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
Recommended Citation
Azzam, J.,
Ong, B.,
&
Struthers, A.
(2022).
Randomized Iterative Methods for Matrix Approximation.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
13164 LNCS, 226-240.
http://doi.org/10.1007/978-3-030-95470-3_17
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15871