Gradient trust region algorithm with limited memory BFGS update for nonsmooth convex minimization

Authors:
Gonglin Yuan;Zengxin Wei;Zhongxing Wang
Affiliations:
Department of Mathematics and Information Science, Guangxi University, Nanning, P.R. China 530004;Department of Mathematics and Information Science, Guangxi University, Nanning, P.R. China 530004;Department of Mathematics and Information Science, Guangxi University, Nanning, P.R. China 530004
Venue:
Computational Optimization and Applications
Year:
2013

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Abstract

By means of a gradient strategy, the Moreau-Yosida regularization, limited memory BFGS update, and proximal method, we propose a trust-region method for nonsmooth convex minimization. The search direction is the combination of the gradient direction and the trust-region direction. The global convergence of this method is established under suitable conditions. Numerical results show that this method is competitive to other two methods.