Sciweavers

Share
CORR
2011
Springer

Convex and Network Flow Optimization for Structured Sparsity

3 years 2 months ago
Convex and Network Flow Optimization for Structured Sparsity
We consider a class of learning problems regularized by a structured sparsity-inducing norm defined as the sum of 2- or ∞-norms over groups of variables. Whereas much effort has been put in developing fast optimization techniques when the groups are disjoint or embedded in a hierarchy, we address here the case of general overlapping groups. To this end, we present two different strategies: On the one hand, we show that the proximal operator associated with a sum of ∞norms can be computed exactly in polynomial time by solving a quadratic min-cost flow problem, allowing the use of accelerated proximal gradient methods. On the other hand, we use proximal splitting techniques, and address an equivalent formulation with non-overlapping groups, but in higher dimension and with additional constraints. We propose efficient and scalable algorithms exploiting these two strategies, which are significantly faster than alternative approaches. We illustrate these methods with several proble...
Julien Mairal, Rodolphe Jenatton, Guillaume Obozin
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2011
Where CORR
Authors Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis Bach
Comments (0)
books