matrix. Anal. Appl. 20(2), 303–353 (1999)
Eisenstat, S.C., Ipsen, I.C.F.: Relative perturbation techniques for singular value problems. SIAM J. Numer. Anal. 32(6), 1972–1988 (1995)
Eldén, L.: Matrix Methods in Data Mining and Pattern Recognition. SIAM, Philadelphia (2007)
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)
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)
Fernando, K.V., Parlett, B.N.: Accurate singular values and differential qd algorithms. Numer. Math. 67, 191–229 (1994)
Fiedler, M.: Special Matrices and Their Applications in Numerical Mathematics, 2nd edn. Dover, Mineola (2008)
Fischer, E.: Über quadratische Formen mit reeller Koeffizienten. Monatshefte Math. Phys. 16, 234–249 (1905)
Fletcher, R., Sorensen, D.C.: An algorithmic derivation of the Jordan canonical form. Am. Math. Mon. 90, 12–16 (1983)
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)
Francis, J.G.F.: The QR transformation. Part I. Comput. J. 4, 265–271 (1961–1962)
Francis, J.G.F.: The QR transformation. Part II. Comput. J. 4, 332–345 (1961–1962)
Frank, W.L.: Computing eigenvalues of complex matrices by determinant evaluation and by methods of Danilewski and Wielandt. J. SIAM 6, 378–392 (1958)
Frobenius, G.: Über Matrizen aus nicht negativen Elementen. Sitzungsber. Königl. Preuss. Akad. Wiss., pp. 456–477. Berlin (1912)
Fröberg, C.-E.: Numerical Mathematics. Theory and Computer Applications. Benjamin/Cummings, Menlo Park (1985)
Gander, W.: Algorithms for the polar decomposition. SIAM J. Sc. Stat. Comput. 11(6), 1102–1115 (1990)
Gander, W.: Zeros of determinants of \(\lambda \)-matrices. Proc. Appl. Math. Mech. 8(1), 10811–10814 (2008)
Gantmacher, F.R.: The Theory of Matrices, vol. II, ix+276 pp. Chelsea Publishing Co, New York (1959)
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)
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)
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)
Geršgorin, S.A.: Über die Abgrenzung der Eigenwerte einer Matrix. Akademia Nauk SSSR, Math. Nat. Sci. 6, 749–754 (1931)
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)
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)
Gohberg, I., Lancaster, P., Rodman, L.: Matrix Polynomials. Academic Press, New York (1982) (Republished in 1964 by SIAM, Philadelphia)
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.)
Goldstine, H.H., Horwitz, L.P.: A procedure for the diagonalization of normal matrices. J. Assoc. Comput. Mach. 6, 176–195 (1959)
Goldstine, H.H., Murray, H.H., von Neumann, J.: The Jacobi method for real symmetric matrices. J. Assoc. Comput. Mach. 6, 59–96 (1959)
Golub, G.H.: Least squares, singular values and matrix approximations. Aplikace Matematiky 13, 44–51 (1968)
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)
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)
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)
Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore (1983)
Golub, G.H., van der Vorst, H.A.: Eigenvalue computations in the 20th century. J. Comput. Appl. Math. 123, 35–65 (2000)
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)
Gragg, W.B.: The QR algorithm for unitary Hessenberg matrices. J. Comp. Appl. Math. 16, 1–8 (1986)
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)
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)
Grcar, J.F.: Operator coefficient methods for linear equations. Report SAND 89–8691, Sandia National Laboratory (1989)
Großer, B., Lang, B.: An \(O(n^2)\) algorithm for the bidiagonal SVD. Linear Algebra Appl. 358, 45–70 (2003)
Großer, B., Lang, B.: On symmetric eigenproblems induced by the bidiagonal SVD. SIAM J. Matrix. Anal. Appl. 26(3), 599–620 (2005)
