  In this paper, a robust sequential quadratic programming method of [1] for
constrained optimization is generalized to problem with stochastic objective
function, deterministic equality and inequality constraints. A stochastic line
search scheme in [2] is employed to globalize the steps. We show that in the
case where the algorithm fails to terminate in finite number of iterations, the
sequence of iterates will converge almost surely to a Karush-Kuhn-Tucker point
under the assumption of extended Mangasarian-Fromowitz constraint
qualification. We also show that, with a specific sampling method, the
probability of the penalty parameter approaching infinity is 0. Encouraging
numerical results are reported.
