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A new modified nonmonotone adaptive trust region method for unconstrained optimization

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  • Zhaocheng Cui

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  • Boying Wu
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    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

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    File URL: http://hdl.handle.net/10.1007/s10589-012-9460-4
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    Bibliographic Info

    Article provided by Springer in its journal Computational Optimization and Applications.

    Volume (Year): 53 (2012)
    Issue (Month): 3 (December)
    Pages: 795-806

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    Handle: RePEc:spr:coopap:v:53:y:2012:i:3:p:795-806

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    Web page: http://www.springer.com/math/journal/10589

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    Related research

    Keywords: Unconstrained optimization; Adaptive trust region method; Nonmonotone technique; Global convergence; Superlinear convergence;

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