Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Universally Optimal Multistage Accelerated Stochastic Gradient Method   We study the problem of minimizing a strongly convex, smooth function when we
have noisy estimates of its gradient. We propose a novel multistage accelerated
algorithm that is universally optimal in the sense that it achieves the optimal
rate both in the deterministic and stochastic case and operates without
knowledge of noise characteristics. The algorithm consists of stages that use a
stochastic version of Nesterov's method with a specific restart and parameters
selected to achieve the fastest reduction in the bias-variance terms in the
convergence rate bounds.

3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph