Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single
  Algorithm   The theory and practice of stochastic optimization has focused on stochastic
gradient descent (SGD) in recent years, retaining the basic first-order
stochastic nature of SGD while aiming to improve it via mechanisms such as
averaging, momentum, and variance reduction. Improvement can be measured along
various dimensions, however, and it has proved difficult to achieve
improvements both in terms of nonasymptotic measures of convergence rate and
asymptotic measures of distributional tightness. In this work, we consider
first-order stochastic optimization from a general statistical point of view,
motivating a specific form of recursive averaging of past stochastic gradients.
The resulting algorithm, which we refer to as \emph{Recursive One-Over-T SGD}
(ROOT-SGD), matches the state-of-the-art convergence rate among online
variance-reduced stochastic approximation methods. Moreover, under slightly
stronger distributional assumptions, the rescaled last-iterate of ROOT-SGD
converges to a zero-mean Gaussian distribution that achieves near-optimal
covariance.

31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: bayes-an
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
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The categories of this paper are: econ
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The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-th