Singular-value (and eigenvalue) distribution and Krylov preconditioning of sequences of sampling matrices approximating integral operators

DOI10.1002/nla.1922zbMath1340.31001OpenAlexW1483999018MaRDI QIDQ2948082

Malik Zaka Ullah, Stefano Serra Capizzano, Debora Sesana, Abdulrahman S. Al-Fhaid

Publication date: 29 September 2015

Published in: Numerical Linear Algebra with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/nla.1922

zbMATH Keywords

integral equation preconditioning eigenvalue distribution GMRES integral operators singular value distribution sampling matrices

Mathematics Subject Classification ID

Numerical computation of matrix norms, conditioning, scaling (65F35) Approximation by operators (in particular, by integral operators) (41A35) Integral representations, integral operators, integral equations methods in two dimensions (31A10)

Related Items (12)

Spectral analysis of finite-dimensional approximations of \(1d\) waves in non-uniform grids ⋮ Distribution results for a special class of matrix sequences: joining approximation theory and asymptotic linear algebra ⋮ Block Generalized Locally Toeplitz Sequences: Topological Construction, Spectral Distribution Results, and Star-Algebra Structure ⋮ A note on the eigenvalues of \(g\)-circulants (and of \(g\)-Toeplitz, \(g\)-Hankel matrices) ⋮ Spectral and convergence analysis of the discrete ALIF method ⋮ A modified version of the Pisarenko method to estimate the power spectral density of any asymptotically wide sense stationary vector process ⋮ Eigenvalue-eigenvector structure of Schoenmakers-Coffey matrices via Toeplitz technology and applications ⋮ A short survey on preconditioners and Korovkin-type theorems ⋮ Block generalized locally Toeplitz sequences: theory and applications in the unidimensional case ⋮ Block generalized locally Toeplitz sequences: theory and applications in the multidimensional case ⋮ Block generalized locally Toeplitz sequences: from the theory to the applications ⋮ Unfoldings and the rank-one approximation of the tensor

Uses Software

Matlab

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