Majorization-minimization procedures and convergence of SQP methods for semi-algebraic and tame programs

Bolte, Jérôme and Pauwels, Edouard (2016) Majorization-minimization procedures and convergence of SQP methods for semi-algebraic and tame programs. Mathematics of Operations Research, vol. 41 (n° 2). pp. 442-465.

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Official URL: http://tse-fr.eu/pub/29012

Abstract

In view of solving nonsmooth and nonconvex problems involving complex constraints (like standard NLP problems), we study general maximization-minimization procedures produced by families of strongly convex sub-problems. Using techniques from semi-algebraic geometry and variational analysis -in particular Lojasiewicz inequality- we establish the convergence of sequences generated by this type of schemes to critical points. The broad applicability of this process is illustrated in the context of NLP. In that case critical points coincide with KKT points. When the data are semi-algebraic or real analytic our method applies (for instance) to the study of various SQP methods: the moving balls method, Sl1QP, ESQP. Under standard qualification conditions, this provides -to the best of our knowledge- the first general convergence results for general nonlinear programming problems. We emphasize the fact that, unlike most works on this subject, no second-order assumption and/or convexity assumptions whatsoever are made. Rate of convergence are shown to be of the same form as those commonly encountered with first order methods.

Item Type: Article
Language: English
Date: 8 January 2016
Refereed: Yes
Uncontrolled Keywords: SQP methods, majorization-minimization, KKT points, KL inequality, nonlinear programming, converging methods, tame optimization
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 16 Mar 2015 14:56
Last Modified: 20 Mar 2019 15:37
OAI ID: oai:tse-fr.eu:29012
URI: http://publications.ut-capitole.fr/id/eprint/16700

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