Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Second-order orthant-based methods with enriched Hessian information for
  sparse $\ell_1$-optimization   We present a second order algorithm, based on orthantwise directions, for
solving optimization problems involving the sparsity enhancing $\ell_1$-norm.
The main idea of our method consists in modifying the descent orthantwise
directions by using second order information both of the regular term and (in
weak sense) of the $\ell_1$-norm. The weak second order information behind the
$\ell_1$-term is incorporated via a partial Huber regularization. One of the
main features of our algorithm consists in a faster identification of the
active set. We also prove that a reduced version of our method is equivalent to
a semismooth Newton algorithm applied to the optimality condition, under a
specific choice of the algorithm parameters. We present several computational
experiments to show the efficiency of our approach compared to other
state-of-the-art algorithms.

26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas