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Convex optimization with sparsity-inducing norms
Francis Bach,Rodolphe Jenatton,Julien Mairal,Guillaume Obozinski +3 more
- pp 19-49
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The article was published on 2011-01-01 and is currently open access. It has received 372 citations till now. The article focuses on the topics: Conic optimization & Proper convex function.read more
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Optimization with Sparsity-Inducing Penalties
TL;DR: In this article, the authors present from a general perspective optimization tools and techniques dedicated to such sparsityinducing penalties, including proximal methods, block-coordinate descent, reweighted $\ell_2$-penalized techniques, working-set and homotopy methods, as well as non-convex formulations and extensions.
Book
Optimization with Sparsity-Inducing Penalties
TL;DR: This monograph covers proximal methods, block-coordinate descent, reweighted l2-penalized techniques, working-set and homotopy methods, as well as non-convex formulations and extensions, and provides an extensive set of experiments to compare various algorithms from a computational point of view.
Proceedings Article
Decimeter-level localization with a single WiFi access point
TL;DR: Chronos, a system that enables a single WiFi access point to localize clients to within tens of centimeters, demonstrates that Chronos's accuracy is comparable to state-of-the-art localization systems, which use four or five access points.
Proceedings Article
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
TL;DR: This work shows that both the basic proximal-gradient method and the accelerated proximal - gradient method achieve the same convergence rate as in the error-free case, provided that the errors decrease at appropriate rates.
Posted Content
Structured Variable Selection with Sparsity-Inducing Norms
TL;DR: In this paper, the authors consider the empirical risk minimization problem for linear supervised learning, with regularization by structured sparsityinducing norms, defined as sums of Euclidean norms on certain subsets of variables.
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