Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators

DOI10.1137/18M1189324zbMath1461.41001arXiv1805.06018OpenAlexW2963477565WikidataQ127459963 ScholiaQ127459963MaRDI QIDQ5230654

Tan Bui-Thanh, Nick Alger, Vishwas Rao, Omar Ghattas, Aaron Myers

Publication date: 28 August 2019

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

Full work available at URL: https://arxiv.org/abs/1805.06018

zbMATH Keywords

convolutionoperator approximationmatrix-freehierarchical matrixdata scalabilityPDE constrained inverse problems

Mathematics Subject Classification ID

Optimality conditions for problems involving partial differential equations (49K20) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Linear operator approximation theory (47A58) Convolution, factorization for one variable harmonic analysis (42A85) Approximation by operators (in particular, by integral operators) (41A35) Probabilistic methods for one variable harmonic analysis (42A61) Preconditioners for iterative methods (65F08)

Related Items (4)

An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement ⋮ Learning physics-based models from data: perspectives from inverse problems and model reduction ⋮ Hierarchical off-diagonal low-rank approximation of Hessians in inverse problems, with application to ice sheet model initialization ⋮ Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs

Uses Software

• hlib
• PyAMG
• GitHub

Cites Work

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