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A new modified nonmonotone adaptive trust region method for unconstrained optimization

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  • Zhaocheng Cui
  • Boying Wu

Abstract

In this paper, we present an adaptive trust region method for solving unconstrained optimization problems which combines nonmonotone technique with a new update rule for the trust region radius. At each iteration, our method can adjust the trust region radius of related subproblem. We construct a new ratio to adjust the next trust region radius which is different from the ratio in the traditional trust region methods. The global and superlinear convergence results of the method are established under reasonable assumptions. Numerical results show that the new method is efficient for unconstrained optimization problems. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Zhaocheng Cui & Boying Wu, 2012. "A new modified nonmonotone adaptive trust region method for unconstrained optimization," Computational Optimization and Applications, Springer, vol. 53(3), pages 795-806, December.
    • Handle: RePEc:spr:coopap:v:53:y:2012:i:3:p:795-806
    DOI: 10.1007/s10589-012-9460-4
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    Citations

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    Cited by:

    1. M. Reza Peyghami & D. Ataee Tarzanagh, 2015. "A relaxed nonmonotone adaptive trust region method for solving unconstrained optimization problems," Computational Optimization and Applications, Springer, vol. 61(2), pages 321-341, June.
    2. Xianfeng Ding & Quan Qu & Xinyi Wang, 2021. "A modified filter nonmonotone adaptive retrospective trust region method," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-16, June.
    3. Jianjun Liu & Xiangmin Xu & Xuehui Cui, 2018. "An accelerated nonmonotone trust region method with adaptive trust region for unconstrained optimization," Computational Optimization and Applications, Springer, vol. 69(1), pages 77-97, January.
    4. D. Ataee Tarzanagh & M. Reza Peyghami & F. Bastin, 2015. "A New Nonmonotone Adaptive Retrospective Trust Region Method for Unconstrained Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 167(2), pages 676-692, November.
    5. Zhou Sheng & Gonglin Yuan, 2018. "An effective adaptive trust region algorithm for nonsmooth minimization," Computational Optimization and Applications, Springer, vol. 71(1), pages 251-271, September.

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