Linear convergence of an algorithm for computing the largest eigenvalue of a nonnegative tensor

Liping Zhang; Liqun Qi

doi:10.1002/nla.822

Linear convergence of an algorithm for computing the largest eigenvalue of a nonnegative tensor

Liping Zhang
, Liqun Qi

Research output: Journal article publication › Journal article › Academic research › peer-review

61 Citations (Scopus)

Abstract

An iterative method for finding the largest eigenvalue of a nonnegative tensor was proposed by Ng, Qi, and Zhou in 2009. In this paper, we establish an explicit linear convergence rate of the Ng-Qi-Zhou method for essentially positive tensors. Numerical results are given to demonstrate linear convergence of the Ng-Qi-Zhou algorithm for essentially positive tensors.

Original language	English
Pages (from-to)	830-841
Number of pages	12
Journal	Numerical Linear Algebra with Applications
Volume	19
Issue number	5
DOIs	https://doi.org/10.1002/nla.822
Publication status	Published - 1 Oct 2012

Keywords

Convergence
Eigenvalue
Essentially positive tensor
Iterative method
Nonnegative tensor

ASJC Scopus subject areas

Algebra and Number Theory
Applied Mathematics

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10.1002/nla.822

Cite this

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abstract = "An iterative method for finding the largest eigenvalue of a nonnegative tensor was proposed by Ng, Qi, and Zhou in 2009. In this paper, we establish an explicit linear convergence rate of the Ng-Qi-Zhou method for essentially positive tensors. Numerical results are given to demonstrate linear convergence of the Ng-Qi-Zhou algorithm for essentially positive tensors.",

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Linear convergence of an algorithm for computing the largest eigenvalue of a nonnegative tensor

Abstract

Keywords

ASJC Scopus subject areas

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