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A subgradient method with non-monotone line search

Author

Listed:
  • O. P. Ferreira

    (Universidade Federal de Goiás)

  • G. N. Grapiglia

    (Université Catholique de Louvain)

  • E. M. Santos

    (Instituto Federal de Educação, Ciência e Tecnologia do Maranhão)

  • J. C. O. Souza

    (Aix-Marseille University
    Federal University of Piauí)

Abstract

In this paper we present a subgradient method with non-monotone line search for the minimization of convex functions with simple convex constraints. Different from the standard subgradient method with prefixed step sizes, the new method selects the step sizes in an adaptive way. Under mild conditions asymptotic convergence results and iteration-complexity bounds are obtained. Preliminary numerical results illustrate the relative efficiency of the proposed method.

Suggested Citation

  • O. P. Ferreira & G. N. Grapiglia & E. M. Santos & J. C. O. Souza, 2023. "A subgradient method with non-monotone line search," Computational Optimization and Applications, Springer, vol. 84(2), pages 397-420, March.
    • Handle: RePEc:spr:coopap:v:84:y:2023:i:2:d:10.1007_s10589-022-00438-z
    DOI: 10.1007/s10589-022-00438-z
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    References listed on IDEAS

    as
    1. Geovani N. Grapiglia & Ekkehard W. Sachs, 2017. "On the worst-case evaluation complexity of non-monotone line search algorithms," Computational Optimization and Applications, Springer, vol. 68(3), pages 555-577, December.
    2. NESTEROV, Yurii, 2014. "Subgradient methods for huge-scale optimization problems," LIDAM Reprints CORE 2593, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    Full references (including those not matched with items on IDEAS)

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