Workshop Stochastic Programming: Formulations, Algorithms, and Applications

Victor M. Zavala, University of Wisconsin-Madison

Date and Time

  • 18.8.2018 8:30am – 5pm
  • 19.8.2018 8:30am - 2pm

The workshop will take place at the Fluno Center - the conference location

Registration and registration fee 

  • Participants of the workshop need to register for the workshop a priori using the papercept registration link. 
  • The workshop fee is 150$ for students and 200$ for normal NMPC participants
  • The registration covers: course material, coffee breaks
  • In case that the number of participants is too low, the workshop might be canceled and refunds will be issued.

This 2-day short course is targeted towards graduate students, researchers, 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 measures, probabilistic constraints, stochastic dominance, 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. 

Covered Topics:

  • Formulations:
    • Introduction to Probability and Optimization
    • Two-Stage and Multi-Stage Formulations
    • Risk Measures and Mitigation
    • Uncertainty Quantification 
    • Sample Average Approximations
    • Inference (Solution) Analysis
    • Multi-Objective Formulations
    • Probabilistic (Chance) Constraints
    • Stochastic Dominance and Conflict Resolution
  • Algorithms:
    • Mixed-Integer and Continuous Optimization 
    • Numerical Linear Algebra 
    • Lagrangian Dual Decomposition
    • Benders Decomposition
    • Progressive Hedging
    • Stochastic Dual Dynamic Programming
  • Applications and Software:
    • Modeling and Solver Tools: Julia, DSP, PIPS-NLP, Ipopt, JuMP, PLASMO
    • Parallel Computing and Scalability Issues
    • Network and Controller Design
    • Model Predictive Control and Real-Time Optimization
    • Chemical, Power, Natural Gas, Batteries, Solar/Wind, Agriculture, Manufacturing

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. 


Last Modification: 25.06.2018 - Contact Person:

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