TY - JOUR
T1 - On the global convergence of a filter-SQP algorithm
AU - Fletcher, Roger
AU - Leyffer, Sven
AU - Toint, Philippe
N1 - Copyright 2004 Elsevier Science B.V., Amsterdam. All rights reserved.
PY - 2003/1/1
Y1 - 2003/1/1
N2 - A mechanism for proving global convergence in SQP-filter methods for nonlinear programming (NLP) is described. Such methods are characterized by their use of the dominance 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.
AB - A mechanism for proving global convergence in SQP-filter methods for nonlinear programming (NLP) is described. Such methods are characterized by their use of the dominance 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.
UR - http://www.scopus.com/inward/record.url?scp=0037288670&partnerID=8YFLogxK
U2 - 10.1137/S105262340038081X
DO - 10.1137/S105262340038081X
M3 - Article
AN - SCOPUS:0037288670
SN - 1052-6234
VL - 13
SP - 44
EP - 59
JO - SIAM Journal on Optimization
JF - SIAM Journal on Optimization
IS - 1
ER -