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Abstract
A mechanism for proving gobal convergence in filtertype methods for nonlinear
programming is described. Such methods are characterized by their use of the
dominance concept of multiobjective optimization, instead of a penalty
paremeter 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 penaltytype 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.
Original language  English 

Place of Publication  Namur 
Publisher  FUNDP, Faculté des Sciences. Département de Mathématique. 
Publication status  Published  2000 
Publication series
Name  Technical report 

Publisher  Department of MAthematics, University of Namur 
Volume  05 
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Filter methods in nonlinear unconstrained or boundconstrained optimization
1/09/02 → 31/08/07
Project: PHD