Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Geometric Convergence of Distributed Gradient Play in Games with
  Unconstrained Action Sets   We provide a distributed algorithm to learn a Nash equilibrium in a class of
non-cooperative games with strongly monotone mappings and unconstrained action
sets. Each player has access to her own smooth local cost function and can
communicate to her neighbors in some undirected graph. We consider a
distributed communication-based gradient algorithm. For this procedure, we
prove geometric convergence to a Nash equilibrium. In contrast to our previous
works [15], [16], where the proposed algorithms required two parameters to be
set up and the analysis was based on a so called augmented game mapping, the
procedure in this work corresponds to a standard distributed gradient play and,
thus, only one constant step size parameter needs to be chosen appropriately to
guarantee fast convergence to a game solution. Moreover, we provide a rigorous
comparison between the convergence rate of the proposed distributed gradient
play and the rate of the GRANE algorithm presented in [15]. It allows us to
demonstrate that the distributed gradient play outperforms the GRANE in terms
of convergence speed.

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The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: cs
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: q-bio