Title

Randomized Iterative Methods for Matrix Approximation

Document Type

Conference Proceeding

Publication Date

1-1-2022

Department

Department of Mathematical Sciences

Abstract

Standard tools to update approximations to a matrix A (for example, Quasi-Newton Hessian approximations in optimization) incorporate computationally expensive one-sided samples AV. This article develops randomized algorithms to efficiently approximate A by iteratively incorporating cheaper two-sided samples U⊤AV. Theoretical convergence rates are proved and realized in numerical experiments. A heuristic accelerated variant is developed and shown to be competitive with existing methods based on one-sided samples.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

9783030954697

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