Block Discrete Empirical Interpolation Methods

Fuente: arXiv
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Main Authors: Gidisu, Perfect Y., Hochstenbach, Michiel E.
Format: Preprint
Published: 2022
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author Gidisu, Perfect Y.
Hochstenbach, Michiel E.
author_facet Gidisu, Perfect Y.
Hochstenbach, Michiel E.
contents We present block variants of the discrete empirical interpolation method (DEIM); as a particular application, we will consider a CUR factorization. The block DEIM algorithms are based on the concept of the maximum volume of submatrices and a rank-revealing QR factorization. We also present a version of the block DEIM procedures, which allows for adaptive choice of block size. The results of the experiments indicate that the block DEIM algorithms exhibit comparable accuracy for low-rank matrix approximation compared to the standard DEIM procedure. However, the block DEIM algorithms also demonstrate potential computational advantages, showcasing increased efficiency in terms of computational time.
format Preprint
id arxiv_https___arxiv_org_abs_2208_02213
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Block Discrete Empirical Interpolation Methods
Gidisu, Perfect Y.
Hochstenbach, Michiel E.
Numerical Analysis
We present block variants of the discrete empirical interpolation method (DEIM); as a particular application, we will consider a CUR factorization. The block DEIM algorithms are based on the concept of the maximum volume of submatrices and a rank-revealing QR factorization. We also present a version of the block DEIM procedures, which allows for adaptive choice of block size. The results of the experiments indicate that the block DEIM algorithms exhibit comparable accuracy for low-rank matrix approximation compared to the standard DEIM procedure. However, the block DEIM algorithms also demonstrate potential computational advantages, showcasing increased efficiency in terms of computational time.
title Block Discrete Empirical Interpolation Methods
topic Numerical Analysis
url https://arxiv.org/abs/2208.02213