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)
Gu, M., Eisenstat, S.C.: A divide-and-conquer algorithm for the bidiagonal SVD. SIAM J. Matrix. Anal. Appl. 16(1), 79–92 (1995)
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)
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)
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)
Hammarling, S.J.: Numerical solution of the stable non-negative definite Lyapunov equation. IMA J. Numer. Anal. 2, 303–323 (1982)
Hari, V., Veselić, K.: On Jacobi’s method for singular value decompositions. SIAM J. Sci. Stat. Comput. 8(5), 741–754 (1987)
Henrici, P.: Bounds for iterates, inverses, spectral variation and fields of values of non-normal matrices. Numer. Math. 4, 24–40 (1962)
Hestenes, M.R.: Inversion of matrices by biorthogonalization and related results. J. Soc. Indust. Appl. Math. 6, 51–90 (1958)
Higham, N.J.: Computing the polar decomposition–with applications. SIAM J. Sci. Stat. Comput. 7(4), 1160–1174 (1986)
Higham, N.J.: Computing real square roots of a real matrix. Linear Algebra Appl. 88(89), 405–430 (1987)
Higham, N.J.: Evaluating Padé approximants of the matrix logarithm. SIAM J. Matrix Anal. Appl. 22(4), 1126–1135 (2001)
Higham, N.J.: The matrix computation toolbox for MATLAB (Version 1.0). Numerical Analysis Report 410. Department of Mathematics, The University of Manchester (2002)
Higham, N.J.: \(J\)-Orthogonal matrices: properties and generation. SIAM Rev. 45(3), 504–519 (2003)
Higham, N.J.: The scaling and squaring method for the matrix exponential function. SIAM J. Matrix Anal. Appl. 26(4), 1179–1193 (2005)
Higham, N.J.: Functions of Matrices. Theory and Computation. SIAM, Philadelphia (2008)
Hoffman, A.J., Wielandt, H.W.: The variation of the spectrum of a normal matrix. Duke Math. J. 20, 37–39 (1953)
Horn, R.A., Johnson, C.R.: Topics in Matrix Analysis. Cambridge University Press, Cambridge, UK (1991)
Horn, R.A., Johnson, C.R.: Matrix Analysis, 2nd edn. Cambridge University Press, Cambridge, UK (2012)
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)
Huppert, B., Schneider, H. (eds.): Wielandt, Helmut: Matematische werke/Mathematical works, vol.2, Linear Algebra and Analysis. Walter de Gruyter, Berlin (1996)
Iannazzo, B.: On the Newton method for the matrix \(p\)th root. SIAM J. Matrix Anal. Appl. 28(2), 503–523 (2006)
Ipsen, I.: Computing an eigenvector with inverse iteration. SIAM Rev. 39, 254–291 (1997)
Ipsen, I.: Accurate eigenvalues for fast trains. SIAM News 37, 1–3 (2004) (9 Nov 2004)
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)
Jarlebring, E., Voss, H.: Rational Krylov methods for nonlinear eigenvalue problems, an iterative method. Appl. Math. 50(6), 543–554 (2005)
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)
Jordan, C.: Mémoires sur les formes bilinéaires. J. Meth. Pures. Appl. Déuxieme Série. 19, 35–54 (1874)
Kågström, B.: Numerical computation of matrix functions. Technical Report UMINF-58.77, Department of Information Processing, University of Umeå, Umeå (1977)
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)
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)
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)
Kahan, W.M.: Accurate eigenvalues of a symmetric tridiagonal matrix. Technical Report No. CS-41, Revised June1968, Computer Science Department, Stanford University (1966)
Kahan, W.M.: Inclusion theorems for clusters of eigenvalues of Hermitian matrices. Technical Report CS42. Computer Science Department, University of Toronto, Toronto (1967)
Kato, T.: Perturbation Theory for Linear Operators, 2nd edn. Springer, New York (1976)
Kenney, C.S., Laub, A.J.: Condition estimates for matrix functions. SIAM J. Matrix. Anal. Approx. 10(2), 191–209 (1989)
