Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Generic Acceleration Framework for Stochastic Composite Optimization   In this paper, we introduce various mechanisms to obtain accelerated
first-order stochastic optimization algorithms when the objective function is
convex or strongly convex. Specifically, we extend the Catalyst approach
originally designed for deterministic objectives to the stochastic setting.
Given an optimization method with mild convergence guarantees for strongly
convex problems, the challenge is to accelerate convergence to a
noise-dominated region, and then achieve convergence with an optimal worst-case
complexity depending on the noise variance of the gradients. A side
contribution of our work is also a generic analysis that can handle inexact
proximal operators, providing new insights about the robustness of stochastic
algorithms when the proximal operator cannot be exactly computed.

23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc