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