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. |