Andreas Grothey and Ken McKinnon
Technical Report MS 98-015
Abstract
Bundle methods for the minimization of non-smooth functions have been
around for almost 20 years. Numerous variations have been
proposed. But until very recently they all suffered from the drawback
of only linear convergence. The aim of this paper is to show how
exploiting an analogy with SQP gives rise to a superlinearly
convergent bundle method. Our algorithm features a trust region
philosophy and is expected to converge superlinearly even for
non-convex problems.
Key words:
Bundle methods, optimization, SQP
Text
(with minor corrections made on 25th June 1999)
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