  We consider distributed computation of generalized Nash equilibrium (GNE)
over networks, in games with shared coupling constraints. Existing methods
require that each player has full access to opponents' decisions. In this
paper, we assume that players have only partial-decision information, and can
communicate with their neighbours over an arbitrary undirected graph. We recast
the problem as that of finding a zero of a sum of monotone operators through
primal-dual analysis. To distribute the problem, we doubly augment variables,
so that each player has local decision estimates and local copies of Lagrangian
multipliers. We introduce a single-layer algorithm, fully distributed with
respect to both primal and dual variables. We show its convergence to a
variational GNE with fixed step-sizes, by reformulating it as a
forward-backward iteration for a pair of doubly-augmented monotone operators.
