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Conference Papers Year : 2013

Optimization with First-Order Surrogate Functions

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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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Dates and versions

hal-00822229 , version 1 (14-05-2013)

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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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