An alternating structured trust region algorithm for separable optimization problems with nonconvex constraints

Dan Xue, Wenyu Sun, Liqun Qi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)365-386
Number of pages22
JournalComputational Optimization and Applications
Issue number2
Publication statusPublished - 1 Mar 2014


  • Alternating direction methods
  • Filter method
  • Nonconvex programming
  • Separable structure
  • Trust region methods

ASJC Scopus subject areas

  • Applied Mathematics
  • Computational Mathematics
  • Control and Optimization

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