Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26829
Title: An alternating structured trust region algorithm for separable optimization problems with nonconvex constraints
Authors: Xue, D
Sun, W
Qi, L 
Keywords: Alternating direction methods
Filter method
Nonconvex programming
Separable structure
Trust region methods
Issue Date: 2014
Publisher: Springer
Source: Computational optimization and applications, 2014, v. 57, no. 2, p. 365-386 How to cite?
Journal: Computational optimization and applications 
Abstract: In this paper, we propose a structured trust-region algorithm combining with filter technique to minimize the sum of two general functions with general constraints. Specifically, the new iterates are generated in the Gauss-Seidel type iterative procedure, whose sizes are controlled by a trust-region type parameter. The entries in the filter are a pair: one resulting from feasibility; the other resulting from optimality. The global convergence of the proposed algorithm is proved under some suitable assumptions. Some preliminary numerical results show that our algorithm is potentially efficient for solving general nonconvex optimization problems with separable structure.
URI: http://hdl.handle.net/10397/26829
ISSN: 0926-6003
EISSN: 1573-2894
DOI: 10.1007/s10589-013-9597-9
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