Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Distributed Stochastic Gradient Tracking Method   In this paper, we study the problem of distributed multi-agent optimization
over a network, where each agent possesses a local cost function that is smooth
and strongly convex. The global objective is to find a common solution that
minimizes the average of all cost functions. Assuming agents only have access
to unbiased estimates of the gradients of their local cost functions, we
consider a distributed stochastic gradient tracking method. We show that, in
expectation, the iterates generated by each agent are attracted to a
neighborhood of the optimal solution, where they accumulate exponentially fast
(under a constant step size choice). More importantly, the limiting (expected)
error bounds on the distance of the iterates from the optimal solution decrease
with the network size, which is a comparable performance to a centralized
stochastic gradient algorithm. Numerical examples further demonstrate the
effectiveness of the method.

14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org