求弱不可还原非负对称张量谱半径的类幂方法

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED
Xueli Bai, Dong-Hui Li, Lei Wu, Jiefeng Xu
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引用次数: 0

摘要

Perron-Frobenius 定理指出,弱不可还原非负张量的谱半径是与正特征向量相对应的唯一正特征值。考虑到这一事实,本文的目的是找出弱不可还原非负对称张量的谱半径及其对应的正特征向量。通过将特征值问题转化为在封闭凸集上最小化凹函数的等价问题,我们推导出了一种更简单、更便宜的迭代方法,称为类幂法,该方法定义明确。此外,我们还证明了类幂方法产生的特征值估计序列和特征向量评估序列分别线性收敛于谱半径及其相应的特征向量。为了加速该方法,我们引入了线搜索技术。改进后的方法保留了与原始版本相同的收敛特性。大量的数值结果表明,改进方法的性能相当出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A power-like method for finding the spectral radius of a weakly irreducible nonnegative symmetric tensor

A power-like method for finding the spectral radius of a weakly irreducible nonnegative symmetric tensor

The Perron–Frobenius theorem says that the spectral radius of a weakly irreducible nonnegative tensor is the unique positive eigenvalue corresponding to a positive eigenvector. With this fact in mind, the purpose of this paper is to find the spectral radius and its corresponding positive eigenvector of a weakly irreducible nonnegative symmetric tensor. By transforming the eigenvalue problem into an equivalent problem of minimizing a concave function on a closed convex set, we derive a simpler and cheaper iterative method called power-like method, which is well-defined. Furthermore, we show that both sequences of the eigenvalue estimates and the eigenvector evaluations generated by the power-like method Q-linearly converge to the spectral radius and its corresponding eigenvector, respectively. To accelerate the method, we introduce a line search technique. The improved method retains the same convergence property as the original version. Plentiful numerical results show that the improved method performs quite well.

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来源期刊
CiteScore
3.70
自引率
9.10%
发文量
91
审稿时长
10 months
期刊介绍: Computational Optimization and Applications is a peer reviewed journal that is committed to timely publication of research and tutorial papers on the analysis and development of computational algorithms and modeling technology for optimization. Algorithms either for general classes of optimization problems or for more specific applied problems are of interest. Stochastic algorithms as well as deterministic algorithms will be considered. Papers that can provide both theoretical analysis, along with carefully designed computational experiments, are particularly welcome. Topics of interest include, but are not limited to the following: Large Scale Optimization, Unconstrained Optimization, Linear Programming, Quadratic Programming Complementarity Problems, and Variational Inequalities, Constrained Optimization, Nondifferentiable Optimization, Integer Programming, Combinatorial Optimization, Stochastic Optimization, Multiobjective Optimization, Network Optimization, Complexity Theory, Approximations and Error Analysis, Parametric Programming and Sensitivity Analysis, Parallel Computing, Distributed Computing, and Vector Processing, Software, Benchmarks, Numerical Experimentation and Comparisons, Modelling Languages and Systems for Optimization, Automatic Differentiation, Applications in Engineering, Finance, Optimal Control, Optimal Design, Operations Research, Transportation, Economics, Communications, Manufacturing, and Management Science.
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