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Numerical Algorithms

, Volume 60, Issue 2, pp 263–277 | Cite as

Handling infeasibility in a large-scale nonlinear optimization algorithm

  • Jose Mario Martínez
  • Leandro da Fonseca Prudente
Original Paper

Abstract

Practical Nonlinear Programming algorithms may converge to infeasible points. It is sensible to detect this situation as quickly as possible, in order to have time to change initial approximations and parameters, with the aim of obtaining convergence to acceptable solutions in further runs. In this paper, a recently introduced Augmented Lagrangian algorithm is modified in such a way that the probability of quick detection of asymptotic infeasibility is enhanced. The modified algorithm preserves the property of convergence to stationary points of the sum of squares of infeasibilities without harming the convergence to KKT points in feasible cases.

Keywords

Augmented lagrangians Nonlinear programming Algorithms Numerical experiments 

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Jose Mario Martínez
    • 1
  • Leandro da Fonseca Prudente
    • 1
  1. 1.Department of Applied Mathematics, IMECC-UNICAMPUniversity of CampinasCampinasBrazil

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