Title of the course: Stochastic Optimization
Instructor: Dr. Tatiana González Grandón
Institution: Humboldt Universität Berlin
Dates: 6-12 August 2018
Prerequisites: Linear Optimization
Level: Graduate, advanced undergraduate
Abstract: Stochastic optimization refers to a collection of methods for minimizing/maximizing an objective function when randomness is present. We will study the types of problems when uncertainty is present in the constraint functions. Over the last few decades these methods have become essential tools for economics, engineering, business and computer science. We will look at applications and essential theory to solve this type of problems.
1. Optimization with random variables Introduction to Optimization Problems with uncertainties and to Probability Tools.
2. Probability Constraints Examples and Solutions.
3. Continuity & Differentiability of Probability Constraints
4. When do we have convexity? Prékopa's Theorem
5. Numerical Solutions and Discussion of Optimization Examples.
Textbook or/and course webpage:
1. Stochastic Programming. Andras Prékopa.
2. Lectures on Stochastic Programming (Modeling and Theory). A. Shapiro, D. Dentcheva.
Language: EN