Kenney, C.S., Laub, A.J.: Padé error estimates for the logarithm of a matrix. Int. J. Control. 10, 707–730 (1989)
Kenney, C.S., Laub, A.J.: Rational iterative methods for the matrix sign function. SIAM J. Matrix. Anal. Approx. 12(2), 273–291 (1991)
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)
Kiełbasiński, A., Ziȩtak, K.: Numerical behavior of Higham’s scaled method for polar decomposition. Numer. Algorithms 32(2–3), 105–140 (2003)
Knight, P.A., Ruiz, D.: A fast method for matrix balancing. IMA J. Numer. Anal. 37, 1–19 (2012)
Kogbetliantz, E.G.: Solution of linear equations by diagonalization of coefficients matrix. Quart. Appl. Math. 13, 123–132 (1955)
Kressner, D.: Numerical Methods for General and Structured Eigenvalue Problems. Number 46 in Lecture Notes in Computational Science and Engineering. Springer, Berlin (2005)
Kressner, D.: The periodic QR algorithms is a disguised QR algorithm. Linear Algebra Appl. 417, 423–433 (2005)
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)
Kressner, D., Schröder, C., Watkins, D.S.: Implicit QR algorithms for palindromic and even eigenvalue problems. Numer. Algor. 51, 209–238 (2009)
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)
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))
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)
Lancaster, P.: Lambda-Matrices and Vibrating Systems. Pergamon Press, Oxford (1966) (Republished in 2002 by Dover, Mineola)
Lancaster, P., Rodman, L.: The Algebraic Riccati Equation. Oxford University Press, Oxford (1995)
Lancaster, P., Tismenetsky, M.: The Theory of Matrices. With Applications. Academic Press, New York (1985)
Laub, A.: A Schur method for solving algebraic Riccati equations. IEEE Trans. Automatic Control, AC 24, 913–921 (1979)
Li, R.-C.: Solving secular equations stably and efficiently. Technical Report UCB/CSD-94-851, Computer Science Department, University of California, Berkeley (1994)
Li, R.-C.: Relative perturbation theory: I. Eigenvalue and singular value variations. SIAM J. Matrix Anal. Appl. 19(4), 956–982 (1998a)
Li, R.-C.: Relative perturbation theory: II. Eigenspace and singular subspace variations. SIAM J. Matrix Anal. Appl. 20(2), 471–492 (1998b)
Li, S., Gu, M., Parlett, B.N.: A modified dqds algorithm. Submitted (2012)
Lozinskii, S.M.: Error estimate for the numerical integration of ordinary differential equations. Izv. Vysš. Učebn. Zaved Matematika 6, 52–90 (1958). in Russian
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)
Mackey, D.S., Mackey, N., Tisseur, F.: Structured tools for structured matrices. Electron. J. Linear Algebra 332–334, 106–145 (2003)
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)
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)
Markov, A.A.: Wahrscheinlichheitsrechnung, 2nd edn. Leipzig, Liebmann (1912)
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])
Mehrmann, V.: Autonomous linear quadratic control problems, theory and numerical solution. In: Lecture Notes in Control and Information Sciences, vol. 163. Springer, Heidelberg (1991)
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)
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)
Meyer, C.D.: The role of the group generalized inverse in the theory of finite Markov chains. SIAM Rev. 17, 443–464 (1975)
Meyer, C.D.: Matrix Analysis and Applied Linear Algebra. SIAM, Philadelphia (2000)
Meyer, C.D., Plemmons, R.J. (eds.): Linear Algebra, Markov Chains, and Queuing Models. Springer, Berlin (1993)
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)
Moler, C.B., Stewart, G.W.: An algorithm for generalized eigenvalue problems. SIAM J. Numer. Anal. 10, 241–256 (1973)
