Hands-On Mathematical Optimization with Python

A hands-on Python-based guide to mathematical optimization for undergraduates and graduates, with numerous applications and code samples.

Krzysztof Postek (Author), Alessandro Zocca (Author), Joaquim A. S. Gromicho (Author), Jeffrey C. Kantor (Author)

9781009493505, Cambridge University Press

Paperback / softback, published 16 January 2025

354 pages
25.4 x 17.8 x 2 cm, 0.68 kg

'This book fills a gap in the literature. It is a bridge between computer programming and mathematical optimization.' Joao Faria, Florida Atlantic University

This practical guide to optimization combines mathematical theory with hands-on coding examples to explore how Python can be used to model problems and obtain the best possible solutions. Presenting a balance of theory and practical applications, it is the ideal resource for upper-undergraduate and graduate students in applied mathematics, data science, business, industrial engineering and operations research, as well as practitioners in related fields. Beginning with an introduction to the concept of optimization, this text presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to convex optimization and optimizations under uncertainty. The book's Python code snippets, alongside more than 50 Jupyter notebooks on the author's GitHub, allow students to put the theory into practice and solve problems inspired by real-life challenges, while numerous exercises sharpen students' understanding of the methods discussed.

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
9. Stochastic optimization
10. Two-stage problems
Appendix A. Linear algebra primer
Appendix B. Solutions of selected exercises
List of Tables
List of Figures
Index.

Subject Areas: Optimization [PBU]