lobpcg.m, MATLAB implementation of the locally optimal block preconditioned conjugate gradient method: Toward the optimal preconditioned eigensolver: Locally optimal block preconditioned conjugate gradient method. We describe new algorithms of the locally optimal block preconditioned conjugate gradient (LOBPCG) method for symmetric eigenvalue problems, based on a local optimization of a three-term recurrence, and suggest several other new methods. To be able to compare numerically different methods in the class, with different preconditioners, we propose a common system of model tests, using random preconditioners and initial guesses. As the “ideal” control algorithm, we advocate the standard preconditioned conjugate gradient method for finding an eigenvector as an element of the null-space of the corresponding homogeneous system of linear equations under the assumption that the eigenvalue is known. We recommend that every new preconditioned eigensolver be compared with this “ideal” algorithm on our model test problems in terms of the speed of convergence, costs of every iteration, and memory requirements. We provide such comparison for our LOBPCG method. Numerical results establish that our algorithm is practically as efficient as the “ideal” algorithm when the same preconditioner is used in both methods. We also show numerically that the LOBPCG method provides approximations to first eigenpairs of about the same quality as those by the much more expensive global optimization method on the same generalized block Krylov subspace. We propose a new version of block Davidson’s method as a generalization of the LOBPCG method. Finally, direct numerical comparisons with the Jacobi-Davidson method show that our method is more robust and converges almost two times faster.

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  11. Jolivet, Pierre; Roman, Jose E.; Zampini, Stefano: KSPHPDDM and PCHPDDM: extending PETSc with advanced Krylov methods and robust multilevel overlapping Schwarz preconditioners (2021)
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  15. Miao, Cun-Qiang; Wu, Wen-Ting: On relaxed filtered Krylov subspace method for non-symmetric eigenvalue problems (2021)
  16. Pollock, Sara; Scott, L. Ridgway: Extrapolating the Arnoldi algorithm to improve eigenvector convergence (2021)
  17. Williamson, Kevin; Cho, Heyrim; Sousedík, Bedřich: Application of adaptive ANOVA and reduced basis methods to the stochastic Stokes-Brinkman problem (2021)
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  20. Kelley, C. T.; Bernholc, J.; Briggs, E. L.; Hamilton, Steven; Lin, Lin; Yang, Chao: Mesh independence of the generalized Davidson algorithm (2020)

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