Moler, C.B., Van Loan, C.F.: Nineteen dubious ways to compute the exponential of a matrix. SIAM Rev. 20(4), 801–836 (1978)
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)
Osborne, E.E.: On pre-conditioning of matrices. J. Assoc. Comput. Mach. 7, 338–345 (1960)
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)
Paige, C.C., Saunders, M.A.: Toward a generalized singular value decomposition. SIAM J. Numer. Anal. 18, 398–405 (1981)
Paige, C.C., Wei, M.: History and generality of the CS decomposition. Linear Algebra Appl. 208(209), 303–326 (1994)
Parlett, B.N.: A recurrence among the elements of functions of triangular matrices. Linear Algebra Appl. 14, 117–121 (1976)
Parlett, B.N.: Problem, The Symmetric Eigenvalue. SIAM, Philadelphia (1998) (Republished amended version of original published by Prentice-Hall, Englewood Cliffs, 1980)
Parlett, B.N.: The Symmetric Eigenvalue Problem. Prentice-Hall, Englewood Cliffs (1980) (Amended version republished in 1998 by SIAM, Philadelphia)
Parlett, B.N.: The new qd algorithm. Acta Numerica 4, 459–491 (1995)
Parlett, B.N., Marques, O.A.: An implementation of the dqds algorithm (positive case). Linear Algebra Appl. 309, 217–259 (2000)
Parlett, B.N., Reinsch, C.: Balancing a matrix for calculation of eigenvalues and eigenvectors. Numer. Math. 13, 293–304 (1969)
Perron, O.: Zur Theorie der Matrizen. Math. Ann. 64, 248–263 (1907)
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)
Pisarenko, V.F.: The retrieval of harmonics from a covariance function. Geophys. J. Roy. Astron. Soc. 33, 347–366 (1973)
Pták, V.: A remark on the Jordan normal form of matrices. Linear Algebra Appl. 310, 5–7 (2000)
Reichel, L., Trefethen, L.N.: Eigenvalues and pseudo-eigenvalues of Toeplitz matrices. Linear Algebra Appl. 162–164, 153–185 (1992)
Ruhe, A.: On the quadratic convergence of the Jacobi method for normal matrices. BIT 7(4), 305–313 (1967)
Ruhe, A.: An algorithm for numerical determination of the structure of a general matrix. BIT 10, 196–216 (1970)
Ruhe, A.: Algorithms for the nonlinear algebraic eigenvalue problem. SIAM J. Numer. Anal. 10(4), 674–689 (1973)
Ruhe, A.: Closest normal matrix finally found. BIT 27(4), 585–594 (1987)
Rutishauser, H.: Der Quotienten-Differenzen-Algoritmus. Z. Angew. Math. Phys. 5, 233–251 (1954)
Rutishauser, H.: Solution of eigenvalue problems with the LR-transformation. Nat. Bur. Standards Appl. Math. Ser. 49, 47–81 (1958)
Rutishauser, H.: Über eine kubisch konvergente Variante der LR-Transformation. Z. Angew. Math. Meth. 40, 49–54 (1960)
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)
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)
Saad, Y.: Numerical Methods for Large Eigenvalue Problems. Halstead Press, New York (1992)
Schulz, G.: Iterativ Berechnung der reciproken Matrize. Z. Angew. Math. Mech 13, 57–59 (1933)
Schur, I.: Über die characteristischen Würzeln einer linearen Substitution mit einer Anwendung auf die Theorie der Integral Gleichungen. Math. Ann. 66, 448–510 (1909)
Simonsson, L.: Subspace Computations via Matrix Decompositions and Geometric Optimization. Ph.D. thesis, Linköping Studies in Science and Technology No. 1052, Linköping (2006)
Singer, S., Singer, S.: Skew-symmetric differential qd algorithm. Appl. Numer. Anal. Comp. Math. 2(1), 134–151 (2005)
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)
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)
Stewart, G.W.: Error and perturbation bounds for subspaces associated with certain eigenvalue problems. SIAM Rev. 15(4), 727–764 (1973)
Stewart, G.W.: Introduction to Matrix Computations. Academic Press, New York (1973)
Stewart, G.W.: On the perturbation of pseudoinverses, projections and linear least squares problems. SIAM Rev. 19(4), 634–662 (1977)
Stewart, G.W.: Matrix Algorithms Volume II: Eigensystems. SIAM, Philadelphia (2001)
Stewart, G.W., Sun, J.-G.: Matrix Perturbation Theory. Academic Press, New York (1990)
Strang, G.: Linear Algebra and Its Applications, 4th edn. SIAM, Philadelphia (2009)
Sutton, B.D.: Computing the complete CS decomposition. Numer. Algor. 50, 33–65 (2009)
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)
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)
Tisseur, F., Meerbergen, K.: The quadratic eigenvalue problem. SIAM Rev. 43(2), 235–286 (2001)
Toeplitz, O.: Das algebraische Analogen zu einem Satze von Fejér. Math. Z. 2, 187–197 (1918)
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)
Trefethen, L.N.: Pseudospectra of linear operators. SIAM Rev. 39(3), 383–406 (1997)
Trefethen, L.N.: Computation of pseudospectra. Acta Numerica 8, 247–295 (1999)
Trefethen, L.N., Embree, M.: Spectra and Pseudospectra: The Behavior of Nonnormal Matrices and Operators. Princeton University Press, Princeton (2006)
Van Huffel, S., Vandewalle, J.: The Total Least Squares Problem; Computational Aspects and Analysis. SIAM, Philadelphia (1991)
Van Loan, C.F.: Generalizing the singular value decomposition. SIAM J. Numer. Anal. 13, 76–83 (1976)
Van Loan, C.F.: A symplectic method for approximating all the eigenvalues of a Hamiltonian matrix. Linear Algebra Appl. 61, 233–252 (1982)
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)
Varah, J.M.: A practical examination of some numerical methods for linear discrete ill-posed problems. SIAM Rev. 21, 100–111 (1979)
Varga, R.S.: Geršgorin and his circles. In: Number 36 in Series in Computational Mathematics. Springer, Berlin (2004)
Veselić, K.: A Jacobi eigenreduction algorithm for definite matrix pairs. Numer. Math. 64(4), 241–269 (1993)
Voss, H.: An Arnoldi method for nonlinear eigenvalue problems. BIT 44(2), 387–401 (2004)
Ward, R.C.: Numerical computation of the matrix exponential with accuracy estimate. SIAM J. Numer. Anal. 14(4), 600–610 (1977)
Ward, R.C.: Eigensystem computation for skew-symmetric matrices and a class of symmetric matrices. ACM Trans. Math. Softw. 4(3), 278–285 (1978)
Watkins, D.S.: Understanding the QR algorithm. SIAM Rev. 24, 427–440 (1982)
Watkins, D.S.: Fundamentals of Matrix Computation, 2nd edn. Wiley-InterScience, New York (2002)
Watkins, D.S.: Product eigenvalue problems. SIAM Rev. 47(3), 3–40 (2005)
Watkins, D.S.: The Matrix Eigenvalue Problem: \(GR\) and Krylov Subspace Methods. SIAM, Philadelphia (2007)
Watkins, D.S.: The QR algorithm revisited. SIAM Rev 50(1), 133–145 (2008)
Watkins, D.S.: Francis’s algorithm. Amer. Math. Monthly 118(5), 387–403 (2011)
Wielandt, H.: Das Iterationsverfahren bei nicht selbstadjungierten linearen Eigenwertaufgaben. Math. Z. 50, 93–143 (1944)
Wilkinson, J.H.: The Algebraic Eigenvalue Problem. Clarendon Press, Oxford (1965)
Wilkinson, J.H.: Global convergence of tridiagonal QR algorithm with origin shifts. Linear Algebra Appl. 1, 409–420 (1968)
Wilkinson, J.H.: The perfidious polynomial. In: Golub, G.H. (ed.) Studies in Numerical Analysis, pp. 1–28. American Mathematical Society, Providence (1984)
Wilkinson, J.H., Reinsch, C. (eds.) Handbook for Automatic Computation, vol. II Linear Algebra. Springer, New York (1971)
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)
Wright, T.G.: Algorithms and Software for Pseudospectra, Ph.D. thesis, Numerical Analysis Group, vi+150 pp. Oxford University Computing Laboratory, Oxford (2002)
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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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