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 600 articles , 1 standard article )

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  1. Nasser, Rayan; Sadkane, Miloud: Convergence and preconditioning of inexact inverse subspace iteration for generalized eigenvalue problems (2020)
  2. Ravibabu, Mashetti; Singh, Arindama: The least squares and line search in extracting eigenpairs in Jacobi-Davidson method (2020)
  3. Rong, Xin; Niu, Ruiping; Liu, Guirong: Stability analysis of smoothed finite element methods with explicit method for transient heat transfer problems (2020)
  4. Schilling, Nathanael; Froyland, Gary; Junge, Oliver: Higher-order finite element approximation of the dynamic Laplacian (2020)
  5. Tremblay, Nicolas; Loukas, Andreas: Approximating spectral clustering via sampling: a review (2020)
  6. Wang, Li; Zhang, Lei-hong; Bai, Zhaojun; Li, Ren-Cang: Orthogonal canonical correlation analysis and applications (2020)
  7. Xu, Fei; Xie, Hehu; Zhang, Ning: A parallel augmented subspace method for eigenvalue problems (2020)
  8. Yushu, Dewen; Matouš, Karel: The image-based multiscale multigrid solver, preconditioner, and reduced order model (2020)
  9. Adachi, Satoru; Nakatsukasa, Yuji: Eigenvalue-based algorithm and analysis for nonconvex QCQP with one constraint (2019)
  10. Altmann, R.; Peterseim, D.: Localized computation of eigenstates of random Schrödinger operators (2019)
  11. Bai, Zhong-Zhi; Miao, Cun-Qiang: Computing eigenpairs of Hermitian matrices in perfect Krylov subspaces (2019)
  12. Chen, Xiao Shan; Vong, Seak-Weng; Li, Wen; Xu, Hongguo: Noda iterations for generalized eigenproblems following Perron-Frobenius theory (2019)
  13. Dax, Achiya: Computing the smallest singular triplets of a large matrix (2019)
  14. Georg, Niklas; Ackermann, Wolfgang; Corno, Jacopo; Schöps, Sebastian: Uncertainty quantification for Maxwell’s eigenproblem based on isogeometric analysis and mode tracking (2019)
  15. Goldenberg, Steven; Stathopoulos, Andreas; Romero, Eloy: A Golub-Kahan Davidson method for accurately computing a few singular triplets of large sparse matrices (2019)
  16. Hochstenbach, Michiel E.; Mehl, Christian; Plestenjak, Bor: Solving singular generalized eigenvalue problems by a rank-completing perturbation (2019)
  17. Huang, Ruihao; Mu, Lin: A new fast method of solving the high dimensional elliptic eigenvalue problem (2019)
  18. 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)
  19. Huhtanen, Marko; Kotila, Vesa: Optimal quotients for solving large eigenvalue problems (2019)
  20. Ismail, M. E. H.; Ranga, A. Sri: (R_II) type recurrence, generalized eigenvalue problem and orthogonal polynomials on the unit circle (2019)

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