Abstract (click to view)
In this study we examine two stages stochastic quadratic programming problems, where the second stage itself is a quadratic programming problem with linear constraints with uncertain right-hand sides. We develop the active-set strategy obtain a estimation for the second stage. This approximation is used to design a computationally gradient solution algorithm to solve stochastic quadratic programs. We will present the convergence and numerical analysis of the algorithm.
Program: PhD in Operations Management
Faculty mentor: Harsha Gangammanavar