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Nesterov Step Reduced Gradient Algorithm for Convex Programming Problems

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Big Data and Networks Technologies (BDNT 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 81))

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Abstract

In this paper, we proposed an implementation of method of speed reduced gradient algorithm for optimizing a convex differentiable function subject to linear equality constraints and nonnegativity bounds on the variables. In particular, at each iteration, we compute a search direction by reduced gradient, and line search by bisection algorithm or Armijo rule. Under some assumption, the convergence rate of speed reduced gradient (SRG) algorithm is proven to be significantly better, both theoretically and practically. The algorithm of SRG are programmed by Matlab, and comparing by Frank-Wolfe algorithm some problems, the numerical results which show the efficient of our approach, we give also an application to ODE, optimal control, image and video co-localization and learning machine.

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Author information

Authors and Affiliations

  1. Faculty of Polydisciplinary Ouarzazate (FPO), Ibn Zohr University, B.P. 284, 45800, Ouarzazate, Morocco

    Abdelkrim El Mouatasim

  2. Faculty of Sciences and Techniques, Department of Computer Science, Moulay Ismail University, IDMS Team, Errachidia, Morocco

    Yousef Farhaoui

Authors
  1. Abdelkrim El Mouatasim
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  2. Yousef Farhaoui
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Correspondence to Abdelkrim El Mouatasim .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University, Errachidia, Morocco

    Yousef Farhaoui

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El Mouatasim, A., Farhaoui, Y. (2020). Nesterov Step Reduced Gradient Algorithm for Convex Programming Problems. In: Farhaoui, Y. (eds) Big Data and Networks Technologies. BDNT 2019. Lecture Notes in Networks and Systems, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-030-23672-4_11

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