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A new non-monotone self-adaptive trust region method for unconstrained optimization

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Abstract

In this paper, based on a simple model of trust region sub-problem, we combine the trust region method with the non-monotone and self-adaptive techniques to propose a new non-monotone self-adaptive trust region algorithm for unconstrained optimization. By use of the simple model, the new method needs less memory capacitance, computational complexity and CPU time. The convergence results of the method are proved under certain conditions. Numerical results show that the new method is effective and attractive for large-scale optimization problems.

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Authors and Affiliations

  1. Collage of Mathematical and Computational Science, China University of Petroleum (Huadong), Qingdao, 266555, China

    Zhaoyang Sang & Qingying Sun

Authors
  1. Zhaoyang Sang
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  2. Qingying Sun
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Correspondence to Zhaoyang Sang.

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This work is supported by the National Natural Science Foundation of China (10571106).

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Sang, Z., Sun, Q. A new non-monotone self-adaptive trust region method for unconstrained optimization. J. Appl. Math. Comput. 35, 53–62 (2011). https://doi.org/10.1007/s12190-009-0339-1

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  • DOI: https://doi.org/10.1007/s12190-009-0339-1

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