A Sampling Method to Chance-constrained Semidefinite Optimization

Chuan Xu (1) , Jianqiang Cheng (2) , Abdel Lisser (2)

1 ParSys - LRI - Systèmes parallèles (LRI)
2 LRI - Laboratoire de Recherche en Informatique

Semidefinite programming has been widely studied for the last two decades. Semidefinite programs are linear programs with semidefinite constraint generally studied with deterministic data. In this paper, we deal with a stochastic semidefinte programs with chance constraints, which is a generalization of chance-constrained linear programs. Based on existing theoretical results, we develop a new sampling method to solve these chance constraints semidefinite problems. Numerical experiments are conducted to compare our results with the state-of-the-art and to show the strength of the sampling method.

Mots clés: stochastic programming; chance-constrained programming; sample approximation; semidefinite program

Domaines: Complexité [cs.CC]; Recherche opérationnelle [math.OC]

Chuan Xu, Jianqiang Cheng, Abdel Lisser. A Sampling Method to Chance-constrained Semidefinite Optimization. Proceedings of the International Conference on Operations Research and Enterprise Systems, 2015, pp.75 - 81. ⟨10.5220/0005276400750081⟩. ⟨hal-01415119⟩