The Computational Complexity of Stochastic Optimization

摘要

This paper presents an investigation on the computational complexity of stochastic optimization problems. We discuss a scenario-based model which captures the important classes of two-stage stochastic combinatorial optimization, two-stage stochastic linear programming, and two-stage stochastic integer linear programming. This model can also be used to handle chance constraints, which are used in many stochastic optimization problems. We derive general upper bounds for the complexity of computational problems related to this model, which hold under very mild conditions. Additionally, we show that these upper bounds are matched for some stochastic combinatorial optimization problems arising in the field of transportation and logistics.