Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Can speed up the convergence rate of stochastic gradient methods to
  $\mathcal{O}(1/k^2)$ by a gradient averaging strategy?   In this paper we consider the question of whether it is possible to apply a
gradient averaging strategy to improve on the sublinear convergence rates
without any increase in storage. Our analysis reveals that a positive answer
requires an appropriate averaging strategy and iterations that satisfy the
variance dominant condition. As an interesting fact, we show that if the
iterative variance we defined is always dominant even a little bit in the
stochastic gradient iterations, the proposed gradient averaging strategy can
increase the convergence rate $\mathcal{O}(1/k)$ to $\mathcal{O}(1/k^2)$ in
probability for the strongly convex objectives with Lipschitz gradients. This
conclusion suggests how we should control the stochastic gradient iterations to
improve the rate of convergence.

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The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
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The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin