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Gradient methods with memory

Author

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  • NESTEROV Yurii,

    (Université catholique de Louvain, CORE, Belgium)

  • FLOREA Mihai I.,

    (Université catholique de Louvain, CORE and INMA, Belgium)

Abstract

In this paper, we consider gradient methods for minimizing smooth convex functions, which employ the information obtained at the previous iterations in order to accelerate the convergence towards the optimal solution. This information is used in the form of piece-wise linear model of the objective function, which provides us with much better prediction abilities as compared with the standard linear model. To the best of our knowledge, this approach was never really applied in Convex Minimization to differentiable functions in view of the high complexity of the corresponding auxiliary problems. However, we show that all necessary computations can be done very efficiently. Consequently, we get new optimization methods, which are better than the usual Gradient Methods both in the number of calls of oracle and in the computational time. Our theoretical conclusions are confirmed by preliminary computational experiments.

Suggested Citation

  • NESTEROV Yurii, & FLOREA Mihai I.,, 2019. "Gradient methods with memory," LIDAM Discussion Papers CORE 2019022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:2019022
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    File URL: https://sites.uclouvain.be/core/publications/coredp/coredp2019.html
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    Keywords

    convex optimization; gradient methods; relative smoothness; rate of convergence piece-wise linear model;
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