Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Structured Sparsity via Alternating Direction Methods   We consider a class of sparse learning problems in high dimensional feature
space regularized by a structured sparsity-inducing norm which incorporates
prior knowledge of the group structure of the features. Such problems often
pose a considerable challenge to optimization algorithms due to the
non-smoothness and non-separability of the regularization term. In this paper,
we focus on two commonly adopted sparsity-inducing regularization terms, the
overlapping Group Lasso penalty $l_1/l_2$-norm and the $l_1/l_\infty$-norm. We
propose a unified framework based on the augmented Lagrangian method, under
which problems with both types of regularization and their variants can be
efficiently solved. As the core building-block of this framework, we develop
new algorithms using an alternating partial-linearization/splitting technique,
and we prove that the accelerated versions of these algorithms require
$O(\frac{1}{\sqrt{\epsilon}})$ iterations to obtain an $\epsilon$-optimal
solution. To demonstrate the efficiency and relevance of our algorithms, we
test them on a collection of data sets and apply them to two real-world
problems to compare the relative merits of the two norms.

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The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat