Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Subgradient Methods and Quantization Effects   We consider a convex unconstrained optimization problem that arises in a
network of agents whose goal is to cooperatively optimize the sum of the
individual agent objective functions through local computations and
communications. For this problem, we use averaging algorithms to develop
distributed subgradient methods that can operate over a time-varying topology.
Our focus is on the convergence rate of these methods and the degradation in
performance when only quantized information is available. Based on our recent
results on the convergence time of distributed averaging algorithms, we derive
improved upper bounds on the convergence rate of the unquantized subgradient
method. We then propose a distributed subgradient method under the additional
constraint that agents can only store and communicate quantized information,
and we provide bounds on its convergence rate that highlight the dependence on
the number of quantization levels.

9
The categories of this paper are: nlin
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph