Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improved Convergence Rate for a Distributed Two-Time-Scale Gradient
  Method under Random Quantization   We study the so-called distributed two-time-scale gradient method for solving
convex optimization problems over a network of agents when the communication
bandwidth between the nodes is limited, and so information that is exchanged
between the nodes must be quantized. Our main contribution is to provide a
novel analysis, resulting to an improved convergence rate of this method as
compared to the existing works. In particular, we show that the method
converges at a rate $O(log_2 k/\sqrt k)$ to the optimal solution, when the
underlying objective function is strongly convex and smooth. The key technique
in our analysis is to consider a Lyapunov function that simultaneously captures
the coupling of the consensus and optimality errors generated by the method.

25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: q-fin
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The categories of this paper are: econ
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th