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Conference papers

Optimization with First-Order Surrogate Functions

Julien Mairal 1, *
* Corresponding author
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
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.
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Submitted on : Tuesday, May 14, 2013 - 12:28:06 PM
Last modification on : Thursday, January 20, 2022 - 5:28:04 PM
Long-term archiving on: : Thursday, August 15, 2013 - 4:15:18 AM


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



Julien Mairal. Optimization with First-Order Surrogate Functions. ICML 2013 - International Conference on Machine Learning, Jun 2013, Atlanta, United States. pp.783-791. ⟨hal-00822229⟩



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