Optimality properties of an Augmented Lagrangian method on infeasible problems

E. Birgin (), J. Martínez () and L. Prudente ()

Computational Optimization and Applications, 2015, vol. 60, issue 3, 609-631

Abstract: Sometimes, the feasible set of an optimization problem that one aims to solve using a Nonlinear Programming algorithm is empty. In this case, two characteristics of the algorithm are desirable. On the one hand, the algorithm should converge to a minimizer of some infeasibility measure. On the other hand, one may wish to find a point with minimal infeasibility for which some optimality condition, with respect to the objective function, holds. Ideally, the algorithm should converge to a minimizer of the objective function subject to minimal infeasibility. In this paper the behavior of an Augmented Lagrangian algorithm with respect to those properties will be studied. Copyright Springer Science+Business Media New York 2015

Keywords: Nonlinear programming; Infeasible domains; Augmented Lagrangians; Algorithms; Numerical experiments (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s10589-014-9685-5

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