A Nonmonotone Adaptive Trust Region Method Based on Conic Model for Unconstrained Optimization. (27th January 2014)

• Record Type: Journal Article
• Authors: Cui, Zhaocheng
• Other Names: Bagirov Adil M. Academic Editor.
• Is Part Of: Journal of optimization. Volume 2014(2014)
• Publication Date: 2014-01-27
• Subjects: Mathematical optimization -- Periodicals; Structural optimization -- Periodicals; Mathematical optimization; Structural optimization; Periodicals; Electronic journals; 519.6
• DOI: 10.1155/2014/237279
• Languages: English
• ISSNs: 2356-752X

Abstract

Abstract : We propose a nonmonotone adaptive trust region method for unconstrained optimization problems which combines a conic model and a new update rule for adjusting the trust region radius. Unlike the traditional adaptive trust region methods, the subproblem of the new method is the conic minimization subproblem. Moreover, at each iteration, we use the last and the current iterative information to define a suitable initial trust region radius. The global and superlinear convergence properties of the proposed method are established under reasonable conditions. Numerical results show that the new method is efficient and attractive for unconstrained optimization problems.