An inexact lagrange-newton method for stochastic quadratic programs with recourse

Abstract

In this paper, two-stage stochastic quadratic programming problems with equality constraints are considered. By Monte Carlo simulation-based approximations of the objective function and its first (second) derivative, an inexact Lagrange-Newton type method is proposed. It is showed that this method is globally convergent with probability one. In particular, the convergence is local superlinear under an integral approximation error bound condition. Moreover, this method can be easily extended to solve stochastic quadratic programming problems with inequality constraints.