Globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds

Andy Conn, N. I. M. Gould, Philippe L. Toint

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    Abstract

    The global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems are considered. In such methods, simple bound constraints are treated separately from more general constraints and the stopping rules for the inner minimization algorithm have this in mind. Global convergence is proved, and it is established that a potentially troublesome penalty parameter is bounded away from zero.
    Original languageEnglish
    Pages (from-to)545-572
    Number of pages28
    JournalSIAM Journal on Numerical Analysis
    Volume28
    Issue number2
    Publication statusPublished - 1 Apr 1991

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