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Random gradient-free minimization of convex functions

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  • Yurii NESTEROV
  • Vladimir SPOKOINY

Abstract

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Suggested Citation

  • Yurii NESTEROV & Vladimir SPOKOINY, 2017. "Random gradient-free minimization of convex functions," LIDAM Reprints CORE 2851, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:2851
    Note: In : Foundations of Computational Mathematics, 17, 527-566, 2017
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    Citations

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    Cited by:

    1. Stefan Wager & Kuang Xu, 2021. "Experimenting in Equilibrium," Management Science, INFORMS, vol. 67(11), pages 6694-6715, November.
    2. Marco Boresta & Tommaso Colombo & Alberto Santis & Stefano Lucidi, 2022. "A Mixed Finite Differences Scheme for Gradient Approximation," Journal of Optimization Theory and Applications, Springer, vol. 194(1), pages 1-24, July.
    3. Yijie Peng & Li Xiao & Bernd Heidergott & L. Jeff Hong & Henry Lam, 2022. "A New Likelihood Ratio Method for Training Artificial Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 638-655, January.
    4. Jun Xie & Chi Cao, 2017. "Non-Convex Economic Dispatch of a Virtual Power Plant via a Distributed Randomized Gradient-Free Algorithm," Energies, MDPI, vol. 10(7), pages 1-12, July.
    5. Ghadimi, Saeed & Powell, Warren B., 2024. "Stochastic search for a parametric cost function approximation: Energy storage with rolling forecasts," European Journal of Operational Research, Elsevier, vol. 312(2), pages 641-652.
    6. Katya Scheinberg, 2022. "Finite Difference Gradient Approximation: To Randomize or Not?," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2384-2388, September.
    7. David Kozak & Stephen Becker & Alireza Doostan & Luis Tenorio, 2021. "A stochastic subspace approach to gradient-free optimization in high dimensions," Computational Optimization and Applications, Springer, vol. 79(2), pages 339-368, June.
    8. Ghaderi, Susan & Ahookhosh, Masoud & Arany, Adam & Skupin, Alexander & Patrinos, Panagiotis & Moreau, Yves, 2024. "Smoothing unadjusted Langevin algorithms for nonsmooth composite potential functions," Applied Mathematics and Computation, Elsevier, vol. 464(C).
    9. Dvurechensky, Pavel & Gorbunov, Eduard & Gasnikov, Alexander, 2021. "An accelerated directional derivative method for smooth stochastic convex optimization," European Journal of Operational Research, Elsevier, vol. 290(2), pages 601-621.
    10. Jun Xie & Qingyun Yu & Chi Cao, 2018. "A Distributed Randomized Gradient-Free Algorithm for the Non-Convex Economic Dispatch Problem," Energies, MDPI, vol. 11(1), pages 1-15, January.
    11. Nikita Kornilov & Alexander Gasnikov & Pavel Dvurechensky & Darina Dvinskikh, 2023. "Gradient-free methods for non-smooth convex stochastic optimization with heavy-tailed noise on convex compact," Computational Management Science, Springer, vol. 20(1), pages 1-43, December.
    12. Jean-Jacques Forneron, 2023. "Noisy, Non-Smooth, Non-Convex Estimation of Moment Condition Models," Papers 2301.07196, arXiv.org, revised Feb 2023.
    13. V. Kungurtsev & F. Rinaldi, 2021. "A zeroth order method for stochastic weakly convex optimization," Computational Optimization and Applications, Springer, vol. 80(3), pages 731-753, December.
    14. Geovani Nunes Grapiglia, 2023. "Quadratic regularization methods with finite-difference gradient approximations," Computational Optimization and Applications, Springer, vol. 85(3), pages 683-703, July.
    15. Michael R. Metel & Akiko Takeda, 2022. "Perturbed Iterate SGD for Lipschitz Continuous Loss Functions," Journal of Optimization Theory and Applications, Springer, vol. 195(2), pages 504-547, November.
    16. Jingxu Xu & Zeyu Zheng, 2023. "Gradient-Based Simulation Optimization Algorithms via Multi-Resolution System Approximations," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 633-651, May.

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