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In this paper, a robust sequential quadratic programming method of [1] forconstrained optimization is generalized to problem with stochastic objectivefunction, deterministic equality and inequality constraints. A stochastic linesearch scheme in [2] is employed to globalize the steps. We show that in thecase where the algorithm fails to terminate in finite number of iterations, thesequence of iterates will converge almost surely to a Karush-Kuhn-Tucker pointunder the assumption of extended Mangasarian-Fromowitz constraintqualification. We also show that, with a specific sampling method, theprobability of the penalty parameter approaching infinity is 0. Encouragingnumerical results are reported.

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