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A Non-monotone Conjugate Subgradient Type Method for Minimization of Convex Functions

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  • Igor Konnov

    (Kazan Federal University)

Abstract

We suggest a conjugate subgradient type method without any line search for minimization of convex non-differentiable functions. Unlike the custom methods of this class, it does not require monotone decrease in the goal function and reduces the implementation cost of each iteration essentially. At the same time, its step-size procedure takes into account behavior of the method along the iteration points. The preliminary results of computational experiments confirm the efficiency of the proposed modification.

Suggested Citation

  • Igor Konnov, 2020. "A Non-monotone Conjugate Subgradient Type Method for Minimization of Convex Functions," Journal of Optimization Theory and Applications, Springer, vol. 184(2), pages 534-546, February.
    • Handle: RePEc:spr:joptap:v:184:y:2020:i:2:d:10.1007_s10957-019-01589-6
    DOI: 10.1007/s10957-019-01589-6
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    References listed on IDEAS

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    1. Yu. Nesterov & V. Shikhman, 2015. "Quasi-monotone Subgradient Methods for Nonsmooth Convex Minimization," Journal of Optimization Theory and Applications, Springer, vol. 165(3), pages 917-940, June.
    2. NESTEROV, Yurii & SHIKHMAN, Vladimir, 2015. "Quasi-monotone subgradient methods for nonsmooth convex minimization," LIDAM Reprints CORE 2670, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

    1. Elena Tovbis & Vladimir Krutikov & Predrag Stanimirović & Vladimir Meshechkin & Aleksey Popov & Lev Kazakovtsev, 2023. "A Family of Multi-Step Subgradient Minimization Methods," Mathematics, MDPI, vol. 11(10), pages 1-24, May.

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