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. 

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

Algorithms:
- Nonconvex Continuous Optimization 
- Numerical Linear Algebra and Globalization Strategies
- Lagrangian Dual Decomposition
- Benders Decomposition
- Progressive Hedging
- Scenario Reduction

Applications and Software:
- Network Design
- Stochastic Optimal Control
- Combined Heat and Power Systems
- Multi-Stakeholder Decision-Making
- Modeling and Solver Tools: DSP, PIPS-NLP, JuMP, PLASMO

Course Dates:
 - April 26th-27th, 2016 at KAUST, Saudi Arabia [link]
 - August 4th-5th, 2016 at the University of Wisconsin-Madison
 - For information on on-site training please contact Victor M. Zavala [link]

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.