Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Linear Convergence Rate of the Distributed Block Proximal Method   The recently developed Distributed Block Proximal Method, for solving
stochastic big-data convex optimization problems, is studied in this paper
under the assumption of constant stepsizes and strongly convex (possibly
non-smooth) local objective functions. This class of problems arises in many
learning and classification problems in which, for example, strongly-convex
regularizing functions are included in the objective function, the decision
variable is extremely high dimensional, and large datasets are employed. The
algorithm produces local estimates by means of block-wise updates and
communication among the agents. The expected distance from the (global)
optimum, in terms of cost value, is shown to decay linearly to a constant value
which is proportional to the selected local stepsizes. A numerical example
involving a classification problem corroborates the theoretical results.

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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
15
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: stat
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The categories of this paper are: astro-ph
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The categories of this paper are: cs
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga