Technical Report MS 02-001
Abstract
Bundle methods for the minimization of nonsmooth functions have been
around for almost 20 years. Numerous variations have been
proposed. But until recently they all suffered from the drawback
of only linear convergence or were limited to convex problems.
This paper presents a new bundle variant which exploits an analogy
with SQP to give rise to a superlinearly
convergent bundle method. The presented algorithm features a trust region
rather than a line-search strategy. We will prove global convergence
of the algorithm and give computational results underpinning the claim
of superlinear convergence even for nonconvex problems.
Key words:
bundle-methods, nonsmooth optimization, nonconvex
optimization, second-order methods
Text
dvi MS 02-001.dvi (~87Kb).
postscript MS 02-001.ps (~1.1Mb).
G-Zipped postscript MS 02-001.ps.gz (~0.6Mb).