  In this paper, we consider the problem of finding a Nash equilibrium in a
multi-player game over generally connected networks. This model differs from a
conventional setting in that players have partial information on the actions of
their opponents and the communication graph is not necessarily the same as the
players' cost dependency graph. We develop a relatively fast algorithm within
the framework of inexact-ADMM, based on local information exchange between the
players. We prove its convergence to Nash equilibrium for fixed step-sizes and
analyze its convergence rate. Numerical simulations illustrate its benefits
when compared to a consensus-based gradient type algorithm with diminishing
step-sizes.
