An interior-point trust-region algorithm for quadratic stochastic symmetric programming

Abstract

© 2017 by the Mathematical Association of Thailand. All rights reserved. Stochastic programming is a framework for modeling optimization problems that involve uncertainty. In this paper, we study two-stage stochastic quadratic symmetric programming to handle uncertainty in data defining (Deter-ministic) symmetric programs in which a quadratic function is minimized over the intersection of an affine set and a symmetric cone with finite event space. Twostage stochastic programs can be modeled as large deterministic programming and we present an interior point trust region algorithm to solve this problem. Numerical results on randomly generated data are available for stochastic symmetric programs. The complexity of our algorithm is proved.