Smoothing Techniques and Augmented Lagrangian Method for Recourse Problem of Two-Stage Stochastic Linear Programming

Abstract EN

The augmented Lagrangian method can be used for solving recourse problems and obtaining their normal solution in solving two-stage stochastic linear programming problems.

The augmented Lagrangian objective function of a stochastic linear problem is not twice differentiable which precludes the use of a Newton method.

In this paper, we apply the smoothing techniques and a fast Newton-Armijo algorithm for solving an unconstrained smooth reformulation of this problem.

Computational results and comparisons are given to show the effectiveness and speed of the algorithm.