2 February 2024 – Hands-on Mathematical Optimization with Python

Speaker: Joaquim Gromicho, Professor of Business Analytics at the University of Amsterdam and Science and Education Officer at ORTEC

Joaquim A.S. Gromicho acts as Science and Education Officer for ORTEC and is full professor of Business Analytics at the University of Amsterdam. He received his PhD in Optimization in 1995 from the Erasmus University Rotterdam, before spending two years as Assistant Professor at the University of Lisbon. He serves the Dutch Statistics and OR Society as editor in chief of STAtOR, a magazine on applications and impact, and the steering committee of the EURO Practitioner’s Forum.

In the era of generative AI and powerful ML packages prevalent in the Python ecosystem, the significance of Mathematical Optimization remains paramount. Recognizing the historical confinement of this tool to the realm of Operations Research experts, Krzysztof Postek, Alessandro Zocca, Jeff Kantor, and I have embarked on a mission to democratize its accessibility. In our collaborative effort, we’ve authored a hands-on, accessible yet ambitious manual, tailored for a diverse audience, including students and professionals. This comprehensive guide not only delves into traditional (mixed-integer) linear optimization but also explores network, convex, conic, stochastic, and robust optimization.

Scheduled for release by Cambridge University Press as an inexpensive textbook in 2024, our book, also available under a Green Open Access license, is designed to empower learners with practical insights. The latest prepublication version is accessible at https://ortec.com/en/featured-insights/insights/math-optimization-python. Realistic examples accompany each topic, and to further enhance the learning experience, we provide an online companion. This resource comprises 50+ fully functional notebooks readily deployable in Google Colab, serving as practical starting points to address diverse real-life challenges you may encounter.

For your convenience, the companion is available at https://mobook.github.io/MO-book/intro.html, within the permissive MIT license. Note that AMPL ported it to their own modelling language at https://mo-book.ampl.com/.

During this webinar, I will demonstrate representative content of the book in action and will also showcase how ChatGPT serves as a valuable collaborator in my professional endeavors, offering insights into its role in my workflow.



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