Computing $f(A)b$ via Least Squares Polynomial Approximations

DOI10.1137/090778250zbMath1234.65027OpenAlexW2042545789MaRDI QIDQ3095055

Jie Chen, Yousef Saad, Mihai Anitescu

Publication date: 28 October 2011

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/090778250

zbMATH Keywords

Gaussian processsplineerror bounddiagonalizable matrixmatrix functionleast squares polynomialmatrix times vector evaluation

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Numerical computation of matrix norms, conditioning, scaling (65F35) Matrix exponential and similar functions of matrices (15A16) Numerical computation of matrix exponential and similar matrix functions (65F60)

Related Items (10)

A blocking scheme for dimension-robust Gibbs sampling in large-scale image deblurring ⋮ Randomized matrix-free trace and log-determinant estimators ⋮ Fast Computation of Spectral Densities for Generalized Eigenvalue Problems ⋮ Fast Estimation of $tr(f(A))$ via Stochastic Lanczos Quadrature ⋮ Limited‐memory polynomial methods for large‐scale matrix functions ⋮ Multipreconditioned Gmres for Shifted Systems ⋮ Stochastic approximation of score functions for Gaussian processes ⋮ Vector estimates for \(f(A)\mathbf b\) via extrapolation ⋮ Efficient D-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems ⋮ How Accurately Should I Compute Implicit Matrix-Vector Products When Applying the Hutchinson Trace Estimator?

Uses Software

• SparseMatrix

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