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Journal article
Probabilistic analysis of block Wiedemann for leading invariant factors
Journal of symbolic computation, v 108, pp 98-116
01 Jan 2022
Abstract
We determine the probability, structure dependent, that the block Wiedemann algorithm correctly computes leading invariant factors. This leads to a tight lower bound for the probability, structure independent. We show, using block size slightly larger than r, that the leading r invariant factors are computed correctly with high probability over any field. Moreover, an algorithm is provided to compute the probability bound for a given matrix size and thus to select the block size needed to obtain the desired probability. The worst case probability bound is improved, post hoc, by incorporating the partial information about the invariant factors. (C) 2021 Published by Elsevier Ltd.
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Details
- Title
- Probabilistic analysis of block Wiedemann for leading invariant factors
- Creators
- Gavin Harrison - Drexel UniversityJeremy Johnson - Drexel UniversityDavid Saunders - Univ Delaware, Dept Comp & Informat Sci, Newark, DE 19716 USA
- Publication Details
- Journal of symbolic computation, v 108, pp 98-116
- Publisher
- Elsevier
- Number of pages
- 19
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000679912200008
- Scopus ID
- 2-s2.0-85110279199
- Other Identifier
- 991019168326404721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Theory & Methods
- Mathematics, Applied