On the Global Convergence of a Filter--SQP Algorithm

A mechanism for proving global convergence in SQP--filter methods for nonlinear programming (NLP) is described. Such methods are characterized by their use of thedominance concept of multiobjective optimization, instead of a penalty parameter whose adjustment can be problematic. The main point of in...

Full description

Saved in:
Bibliographic Details
Published inSIAM journal on optimization Vol. 13; no. 1; pp. 44 - 59
Main Authors Fletcher, Roger, Leyffer, Sven, Toint, Philippe L.
Format Journal Article
LanguageEnglish
Published Philadelphia Society for Industrial and Applied Mathematics 01.01.2002
Subjects
Algorithms
Applied mathematics
Online AccessGet full text

Cover

More Information
Summary:A mechanism for proving global convergence in SQP--filter methods for nonlinear programming (NLP) is described. Such methods are characterized by their use of thedominance concept of multiobjective optimization, instead of a penalty parameter whose adjustment can be problematic. The main point of interest is to demonstrate how convergence for NLP can be induced without forcing sufficient descent in a penalty-type merit function. The proof relates to a prototypical algorithm, within which is allowed a range of specific algorithm choices associated with the Hessian matrix representation, updating the trust region radius, and feasibility restoration.
ISSN:1052-6234
1095-7189
DOI:10.1137/S105262340038081X