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A Nonmonotone Adaptive Trust Region Method Based on Conic Model for Unconstrained Optimization

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  • Zhaocheng Cui

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.

Suggested Citation

  • Zhaocheng Cui, 2014. "A Nonmonotone Adaptive Trust Region Method Based on Conic Model for Unconstrained Optimization," Journal of Optimization, Hindawi, vol. 2014, pages 1-8, January.
    • Handle: RePEc:hin:jjopti:237279
    DOI: 10.1155/2014/237279
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    References listed on IDEAS

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    1. Wenyu Sun & Ya-xiang Yuan, 2001. "A Conic Trust-Region Method for Nonlinearly Constrained Optimization," Annals of Operations Research, Springer, vol. 103(1), pages 175-191, March.
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