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 475 articles )

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  1. Aishima, Kensuke: Convergence proof of the harmonic Ritz pairs of iterative projection methods with restart strategies for symmetric eigenvalue problems (2020)
  2. Aristodemo, A.; Gemignani, L.: Accelerating the Sinkhorn-Knopp iteration by Arnoldi-type methods (2020)
  3. 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)
  4. Campos, Carmen; Roman, Jose E.: A polynomial Jacobi-Davidson solver with support for non-monomial bases and deflation (2020)
  5. Carcenac, Manuel; Redif, Soydan: Application of the sequential matrix diagonalization algorithm to high-dimensional functional MRI data (2020)
  6. Dax, Achiya: A cross-product approach for low-rank approximations of large matrices (2020)
  7. Demyanko, Kirill V.; Kaporin, Igor E.; Nechepurenko, Yuri M.: Inexact Newton method for the solution of eigenproblems arising in hydrodynamic temporal stability analysis (2020)
  8. Fukaya, Takeshi; Kannan, Ramaseshan; Nakatsukasa, Yuji; Yamamoto, Yusaku; Yanagisawa, Yuka: Shifted Cholesky QR for computing the QR factorization of ill-conditioned matrices (2020)
  9. Horning, Andrew; Townsend, Alex: FEAST for differential eigenvalue problems (2020)
  10. Jia, Zhongxiao: Approximation accuracy of the Krylov subspaces for linear discrete ill-posed problems (2020)
  11. 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)
  12. Nakatsukasa, Yuji: Sharp error bounds for Ritz vectors and approximate singular vectors (2020)
  13. Rong, Xin; Niu, Ruiping; Liu, Guirong: Stability analysis of smoothed finite element methods with explicit method for transient heat transfer problems (2020)
  14. Tremblay, Nicolas; Loukas, Andreas: Approximating spectral clustering via sampling: a review (2020)
  15. Adachi, Satoru; Nakatsukasa, Yuji: Eigenvalue-based algorithm and analysis for nonconvex QCQP with one constraint (2019)
  16. Altmann, R.; Peterseim, D.: Localized computation of eigenstates of random Schrödinger operators (2019)
  17. Chen, Xiao Shan; Vong, Seak-Weng; Li, Wen; Xu, Hongguo: Noda iterations for generalized eigenproblems following Perron-Frobenius theory (2019)
  18. Georg, Niklas; Ackermann, Wolfgang; Corno, Jacopo; Schöps, Sebastian: Uncertainty quantification for Maxwell’s eigenproblem based on isogeometric analysis and mode tracking (2019)
  19. Goldenberg, Steven; Stathopoulos, Andreas; Romero, Eloy: A Golub-Kahan Davidson method for accurately computing a few singular triplets of large sparse matrices (2019)
  20. Hochstenbach, Michiel E.; Mehl, Christian; Plestenjak, Bor: Solving singular generalized eigenvalue problems by a rank-completing perturbation (2019)

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