Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data
  Optimization   We propose a decomposition framework for the parallel optimization of the sum
of a differentiable {(possibly nonconvex)} function and a nonsmooth (possibly
nonseparable), convex one. The latter term is usually employed to enforce
structure in the solution, typically sparsity. The main contribution of this
work is a novel \emph{parallel, hybrid random/deterministic} decomposition
scheme wherein, at each iteration, a subset of (block) variables is updated at
the same time by minimizing local convex approximations of the original
nonconvex function. To tackle with huge-scale problems, the (block) variables
to be updated are chosen according to a \emph{mixed random and deterministic}
procedure, which captures the advantages of both pure deterministic and random
update-based schemes. Almost sure convergence of the proposed scheme is
established. Numerical results show that on huge-scale problems the proposed
hybrid random/deterministic algorithm outperforms both random and deterministic
schemes.

11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin