JDQZ

Matlab® implementation of the JDQZ algorithm. The JDQZ algorithm can be used for computing a few selected eigenvalues with some desirable property together with the associated eigenvectors of a matrix pencil A-lambda*B. The matrices can be real or complex, Hermitian or non-Hermitian, .... The algorithm is effective especially in case A and B are sparse and of large size. The Jacobi-Davidson method is used to compute a partial generalized Schur decomposition of the pair (A,B). The decomposition leads to the wanted eigenpairs.


References in zbMATH (referenced in 600 articles , 1 standard article )

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  1. Bergamaschi, Luca; Caliari, Marco; Vianello, Marco: Efficient approximation of the exponential operator for discrete 2D advection-diffusion problems. (2003)
  2. Bergamaschi, Luca; Pini, Giorgio; Sartoretto, Flavio: Computational experience with sequential and parallel, preconditioned Jacobi--Davidson for large, sparse symmetric matrices (2003)
  3. Datta, Karabi; Hong, Yoopyo; Lee, Ran Baik: Parametrized Newton’s iteration for computing an eigenpair of a real symmetric matrix in an interval (2003)
  4. Graham, Ivan G.; Spence, Alastair; Vainikko, Eero: Parallel iterative methods for Navier-Stokes equations and application to eigenvalue computation (2003)
  5. Hernández, Vicente; Román, Jose E.; Vidal, Vicente: SLEPc: Scalable library for eigenvalue problem computations (2003)
  6. Higham, Nicholas J.; Tisseur, Françoise: Bounds for eigenvalues of matrix polynomials (2003)
  7. Hochstenbach, Michiel E.; van der Vorst, Henk A.: Alternatives to the Rayleigh quotient for the quadratic eigenvalue problem (2003)
  8. Hwang, Tsung-Min; Lin, Wen-Wei; Mehrmann, Volker: Numerical solution of quadratic eigenvalue problems with structure-preserving methods (2003)
  9. Jia, Zhongxiao; Niu, Datian: An implicitly restarted refined bidiagonalization Lanczos method for computing a partial singular value decomposition (2003)
  10. Karniadakis, George Em; Kirby, Robert M. II: Parallel scientific computing in C++ and MPI. A seamless approach to parallel algorithms and their implementation. With CD-ROM (2003)
  11. Knyazev, Andrew V.; Neymeyr, Klaus: Efficient solution of symmetric eigenvalue problems using multigrid preconditioners in the locally optimal block conjugate gradient method (2003)
  12. Knyazev, Andrew V.; Neymeyr, Klaus: A geometric theory for preconditioned inverse iteration. III: A short and sharp convergence estimate for generalized eigenvalue problems (2003)
  13. Li, Ren-Cang; Ye, Qiang: A Krylov subspace method for quadratic matrix polynomials with application to constrained least squares problems (2003)
  14. Meerbergen, Karl: The solution of parametrized symmetric linear systems (2003)
  15. Mehrmann, Volker: Numerical methods for eigenvalue and control problems (2003)
  16. Meylan, Michael H.; Gross, Lutz: A parallel algorithm to find the zeros of a complex analytic function. (2003)
  17. Mičušík, Branislav; Pajdla, Tomáš: Omnidirectional camera model and epipolar geometry estimation by RANSAC with bucketing (2003)
  18. Ovtchinnikov, E.: Convergence estimates for the generalized Davidson method for symmetric eigenvalue problems. I: The preconditioning aspect (2003)
  19. Sakurai, Tetsuya; Sugiura, Hiroshi: A projection method for generalized eigenvalue problems using numerical integration. (2003)
  20. Schwetlick, Hubert; Schnabel, Uwe: Iterative computation of the smallest singular value and the corresponding singular vectors of a matrix. (2003)

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