Speaker: Alexander Shapiro, Georgia Institute of Technology
Computational complexity of stochastic programs
Abstract: The traditional approach to solving stochastic programming problems involves discretization of the underlying probability distributions. However, the number of required discretization points (called scenarios) grows exponentially both with increase of the number of random parameters and number of stages. In order to deal with this exponential explosion, randomization approaches based on Monte Carlo sampling techniques were developed. In this talk we discuss computational complexity of some of such methods from theoretical and practical points of view.Time: Thursday, April 4, 2019, 10:50-11:50 a.m.
Department of Mathematical Sciences
George Mason University
4400 University Drive, MS 3F2
Fairfax, VA 22030-4444
Tel. 703-993-1460, Fax. 703-993-1491