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 557 articles , 1 standard article )

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  1. Herrero, Henar; Maday, Yvon; Pla, Francisco: Reduced basis method applied to a convective stability problem (2018)
  2. Jarlebring, E.; Koskela, A.; Mele, G.: Disguised and new quasi-Newton methods for nonlinear eigenvalue problems (2018)
  3. Krämer, Lukas; Lang, Bruno: Convergence of integration-based methods for the solution of standard and generalized Hermitian eigenvalue problems (2018)
  4. Kressner, Daniel; Luce, Robert: Fast computation of the matrix exponential for a Toeplitz matrix (2018)
  5. Kwok, Felix: Numerical methods for spectral theory (2018)
  6. Lee, Dongjin; Hoshi, Takeo; Sogabe, Tomohiro; Miyatake, Yuto; Zhang, Shao-Liang: Solution of the (k)-th eigenvalue problem in large-scale electronic structure calculations (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. Lu, Zhongjie; van der Vegt, J. J. W.; Xu, Yan: Spectral approximation for polynomial eigenvalue problems (2018)
  9. Mele, Giampaolo; Jarlebring, Elias: On restarting the tensor infinite Arnoldi method (2018)
  10. Miao, Cun-Qiang: Computing eigenpairs in augmented Krylov subspace produced by Jacobi-Davidson correction equation (2018)
  11. Tomljanović, Zoran; Beattie, Christopher; Gugercin, Serkan: Damping optimization of parameter dependent mechanical systems by rational interpolation (2018)
  12. Xue, Fei: A block preconditioned harmonic projection method for large-scale nonlinear eigenvalue problems (2018)
  13. Yang, Liu; Sun, Yuquan; Gong, Fanghui: The inexact residual iteration method for quadratic eigenvalue problem and the analysis of convergence (2018)
  14. Zhang, Jingyuan: A stable explicitly solvable numerical method for the Riesz fractional advection-dispersion equations (2018)
  15. Zhang, Lei-Hong; Shen, Chungen; Yang, Wei Hong; Júdice, Joaquim J.: A Lanczos method for large-scale extreme Lorentz eigenvalue problems (2018)
  16. Zhao, Tao: A convergence analysis of the inexact simplified Jacobi-Davidson algorithm for polynomial eigenvalue problems (2018)
  17. Adachi, Satoru; Iwata, Satoru; Nakatsukasa, Yuji; Takeda, Akiko: Solving the trust-region subproblem by a generalized eigenvalue problem (2017)
  18. Aishima, Kensuke: On convergence of iterative projection methods for symmetric eigenvalue problems (2017)
  19. 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)
  20. Argentati, Merico E.; Knyazev, Andrew V.; Neymeyr, Klaus; Ovtchinnikov, Evgueni E.; Zhou, Ming: Convergence theory for preconditioned eigenvalue solvers in a nutshell (2017)

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