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 434 articles )

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  1. Huang, Wei-Qiang; Lin, Wen-Wei; Lu, Henry Horng-Shing; Yau, Shing-Tung: iSIRA: integrated shift-invert residual Arnoldi method for graph Laplacian matrices from big data (2019)
  2. Ismail, M. E. H.; Ranga, A. Sri: $R_II$ type recurrence, generalized eigenvalue problem and orthogonal polynomials on the unit circle (2019)
  3. Bai, Zhaojun; Lu, Ding; Vandereycken, Bart: Robust Rayleigh quotient minimization and nonlinear eigenvalue problems (2018)
  4. Bergamaschi, Luca; Bozzo, Enrico: Computing the smallest eigenpairs of the graph Laplacian (2018)
  5. Kressner, Daniel; Luce, Robert: Fast computation of the matrix exponential for a Toeplitz matrix (2018)
  6. Lin, Matthew M.; Chiang, Chun-Yueh: An iterative method for solving the stable subspace of a matrix pencil and its application (2018)
  7. Mele, Giampaolo; Jarlebring, Elias: On restarting the tensor infinite Arnoldi method (2018)
  8. Tomljanović, Zoran; Beattie, Christopher; Gugercin, Serkan: Damping optimization of parameter dependent mechanical systems by rational interpolation (2018)
  9. Xue, Fei: A block preconditioned harmonic projection method for large-scale nonlinear eigenvalue problems (2018)
  10. Zhang, Lei-Hong; Shen, Chungen; Yang, Wei Hong; Júdice, Joaquim J.: A Lanczos method for large-scale extreme Lorentz eigenvalue problems (2018)
  11. Adachi, Satoru; Iwata, Satoru; Nakatsukasa, Yuji; Takeda, Akiko: Solving the trust-region subproblem by a generalized eigenvalue problem (2017)
  12. Aishima, Kensuke: On convergence of iterative projection methods for symmetric eigenvalue problems (2017)
  13. 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)
  14. Argentati, Merico E.; Knyazev, Andrew V.; Neymeyr, Klaus; Ovtchinnikov, Evgueni E.; Zhou, Ming: Convergence theory for preconditioned eigenvalue solvers in a nutshell (2017)
  15. Berljafa, Mario; Güttel, Stefan: Parallelization of the rational Arnoldi algorithm (2017)
  16. Betcke, Marta M.; Voss, Heinrich: Restarting iterative projection methods for Hermitian nonlinear eigenvalue problems with minmax property (2017)
  17. Dax, Achiya: The numerical rank of Krylov matrices (2017)
  18. Gaaf, Sarah W.; Jarlebring, Elias: The infinite bi-Lanczos method for nonlinear eigenvalue problems (2017)
  19. Güttel, Stefan; Tisseur, Françoise: The nonlinear eigenvalue problem (2017)
  20. Kiltz, Eike; Pietrzak, Krzysztof; Venturi, Daniele; Cash, David; Jain, Abhishek: Efficient authentication from hard learning problems (2017)

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