An SDP relaxation method for perron pairs of a nonnegative tensor
作者：
Highlights：
• We propose a SemiDefinite Programing (SDP) relaxation algorithm to directly compute all Perron eigenvectors of a nonnegative tensor with finite Perron eigenvectors, where all Perron eigenvectors associated with monotonous Perron eigenvalues are generated by solving finite SDP problems.
• We prove the convergence of the proposed algorithm.
• Numerical experiments illustrate the efficiency of the proposed algorithm.
摘要
•We propose a SemiDefinite Programing (SDP) relaxation algorithm to directly compute all Perron eigenvectors of a nonnegative tensor with finite Perron eigenvectors, where all Perron eigenvectors associated with monotonous Perron eigenvalues are generated by solving finite SDP problems.•We prove the convergence of the proposed algorithm.•Numerical experiments illustrate the efficiency of the proposed algorithm.
论文关键词：Nonnegative tensor,Perron eigenvector,Polynomial optimization,SDP Relaxation
论文评审过程：Received 13 July 2020, Revised 7 July 2021, Accepted 10 December 2021, Available online 18 February 2022, Version of Record 18 February 2022.
论文官网地址：https://doi.org/10.1016/j.amc.2021.126866
