Conjugate gradient-like methods for solving general tensor equation with Einstein product

被引:16
|
作者
Hajarian, Masoud [1 ]
机构
[1] Shahid Beheshti Univ, Fac Math Sci, Dept Math, Gen Campus, Tehran 19839, Iran
关键词
ALKALMAZ-MAT-LAPOK; MULTILINEAR SYSTEMS; ITERATIVE ALGORITHMS; ARBITRARY MATRICES; DIRECTIONS;
D O I
10.1016/j.jfranklin.2020.01.010
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Tensors have a wide application in data mining, chemistry, information sciences, documents analysis and medical engineering. In this work, we study the general tensor equation Sigma(l)(i=1) F-i *(P) chi *(Q) G(i) = H with Einstein product where F-i, G(i), H, for i = 1, 2, ..., l, are known tensors and chi is an unknown tensor to be determined. The main motivation for this study is the investigation of conjugate gradient-like methods for solving this tensor equation. We show that the conjugate gradient-like methods converge to tensor solutions in a finite number of steps in the absence of round-off errors. Numerical examples confirm 'oretical results and demonstrate the accuracy and computational efficiency of the methods. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
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收藏
页码:4272 / 4285
页数:14
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