A Quasi-Monte-Carlo-Based Feasible Sequential System of Linear Equations Method for Stochastic Programs with Recourse

Abstract EN

A two-stage stochastic quadratic programming problem with inequality constraints is considered.

By quasi-Monte-Carlo-based approximations of the objective function and its first derivative, a feasible sequential system of linear equations method is proposed.

A new technique to update the active constraint set is suggested.

We show that the sequence generated by the proposed algorithm converges globally to a Karush-Kuhn-Tucker (KKT) point of the problem.

In particular, the convergence rate is locally superlinear under some additional conditions.