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Updating the regularization parameter in the adaptive cubic regularization algorithm

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  • N. Gould

    ()

  • M. Porcelli

    ()

  • P. Toint

    ()

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    Abstract

    The adaptive cubic regularization method (Cartis et al. in Math. Program. Ser. A 127(2):245–295, 2011 ; Math. Program. Ser. A. 130(2):295–319, 2011 ) has been recently proposed for solving unconstrained minimization problems. At each iteration of this method, the objective function is replaced by a cubic approximation which comprises an adaptive regularization parameter whose role is related to the local Lipschitz constant of the objective’s Hessian. We present new updating strategies for this parameter based on interpolation techniques, which improve the overall numerical performance of the algorithm. Numerical experiments on large nonlinear least-squares problems are provided. Copyright Springer Science+Business Media, LLC 2012

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    File URL: http://hdl.handle.net/10.1007/s10589-011-9446-7
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    Bibliographic Info

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

    Volume (Year): 53 (2012)
    Issue (Month): 1 (September)
    Pages: 1-22

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    Handle: RePEc:spr:coopap:v:53:y:2012:i:1:p:1-22

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

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    Related research

    Keywords: Unconstrained optimization; Cubic regularization; Numerical performance;

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