Stochastic iteration for a constrained optimization problem

A primal dual method of Kushner and Sanvicente for a constrained optimization problem with convex regression functions is investigated without a priori bounds. For the stochastic approximation sequence almost sure convergence to a random optimal solution and a random Kuhn-Tucker vector is shown, and for the uniqueness case, a functional central limit theorem is given.