Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal Stochastic Strongly Convex Optimization with a Logarithmic
  Number of Projections   We consider stochastic strongly convex optimization with a complex inequality
constraint. This complex inequality constraint may lead to computationally
expensive projections in algorithmic iterations of the stochastic gradient
descent~(SGD) methods. To reduce the computation costs pertaining to the
projections, we propose an Epoch-Projection Stochastic Gradient
Descent~(Epro-SGD) method. The proposed Epro-SGD method consists of a sequence
of epochs; it applies SGD to an augmented objective function at each iteration
within the epoch, and then performs a projection at the end of each epoch.
Given a strongly convex optimization and for a total number of $T$ iterations,
Epro-SGD requires only $\log(T)$ projections, and meanwhile attains an optimal
convergence rate of $O(1/T)$, both in expectation and with a high probability.
To exploit the structure of the optimization problem, we propose a proximal
variant of Epro-SGD, namely Epro-ORDA, based on the optimal regularized dual
averaging method. We apply the proposed methods on real-world applications; the
empirical results demonstrate the effectiveness of our methods.

15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
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The categories of this paper are: econ
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The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas