An alternating shifted higher order power method based algorithm for rank-R Hermitian approximation and solving Hermitian CP-decomposition problems

IF 2.1 2区 数学 Q1 MATHEMATICS, APPLIED
Xiaofang Xin, Guyan Ni, Ying Li
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引用次数: 0

Abstract

The Hermitian tensor is a higher order extension of the Hermitian matrix that can be used to represent quantum mixed states and solve problems such as entanglement and separability of quantum mixed states. In this paper, we propose a novel numerical algorithm, an alternating shifted higher order power method (AS-HOPM), for rank-R Hermitian approximation, which can also be used to compute Hermitian Candecomp/Parafac (CP) decomposition. At the same time, for the choice of initial points, we give a Broyden–Fletcher–Goldfarb–Shanno (BFGS) method based on unconstrained optimization, and propose a BFGS-AS-HOPM algorithm for rank-R Hermitian approximation. For solving the Hermitian CP-decomposition problem, numerical experiments show that using the BFGS-AS-HOPM algorithm has a higher success rate than using the AS-HOPM algorithm alone.
基于交替移位高阶幂方法的秩R赫米蒂近似算法和赫米蒂CP分解问题求解方法
赫米提张量是赫米提矩阵的高阶扩展,可用来表示量子混合态,解决量子混合态的纠缠和可分离性等问题。本文提出了一种新颖的数值算法--交替移位高阶幂方法(AS-HOPM),用于秩R赫米提近似,也可用于计算赫米提Candecomp/Parafac(CP)分解。同时,对于初始点的选择,我们给出了一种基于无约束优化的 Broyden-Fletcher-Goldfarb-Shanno (BFGS) 方法,并提出了一种用于秩-R 赫米提近似的 BFGS-AS-HOPM 算法。数值实验表明,在求解赫米蒂CP分解问题时,使用BFGS-AS-HOPM算法比单独使用AS-HOPM算法具有更高的成功率。
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来源期刊
CiteScore
5.40
自引率
4.20%
发文量
437
审稿时长
3.0 months
期刊介绍: The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.
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