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A nonmonotone filter method for nonlinear optimization

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Author Info

  • Chungen Shen

    ()

  • Sven Leyffer

    ()

  • Roger Fletcher

    ()

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    Abstract

    We propose a new nonmonotone filter method to promote global and fast local convergence for sequential quadratic programming algorithms. Our method uses two filters: a standard, global g-filter for global convergence, and a local nonmonotone l-filter that allows us to establish fast local convergence. We show how to switch between the two filters efficiently, and we prove global and superlinear local convergence. A special feature of the proposed method is that it does not require second-order correction steps. We present preliminary numerical results comparing our implementation with a classical filter SQP method. Copyright Springer Science+Business Media, LLC 2012

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    File URL: http://hdl.handle.net/10.1007/s10589-011-9430-2
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    Bibliographic Info

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

    Volume (Year): 52 (2012)
    Issue (Month): 3 (July)
    Pages: 583-607

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    Handle: RePEc:spr:coopap:v:52:y:2012:i:3:p:583-607

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

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    Related research

    Keywords: Nonlinear optimization; Nonmonotone filter; Global convergence; Local convergence;

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