A Sequential Quadratic Programming Method for Optimization with Stochastic Objective Functions, Deterministic Inequality Constraints and Robust Subproblems

In this paper, a robust sequential quadratic programming method for constrained optimization is generalized to problem with an {expectation} objective function {and} deterministic equality and inequality constraints. A stochastic line search scheme is employed to globalize the steps. {We show theoretically that sequences generated by the algorithm converge almost surely to a Karush-Kuhn-Tucker point under the assumption of the extended Mangasarian-Fromovitz constraint qualification}. Encouraging numerical results are reported.