MS 96-004 Abstract

A Superlinearly Convergent Trust Region Bundle Method

Andreas Grothey and Ken McKinnon

Technical Report MS 98-015

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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