  This paper considers a networked aggregative game (NAG) where the players are
distributed over a communication network. By only communicating with a subset
of players, the goal of each player in the NAG is to minimize an individual
cost function that depends on its own action and the aggregate of all the
players' actions. To this end, we design a novel distributed algorithm that
jointly exploits the ideas of the consensus algorithm and the conditional
projection descent. Under strongly monotone assumption on the pseudo-gradient
mapping, the proposed algorithm with fixed step-sizes is proved to converge
linearly to the unique Nash equilibrium of the NAG. Then the theoretical
results are validated by numerical experiments.
