Randomized estimation of spectral densities of large matrices made accurate

DOI10.1007/s00211-016-0837-7zbMath1364.15007arXiv1504.07690OpenAlexW2963762865MaRDI QIDQ527821

Lin Lin

Publication date: 12 May 2017

Published in: Numerische Mathematik (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1504.07690

zbMATH Keywords

Hermitian matrixspectral densitymemory costcomputational costlow rank decompositionrandomized method

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Eigenvalues, singular values, and eigenvectors (15A18)

Related Items (7)

Randomized matrix-free trace and log-determinant estimators ⋮ Improved Variants of the Hutch++ Algorithm for Trace Estimation ⋮ Fast Computation of Spectral Densities for Generalized Eigenvalue Problems ⋮ Krylov-Aware Stochastic Trace Estimation ⋮ XT<scp>race</scp>: Making the Most of Every Sample in Stochastic Trace Estimation ⋮ Randomized block Krylov subspace methods for trace and log-determinant estimators ⋮ How Accurately Should I Compute Implicit Matrix-Vector Products When Applying the Hutchinson Trace Estimator?

Uses Software

• FEAST
• SparseMatrix
• lobpcg.m

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