A NEW SMOOTHING SPECTRAL CONJUGATE GRADIENT METHOD FOR SOLVING TENSOR COMPLEMENTARITY PROBLEMS

被引:0
|
作者
Lv, Shichun [1 ]
Du, Shou-qiang [1 ]
机构
[1] Qingdao Univ, Sch Math & Stat, Qingdao 266071, Peoples R China
基金
中国国家自然科学基金;
关键词
Tensor complementarity problem; Unconstrained optimization; Smoothing spectral conjugate gradient method; Global convergence; SUFFICIENT DESCENT PROPERTY; PERRON-FROBENIUS THEOREM; NONNEGATIVE TENSORS; GLOBAL CONVERGENCE; LARGEST EIGENVALUE; RESTART PROCEDURES; FLETCHER-REEVES; EQUATION;
D O I
10.3934/jimo.2021150
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In recent years, the tensor complementarity problem has attracted widespread attention and has been extensively studied. The research work of tensor complementarity problem mainly focused on theory, solution methods and applications. In this paper, we study the solution method of tensor complementarity problem. Based on the equivalence relation of the tensor complementarity problem and unconstrained optimization problem, we propose a new smoothing spectral conjugate gradient method with Armijo line search. Under mild conditions, we establish the global convergence of the proposed method. Finally, some numerical results are given to show the effectiveness of the proposed method and verify our theoretical results.
引用
收藏
页码:4111 / 4127
页数:17
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