A Global Convergence Analysis for Computing a Symmetric Low-Rank Orthogonal Approximation

Wenxin Du (), Shenglong Hu, Youyicun Lin () and Jie Wang ()
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Wenxin Du: Department of Mathematics, School of Science, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, P. R. China
Shenglong Hu: Department of Mathematics, School of Science, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, P. R. China
Youyicun Lin: Department of Mathematics, School of Science, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, P. R. China
Jie Wang: School of Science, China Jiliang University, Hangzhou, Zhejiang 310018, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2022, vol. 39, issue 06, 1-12

Abstract: In this paper, we present a refined convergence analysis for a simple yet powerful method for computing a symmetric low-rank orthogonal approximation of a symmetric tensor proposed in the literature. The significance is that the assumption guaranteeing the global convergence is vastly relaxed to only on an input parameter of this algorithm.

Keywords: Symmetric tensor; low-rank orthogonal approximation; polar decomposition; global convergence (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0217595922500038

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