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.

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References in zbMATH (referenced in 489 articles )

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  1. Breiding, Paul; Vannieuwenhoven, Nick: The condition number of Riemannian approximation problems (2021)
  2. Aishima, Kensuke: Convergence proof of the harmonic Ritz pairs of iterative projection methods with restart strategies for symmetric eigenvalue problems (2020)
  3. Aristodemo, A.; Gemignani, L.: Accelerating the Sinkhorn-Knopp iteration by Arnoldi-type methods (2020)
  4. Benner, Peter; Bujanović, Zvonimir; Kürschner, Patrick; Saak, Jens: A numerical comparison of different solvers for large-scale, continuous-time algebraic Riccati equations and LQR problems (2020)
  5. Blekherman, Grigoriy; Kummer, Mario; Riener, Cordian; Schweighofer, Markus; Vinzant, Cynthia: Generalized eigenvalue methods for Gaussian quadrature rules (2020)
  6. Campos, Carmen; Roman, Jose E.: A polynomial Jacobi-Davidson solver with support for non-monomial bases and deflation (2020)
  7. Carcenac, Manuel; Redif, Soydan: Application of the sequential matrix diagonalization algorithm to high-dimensional functional MRI data (2020)
  8. Dax, Achiya: A cross-product approach for low-rank approximations of large matrices (2020)
  9. Demyanko, Kirill V.; Kaporin, Igor E.; Nechepurenko, Yuri M.: Inexact Newton method for the solution of eigenproblems arising in hydrodynamic temporal stability analysis (2020)
  10. Fukaya, Takeshi; Kannan, Ramaseshan; Nakatsukasa, Yuji; Yamamoto, Yusaku; Yanagisawa, Yuka: Shifted Cholesky QR for computing the QR factorization of ill-conditioned matrices (2020)
  11. Horning, Andrew; Townsend, Alex: FEAST for differential eigenvalue problems (2020)
  12. Jia, Zhongxiao: Approximation accuracy of the Krylov subspaces for linear discrete ill-posed problems (2020)
  13. Litvinenko, Alexander; Logashenko, Dmitry; Tempone, Raul; Wittum, Gabriel; Keyes, David: Solution of the 3D density-driven groundwater flow problem with uncertain porosity and permeability (2020)
  14. Lu, Ding: Nonlinear eigenvector methods for convex minimization over the numerical range (2020)
  15. Medale, Marc; Cochelin, Bruno; Bissen, Edouard; Alpy, Nicolas: A one-dimensional full-range two-phase model to efficiently compute bifurcation diagrams in sub-cooled boiling flows in vertical heated tube (2020)
  16. Miao, Cun-Qiang: On Chebyshev-Davidson method for symmetric generalized eigenvalue problems (2020)
  17. Mitchell, Tim: Computing the Kreiss constant of a matrix (2020)
  18. Nakatsukasa, Yuji: Sharp error bounds for Ritz vectors and approximate singular vectors (2020)
  19. Ravibabu, Mashetti; Singh, Arindama: The least squares and line search in extracting eigenpairs in Jacobi-Davidson method (2020)
  20. Rong, Xin; Niu, Ruiping; Liu, Guirong: Stability analysis of smoothed finite element methods with explicit method for transient heat transfer problems (2020)

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