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 350 articles )

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  1. Breuer, Alex; Lumsdaine, Andrew: Matrix-free Krylov iteration for implicit convolution of numerically low-rank data (2016)
  2. Gaudreau, P.; Slevinsky, R.; Safouhi, H.: The double exponential sinc collocation method for singular Sturm-Liouville problems (2016)
  3. Giani, Stefano; Grubišić, Luka; Międlar, Agnieszka; Ovall, Jeffrey S.: Robust error estimates for approximations of non-self-adjoint eigenvalue problems (2016)
  4. Higham, Nicholas J.; Strabić, Nataša: Bounds for the distance to the nearest correlation matrix (2016)
  5. Huang, Tsung-Ming; Lin, Wen-Wei; Mehrmann, Volker: A Newton-type method with nonequivalence deflation for nonlinear eigenvalue problems arising in photonic crystal modeling (2016)
  6. Nakatsukasa, Yuji; Freund, Roland W.: Computing fundamental matrix decompositions accurately via the matrix sign function in two iterations: the power of Zolotarev’s functions (2016)
  7. Neymeyr, Klaus; Zhou, Ming: Convergence analysis of restarted Krylov subspace eigensolvers (2016)
  8. Palacios, José Luis; Quiroz, Daniel: Birth and death chains on finite trees: computing their stationary distribution and hitting times (2016)
  9. Schröder, Christian; Taslaman, Leo: Backward error analysis of the shift-and-invert Arnoldi algorithm (2016)
  10. Shi, Zhanwen; Yang, Guanyu; Xiao, Yunhai: A limited memory BFGS algorithm for non-convex minimization with applications in matrix largest eigenvalue problem (2016)
  11. Vecharynski, Eugene; Yang, Chao; Xue, Fei: Generalized preconditioned locally harmonic residual method for non-Hermitian eigenproblems (2016)
  12. Aishima, Kensuke: Global convergence of the restarted Lanczos and Jacobi-Davidson methods for symmetric eigenvalue problems (2015)
  13. Brandts, Jan H.; Reis da Silva, Ricardo: On the subspace projected approximate matrix method. (2015)
  14. Ding, Weiyang; Wei, Yimin: Generalized tensor eigenvalue problems (2015)
  15. Hnětynková, Iveta; Plešinger, Martin: Complex wedge-shaped matrices: a generalization of Jacobi matrices (2015)
  16. Jia, Zhongxiao; Li, Cen: Harmonic and refined harmonic shift-invert residual Arnoldi and Jacobi-Davidson methods for interior eigenvalue problems (2015)
  17. Li, Tiexiang; Huang, Wei-Qiang; Lin, Wen-Wei; Liu, Jijun: On spectral analysis and a novel algorithm for transmission eigenvalue problems (2015)
  18. Mas, José; Cerdán, Juana; Malla, Natalia; Marín, José: Application of the Jacobi-Davidson method for spectral low-rank preconditioning in computational electromagnetics problems (2015)
  19. Nakatsukasa, Yuji; Noferini, Vanni; Townsend, Alex: Computing the common zeros of two bivariate functions via Bézout resultants (2015)
  20. Naumov, M.; Arsaev, M.; Castonguay, P.; Cohen, J.; Demouth, J.; Eaton, J.; Layton, S.; Markovskiy, N.; Reguly, I.; Sakharnykh, N.; Sellappan, V.; Strzodka, R.: AmgX: a library for GPU accelerated algebraic multigrid and preconditioned iterative methods (2015)

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