MO-Book: AMPL + Python 🐍

The repository of notebooks Hands-On Mathematical Optimization with AMPL in Python introduce the concepts and tools of mathematical optimization with examples from a range of disciplines. The goals of these notebooks are to:

• provide a foundation for hands-on learning of mathematical optimization,

• demonstrate the tools and concepts of optimization with practical examples,

• help readers to develop the practical skills needed to build models and solving problems using state-of-the-art modeling languages and solvers.

Table of Contents

MO-BOOK: Hands-On Mathematical Optimization with AMPL in Python 🐍

1. Mathematical Optimization

2. Linear Optimization

3. Mixed Integer Linear Optimization

4. Network Optimization

5. Convex Optimization

6. Conic Optimization

7. Accounting for Uncertainty: Optimization Meets Reality

8. Robust Optimization - Single Stage Problems

9. Stochastic Optimization - Single Stage Problems

10. Two-Stage Problems

Authors

• Krzysztof Postek, Boston Consulting Group (formerly TU Delft)

• Alessandro Zocca, VU Amsterdam

• Joaquim Gromicho, ORTEC and the University of Amsterdam

• Jeffrey Kantor, University of Notre Dame

Citation

If you wish to cite this work, please use

@book{PostekZoccaAMPL2024,
  title     = "Hands-On Mathematical Optimization with AMPL in Python",
  author    = "Postek, Krzysztof and Zocca, Alessandro and Gromicho, Joaquim and Kantor, Jeffrey",
  year      = 2024,
  publisher = "Online",
  url       = "https://ampl.com/mo-book"
}