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 505 articles , 1 standard article )

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  1. Bai, Zhaojun; Lu, Ding; Vandereycken, Bart: Robust Rayleigh quotient minimization and nonlinear eigenvalue problems (2018)
  2. Bergamaschi, Luca; Bozzo, Enrico: Computing the smallest eigenpairs of the graph Laplacian (2018)
  3. Greenbaum, Anne; Kyanfar, Faranges; Salemi, Abbas: On the convergence rate of DGMRES (2018)
  4. Jarlebring, E.; Koskela, A.; Mele, G.: Disguised and new quasi-Newton methods for nonlinear eigenvalue problems (2018)
  5. Krämer, Lukas; Lang, Bruno: Convergence of integration-based methods for the solution of standard and generalized Hermitian eigenvalue problems (2018)
  6. Kressner, Daniel; Luce, Robert: Fast computation of the matrix exponential for a Toeplitz matrix (2018)
  7. Lin, Matthew M.; Chiang, Chun-Yueh: An iterative method for solving the stable subspace of a matrix pencil and its application (2018)
  8. Mele, Giampaolo; Jarlebring, Elias: On restarting the tensor infinite Arnoldi method (2018)
  9. Miao, Cun-Qiang: Computing eigenpairs in augmented Krylov subspace produced by Jacobi-Davidson correction equation (2018)
  10. Xue, Fei: A block preconditioned harmonic projection method for large-scale nonlinear eigenvalue problems (2018)
  11. Yang, Liu; Sun, Yuquan; Gong, Fanghui: The inexact residual iteration method for quadratic eigenvalue problem and the analysis of convergence (2018)
  12. Zhang, Lei-Hong; Shen, Chungen; Yang, Wei Hong; Júdice, Joaquim J.: A Lanczos method for large-scale extreme Lorentz eigenvalue problems (2018)
  13. Zhao, Tao: A convergence analysis of the inexact simplified Jacobi-Davidson algorithm for polynomial eigenvalue problems (2018)
  14. Adachi, Satoru; Iwata, Satoru; Nakatsukasa, Yuji; Takeda, Akiko: Solving the trust-region subproblem by a generalized eigenvalue problem (2017)
  15. Aishima, Kensuke: On convergence of iterative projection methods for symmetric eigenvalue problems (2017)
  16. Antoine, Xavier; Levitt, Antoine; Tang, Qinglin: Efficient spectral computation of the stationary states of rotating Bose-Einstein condensates by preconditioned nonlinear conjugate gradient methods (2017)
  17. Argentati, Merico E.; Knyazev, Andrew V.; Neymeyr, Klaus; Ovtchinnikov, Evgueni E.; Zhou, Ming: Convergence theory for preconditioned eigenvalue solvers in a nutshell (2017)
  18. Bai, Zhong-Zhi; Miao, Cun-Qiang: On local quadratic convergence of inexact simplified Jacobi-Davidson method (2017)
  19. Berljafa, Mario; Güttel, Stefan: Parallelization of the rational Arnoldi algorithm (2017)
  20. Betcke, Marta M.; Voss, Heinrich: Restarting iterative projection methods for Hermitian nonlinear eigenvalue problems with minmax property (2017)

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