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Maintaining the positive definiteness of the matrices in reduced secant methods for equality constrained optimization
Authors:Jean Charles Gilbert
Affiliation:(1) International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria;(2) Present address: Institut National de Recherche en Informatique et an Automatique (INRIA), Domaine de Voluceau, Rocquencourt, BP 105, F-78153 Le Chesnay, France
Abstract:We propose an algorithm for minimizing a functionf on Ropfn in the presence ofm equality constraintsc that locally is a reduced secant method. The local method is globalized using a nondifferentiable augmented Lagrangian whose decrease is obtained by both a longitudinal search that decreases mainlyf and a transversal search that decreases mainly parcpar. Our main objective is to show that the longitudinal path can be designed to maintain the positive definiteness of the reduced matrices by means of the positivity ofgammakTdeltak, wheregammak is the change in the reduced gradient and deltak is the reduced longitudinal displacement.Work supported by the FNRS (Fonds National de la Recherche Scientifique) of Belgium.
Keywords:Augmented Lagrangian  constrained optimization  exact penalty function  global convergence  optimization algorithm  reduced secant method  superlinear convergence  Wolfe's step-size selection
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