Stochastic Programming: Formulations, Algorithms, and Applications.

Summary:  This short course is targeted towards graduate students and practitioners interested in learning how to formulate, analyze, and solve stochastic programming problems. The course provides a review of probability and optimization concepts and covers different problem classes that include risk metrics, probabilistic constraints, and (partial) differential equations. The course also explores conceptual connections with non-smooth and mixed-integer optimization that facilitates modeling and analysis. Algorithms and software tools for the solution of continuous and mixed-integer formulations in parallel computers are also discussed. Numerical examples implemented in the open-source Julia programming language are provided. Finally, real applications are discussed to demonstrate the scope of the concepts and tools. 

Contact:  For information please contact  Victor M. Zavala [link]

Formulations:
- Introduction to Probability and Optimization
- Two-Stage and Multi-Stage Formulations
- Risk Management and Metrics
- Uncertainty Quantification 
- Sample Average Approximations
- Inference (Solution) Analysis
- Multi-Objective and Multi-Stakeholder Formulations
- Probabilistic Constraints

Algorithms:
- Nonconvex Continuous Optimization 
- Numerical Linear Algebra 
- Lagrangian Dual Decomposition
- Benders Decomposition
- Progressive Hedging
- Stochastic Dual Dynamic Programming
- Scenario Reduction

Applications and Software:
- Modeling and Solver Tools: Julia, DSP, PIPS-NLP, JuMP, PLASMO
- Network and Supply Chain Design
- Model Predictive Control
- Energy Systems: Power, Natural Gas, Batteries, Solar/Wind, Agriculture, Manufacturing

Course Dates:
 - April 2016 at KAUST, Saudi Arabia 
 - August  2016 at the University of Wisconsin-Madison
-  August 2016 at Dow Chemical Headquarters
-  December 2016 at ITESM, Mexico 
-  April 2017 at Universidad Nacional and Universidad de Antioquia, Colombia
-  July 2017 at Universidad de Valladolid, Spain
-  August 2017 at University of Wisconsin-Madison

Companies that have taken the Course:
- Air Liquide
- ExxonMobil
- Dow Chemical
- Johnson Controls
- Saudi Aramco

About the instructor:  Victor M. Zavala is the Richard H. Soit Assistant Professor in the Department of Chemical and Biological Engineering at the University of Wisonsin-Madison. Before joining UW-Madison, he was a computational mathematician in the Mathematics and Computer Science Division at Argonne National Laboratory. He holds a B.Sc. degree from Universidad Iberoamericana and a Ph.D. degree from Carnegie Mellon University, both in chemical engineering. He is the recipient of a Department of Energy Early Career Award under which he develops scalable optimization algorithms. He is also a technical editor of the Mathematical Programming Computation journal. His research interests are in the areas of mathematical modeling of energy systems, high-performance computing, stochastic optimization, and predictive control.