Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Barzilai-Borwein Step Size for Stochastic Gradient Descent   One of the major issues in stochastic gradient descent (SGD) methods is how
to choose an appropriate step size while running the algorithm. Since the
traditional line search technique does not apply for stochastic optimization
algorithms, the common practice in SGD is either to use a diminishing step
size, or to tune a fixed step size by hand, which can be time consuming in
practice. In this paper, we propose to use the Barzilai-Borwein (BB) method to
automatically compute step sizes for SGD and its variant: stochastic variance
reduced gradient (SVRG) method, which leads to two algorithms: SGD-BB and
SVRG-BB. We prove that SVRG-BB converges linearly for strongly convex objective
functions. As a by-product, we prove the linear convergence result of SVRG with
Option I proposed in [10], whose convergence result is missing in the
literature. Numerical experiments on standard data sets show that the
performance of SGD-BB and SVRG-BB is comparable to and sometimes even better
than SGD and SVRG with best-tuned step sizes, and is superior to some advanced
SGD variants.

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The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
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The categories of this paper are: physics
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The categories of this paper are: nlin
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The categories of this paper are: cond-mat