A limited memory algorithm for inequality constrained minimization

Paul Armand (armandunilim.fr)
Philippe Segalat (segalatunilim.fr)

Abstract: A method for solving inequality constrained minimization problems is described. The algorithm is based on a primal-dual interior point approach, with a line search globalization strategy. A quasi-Newton technique (BFGS) with limited memory storage is used to approximate the second derivatives of the functions. The method is especially intended for solving problems with a large number of variables with bound constraints and a medium number of general inequality constraints. Some numerical experiments are presented to validate our approach.

Keywords: constrained optimization, interior point method, large scale optimization, limited memory method, quasi-Newton method

Category 1: Nonlinear Optimization

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: Rapport de recherche du LACO 2003-08, LACO, Universite de Limoges, Faculte des Sciences et Techniques, 123, avenue Albert Thomas, 87060 Limoges (FRANCE)

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Entry Submitted: 10/07/2003
Entry Accepted: 10/07/2003
Entry Last Modified: 10/07/2003

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