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Handling infeasibility in a large-scale nonlinear optimization algorithm

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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.

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Authors and Affiliations

  1. Department of Applied Mathematics, IMECC-UNICAMP, University of Campinas, CP 6065, 13081-970, Campinas, SP, Brazil

    Jose Mario Martínez & Leandro da Fonseca Prudente

Authors
  1. Jose Mario Martínez
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  2. Leandro da Fonseca Prudente
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Correspondence to Jose Mario Martínez.

Additional information

This author was supported by PRONEX-Optimization (PRONEX—CNPq/FAPERJ E-26/171.164/2003—APQ1), FAPESP (Grant 06/53768-0) and CNPq.

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Martínez, J.M., Prudente, L.d.F. Handling infeasibility in a large-scale nonlinear optimization algorithm. Numer Algor 60, 263–277 (2012). https://doi.org/10.1007/s11075-012-9561-2

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  • DOI: https://doi.org/10.1007/s11075-012-9561-2

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