A convexity-like structure for polar decomposition with an application to distributed computing

שמור ב:
מידע ביבליוגרפי
הוצא לאור ב:arXiv.org (Dec 18, 2024), p. n/a
מחבר ראשי: Alimisis, Foivos
מחברים אחרים: Vandereycken, Bart
יצא לאור:
Cornell University Library, arXiv.org
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גישה מקוונת:Citation/Abstract
Full text outside of ProQuest
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MARC

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100 1 |a Alimisis, Foivos 
245 1 |a A convexity-like structure for polar decomposition with an application to distributed computing 
260 |b Cornell University Library, arXiv.org  |c Dec 18, 2024 
513 |a Working Paper 
520 3 |a We make a full landscape analysis of the (generally non-convex) orthogonal Procrustes problem. This problem is equivalent with computing the polar factor of a square matrix. We reveal a convexity-like structure, which explains the already established tractability of the problem and show that gradient descent in the orthogonal group computes the polar factor of a square matrix with linear convergence rate if the matrix is invertible and with an algebraic one if the matrix is singular. These results are similar to the ones of Alimisis and Vandereycken (2024) for the symmetric eigenvalue problem. We present an instance of a distributed Procrustes problem, which is hard to deal by standard techniques from numerical linear algebra. Our theory though can provide a solution. 
653 |a Eigenvalues 
653 |a Matrix algebra 
653 |a Linear algebra 
653 |a Matrices (mathematics) 
653 |a Convexity 
653 |a Distributed processing 
700 1 |a Vandereycken, Bart 
773 0 |t arXiv.org  |g (Dec 18, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3147267459/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.13990