JDQR

From this page you can get a Matlab® implementation of the JDQR algorithm. The JDQR algorithm can be used for computing a few selected eigenvalues with some desirable property together with the associated eigenvectors of a matrix A. The matrix can be real or complex, Hermitian or non-Hermitian, .... The algorithm is effective especially in case A is sparse and of large size. The Jacobi-Davidson method is used to compute a partial Schur decomposition of A. The decomposition leads to the wanted eigenpairs.


References in zbMATH (referenced in 430 articles )

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  1. Bergamaschi, Luca; Bozzo, Enrico: Computing the smallest eigenpairs of the graph Laplacian (2018)
  2. Kressner, Daniel; Luce, Robert: Fast computation of the matrix exponential for a Toeplitz matrix (2018)
  3. Lin, Matthew M.; Chiang, Chun-Yueh: An iterative method for solving the stable subspace of a matrix pencil and its application (2018)
  4. Mele, Giampaolo; Jarlebring, Elias: On restarting the tensor infinite Arnoldi method (2018)
  5. Xue, Fei: A block preconditioned harmonic projection method for large-scale nonlinear eigenvalue problems (2018)
  6. Zhang, Lei-Hong; Shen, Chungen; Yang, Wei Hong; Júdice, Joaquim J.: A Lanczos method for large-scale extreme Lorentz eigenvalue problems (2018)
  7. Adachi, Satoru; Iwata, Satoru; Nakatsukasa, Yuji; Takeda, Akiko: Solving the trust-region subproblem by a generalized eigenvalue problem (2017)
  8. Aishima, Kensuke: On convergence of iterative projection methods for symmetric eigenvalue problems (2017)
  9. 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)
  10. Argentati, Merico E.; Knyazev, Andrew V.; Neymeyr, Klaus; Ovtchinnikov, Evgueni E.; Zhou, Ming: Convergence theory for preconditioned eigenvalue solvers in a nutshell (2017)
  11. Berljafa, Mario; Güttel, Stefan: Parallelization of the rational Arnoldi algorithm (2017)
  12. Betcke, Marta M.; Voss, Heinrich: Restarting iterative projection methods for Hermitian nonlinear eigenvalue problems with minmax property (2017)
  13. Dax, Achiya: The numerical rank of Krylov matrices (2017)
  14. Gaaf, Sarah W.; Jarlebring, Elias: The infinite bi-Lanczos method for nonlinear eigenvalue problems (2017)
  15. Güttel, Stefan; Tisseur, Françoise: The nonlinear eigenvalue problem (2017)
  16. Kiltz, Eike; Pietrzak, Krzysztof; Venturi, Daniele; Cash, David; Jain, Abhishek: Efficient authentication from hard learning problems (2017)
  17. Lin, Lin: Localized spectrum slicing (2017)
  18. Mercier, Sylvain; Gratton, Serge; Tardieu, Nicolas; Vasseur, Xavier: A new preconditioner update strategy for the solution of sequences of linear systems in structural mechanics: application to saddle point problems in elasticity (2017)
  19. Nakatsukasa, Yuji: Accuracy of singular vectors obtained by projection-based SVD methods (2017)
  20. Nakatsukasa, Yuji; Soma, Tasuku; Uschmajew, André: Finding a low-rank basis in a matrix subspace (2017)

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