Parallel stochastic estimation method of eigenvalue distribution

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概要

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• Some kinds of eigensolver for large sparse matrices require specification of parameters that are based on rough estimates of the desired eigenvalues. In this paper, we propose a stochastic estimation method of eigenvalue distribution using the combination of a stochastic estimator of the matrix trace and contour integrations. The proposed method can be easily parallelized and applied to matrices for which factorization is infeasible. Numerical experiments are executed to show that the method can perform rough estimates at a low computational cost.

著者

• Tadano Hiroto

  Graduate School of Systems and Information Engineering, University of Tsukuba

• Futamura Yasunori

  Graduate School of Systems and Information Engineering, University of Tsukuba

• Sakurai Tetsuya

  Graduate School of Systems and Information Engineering, University of Tsukuba

関連論文

• A quadrature-based eigensolver with a Krylov subspace method for shifted linear systems for Hermitian eigenproblems in lattice QCD
• Parallel stochastic estimation method of eigenvalue distribution
• A block sparse approximate inverse with cutoff preconditioner for semi-sparse linear systems derived from Molecular Orbital calculations
• A convergence improvement of the BSAIC preconditioner by deflation

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