Optimization with First-Order Surrogate Functions

Julien Mairal 1, *
* Auteur correspondant
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : In this paper, we study optimization methods consisting of iteratively minimizing surrogates of an objective function. By proposing several algorithmic variants and simple convergence analyses, we make two main contributions. First, we provide a unified viewpoint for several first-order optimization techniques such as accelerated proximal gradient, block coordinate descent, or Frank-Wolfe algorithms. Second, we introduce a new incremental scheme that experimentally matches or outperforms state-of-the-art solvers for large-scale optimization problems typically arising in machine learning.
Type de document :
Communication dans un congrès
ICML 2013 - International Conference on Machine Learning, Jun 2013, Atlanta, United States. 28, pp.783-791, 2013, JMLR Proceedings. 〈http://jmlr.org/proceedings/papers/v28/mairal13.html〉
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Contributeur : Julien Mairal <>
Soumis le : mardi 14 mai 2013 - 12:28:06
Dernière modification le : mardi 26 août 2014 - 09:45:34
Document(s) archivé(s) le : jeudi 15 août 2013 - 04:15:18

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  • HAL Id : hal-00822229, version 1
  • ARXIV : 1305.3120

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Julien Mairal. Optimization with First-Order Surrogate Functions. ICML 2013 - International Conference on Machine Learning, Jun 2013, Atlanta, United States. 28, pp.783-791, 2013, JMLR Proceedings. 〈http://jmlr.org/proceedings/papers/v28/mairal13.html〉. 〈hal-00822229〉

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