期刊
SIAM JOURNAL ON OPTIMIZATION
卷 12, 期 4, 页码 1075-1089出版社
SIAM PUBLICATIONS
DOI: 10.1137/S1052623498339727
关键词
augmented Lagrangian; constrained optimization; direct search; nonlinear programming; pattern search
We give a pattern search method for nonlinearly constrained optimization that is an adaption of a bound constrained augmented Lagrangian method first proposed by Conn, Gould, and Toint [SIAM J. Numer. Anal., 28 ( 1991), pp. 545 572]. In the pattern search adaptation, we solve the bound constrained subproblem approximately using a pattern search method. The stopping criterion proposed by Conn, Gould, and Toint for the solution of the subproblem requires explicit knowledge of derivatives. Such information is presumed absent in pattern search methods; however, we show how we can replace this with a stopping criterion based on the pattern size in a way that preserves the convergence properties of the original algorithm. In this way we proceed by successive, inexact, bound constrained minimization without knowing exactly how inexact the minimization is. As far as we know, this is the first provably convergent direct search method for general nonlinear programming.
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