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Distributed Nash equilibrium learning: A second-order proximal algorithm
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DOI：10.1002/rnc.5618.png)
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Abstract
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This article addresses the distributed Nash equilibrium (NE) seeking problem for multiagent networked games with partial decision information. We employ a quadratically approximated alternating direction method of multipliers together with an augmented consensus procedure to compute the NE of games with twice differentiable cost functions. The resulting second-order proximal algorithm enjoys relatively fast convergence rate and less burden on step size selection compared with the existing works. Numerical simulations are consistent with our theoretical analysis.
Keywords:
ADMM
multiagent system
Nash equilibrium
networked games
second-order proximal algorithm
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