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      A map of contour integral-based eigensolvers for solving generalized eigenvalue problems

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          Abstract

          Recently, contour integral-based eigensolvers have been actively studied for solving interior eigenvalue problems that find all eigenvalues located in a certain region and their corresponding eigenvectors. In this paper, we reconsider the algorithms of the five typical contour integral-based eigensolvers from the view point of projection methods, and then map the relationships among these methods. From the analysis, we conclude that all contour integral-based eigensolvers can be regarded as projection methods and can be categorized on their subspace, an orthogonal condition and a problem to be applied implicitly.

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          A projection method for generalized eigenvalue problems using numerical integration

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            A Density Matrix-based Algorithm for Solving Eigenvalue Problems

            A new numerical algorithm for solving the symmetric eigenvalue problem is presented. The technique deviates fundamentally from the traditional Krylov subspace iteration based techniques (Arnoldi and Lanczos algorithms) or other Davidson-Jacobi techniques, and takes its inspiration from the contour integration and density matrix representation in quantum mechanics. It will be shown that this new algorithm - named FEAST - exhibits high efficiency, robustness, accuracy and scalability on parallel architectures. Examples from electronic structure calculations of Carbon nanotubes (CNT) are presented, and numerical performances and capabilities are discussed.
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              An integral method for solving nonlinear eigenvalue problems

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                Author and article information

                Journal
                1510.02572

                Numerical & Computational mathematics
                Numerical & Computational mathematics

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