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 - Supply Chains - Energy Systems: Power, Natural Gas, Batteries, Solar/Wind 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 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. |