An Interior-Point Trust-Region Algorithm for Quadratic Stochastic Symmetric Programming

Phannipa Kabcome, Thanasak Mouktonglang

Abstract

Stochastic programming is a framework for modeling optimizationproblems that involve uncertainty. In this paper, we study two-stage stochasticquadratic symmetric programming to handle uncertainty in data dening (Deter-ministic) symmetric programs in which a quadratic function is minimized over theintersection of an ane set and a symmetric cone with nite event space. Two-stage stochastic programs can be modeled as large deterministic programming andwe present an interior point trust region algorithm to solve this problem. Numer-ical results on randomly generated data are available for stochastic symmetricprograms. The complexity of our algorithm is proved.