Stochastic optimization problems attempt to model uncertainty in the data by assuming that (part of) the input is specified in terms of a probability distribution. We consider the well-studied paradigm of 2-stage models with recourse: Among the range of applications we consider are stochastic versions of the set cover, vertex cover, facility location, multicut (on trees), and multicommodity flow problems.
REF study stochastic versions of the set cover and facility location problems, and present an approximation scheme based on randomized LP rounding.
Stochastic optimization is (almost) as easy as deterministic optimization
45th Annual IEEE Symposium on Foundations of Computer Science
45th Annual IEEE Symposium on Foundations of Computer Science
Computer Science