Stochastic Optimization aims at optimal decisions for problems with uncertain data. It is applicable in many areas like e.g. industrial engineering, operations research or finance. Students will learn to model stochastic decision problems, important mathematical properties of stochastic optimization problems and numerical methods for solution.
Two stage and multistage recourse problems
Nonanticipativity, value of information and value of the stochastic solution
probabilistic constraints
risk averse optimization
L-shaped method and further numerical approaches
Approximation and sampling methods
