  In this paper we present optimization problems with biconvex objective
function and linear constraints such that the set of global minima of the
optimization problems is the same as the set of Nash equilibria of a n-player
general-sum normal form game. We further show that the objective function is an
invex function and consider a projected gradient descent algorithm. We prove
that the projected gradient descent scheme converges to a partial optimum of
the objective function. We also present simulation results on certain test
cases showing convergence to a Nash equilibrium strategy.
