A filter-trust-region method for unconstrained optimization

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    A new filter-trust-region algorithm for solving unconstrained nonlinear optimization problems is introduced. Based on the filter technique introduced by Fletcher and Leyffer, it extends an existing technique of Gould, Leyffer, and Toint [SIAM J. Optim., 15 (2004), pp. 17-38] for nonlinear equations and nonlinear least-squares to the fully general unconstrained optimization problem. The new algorithm is shown to be globally convergent to at least one second-order critical point, and numerical experiments indicate that it is very competitive with more classical trust-region algorithms. © 2005 Society for Industrial and Applied Mathematics.
    Original languageEnglish
    Pages (from-to)341-357
    Number of pages17
    JournalSIAM Journal on Optimization
    Issue number2
    Publication statusPublished - 1 Jan 2006


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