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Large-Scale Active-Set Box-Constrained Optimization Method with Spectral Projected Gradients

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Abstract

A new active-set method for smooth box-constrained minimization is introduced. The algorithm combines an unconstrained method, including a new line-search which aims to add many constraints to the working set at a single iteration, with a recently introduced technique (spectral projected gradient) for dropping constraints from the working set. Global convergence is proved. A computer implementation is fully described and a numerical comparison assesses the reliability of the new algorithm.

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Authors and Affiliations

  1. Department of Computer Science IME-USP, University of São Paulo, Rua do Matão 1010, Cidade Universitária, 05508-900, São Paulo SP, Brazil

    Ernesto G. Birgin

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

    José Mario Martínez

Authors
  1. Ernesto G. Birgin
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  2. José Mario Martínez
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Birgin, E.G., Mario Martínez, J. Large-Scale Active-Set Box-Constrained Optimization Method with Spectral Projected Gradients. Computational Optimization and Applications 23, 101–125 (2002). https://doi.org/10.1023/A:1019928808826

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