Skip to content

Latest commit

 

History

History
42 lines (28 loc) · 2.64 KB

intro.md

File metadata and controls

42 lines (28 loc) · 2.64 KB
Raw

Data-Driven Mathematical Optimization in Python

Welcome to this repository of companion notebooks for Data-Driven Mathematical Optimization in Python, a project currently under development with completion expected by Spring, 2023. These notebooks 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 problem using state-of-the-art modeling languages and solvers.

Getting started

The notebooks in this repository make extensive use of Pyomo which is a complete and versatile mathematical optimization package for the Python ecosystem. Pyomo provides a means to build models for optimization using the concepts of decision variables, constraints, and objectives from mathematical optimization, then transform and generate solutions using open source or commercial solvers.

All notebooks in this repository can be opened and run in Google Colab. A launch icon appearing at the top of a page (look for the rocket) indicates the notebook can be opened as an executable document. Selecting Colab will reopen the notebook in Google Colab. Cells inside of the notebooks will perform any necessary installations of Pyomo and solvers needed to execute the code within the notebook.

Start your journey with the first chapter!

Help us!

We seek your feedback! If you encounter an issue or have suggestions on how to make these examples better, please open an issue using the link at the top of every page (look for the Github cat icon).

About Us

We are a group of researchers and educators who came together with a common purpose of developing materials for use in our classroom teaching. Hopefully, these materials will find use in other classrooms and, most importantly, by those seeking entry into the world of building optimization models for data-rich applications.

  • 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{PostekZocca2022,
title     = "Data-Driven Mathematical Optimization in Python",
author    = "Postek, Krzysztof and Zocca, Alessandro and Gromicho, Joaquim and Kantor, Jeffrey"
year      = 2023,
publisher = "Online",
url       = "https://mobook.github.io/MO-book/"
}