Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A parameterized proximal point algorithm for separable convex
  optimization   In this paper, we develop a parameterized proximal point algorithm (P-PPA)
for solving a class of separable convex programming problems subject to linear
and convex constraints. The proposed algorithm is provable to be globally
convergent with a worst-case O(1/t) convergence rate, wheret denotes the
iteration number. By properly choosing the algorithm parameters, numerical
experiments on solving a sparse optimization problem arising from statistical
learning show that our P-PPA could perform significantly better than other
state-of-the-art methods, such as the alternating direction method of
multipliers and the relaxed proximal point algorithm.

22
The categories of this paper are: chao-dyn
1
The categories of this paper are: cs
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex