Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inexact-Proximal Accelerated Gradient Method for Stochastic Nonconvex
  Constrained Optimization Problems   Stochastic nonconvex optimization problems with nonlinear constraints have a
broad range of applications in intelligent transportation, cyber-security, and
smart grids. In this paper, first, we propose an inexact-proximal accelerated
gradient method to solve a nonconvex stochastic composite optimization problem
where the objective is the sum of smooth and nonsmooth functions, the
constraint functions are assumed to be deterministic and the solution to the
proximal map of the nonsmooth part is calculated inexactly at each iteration.
We demonstrate an asymptotic sublinear rate of convergence for stochastic
settings using increasing sample-size considering the error in the proximal
operator diminishes at an appropriate rate. Then we customize the proposed
method for solving stochastic nonconvex optimization problems with nonlinear
constraints and demonstrate a convergence rate guarantee. Numerical results
show the effectiveness of the proposed algorithm.

9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat