by Postek, Zocca, Gromicho, Kantor with Filipe Brandao for AMPL additions.
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.
→ Ideal for students, researchers, and practitioners in operations research, data science, and engineering.
→ No prior experience with AMPL or mathematical optimization required—perfect for beginners and professionals alike.
→ Designed specifically for users who want to integrate AMPL within the Python ecosystem.
→ Learn how to formulate and solve optimization models using AMPL’s powerful modeling capabilities alongside Python scripting.
→ Step-by-step modeling techniques for solving linear, nonlinear, and integer optimization problems.
→ How to efficiently run, analyze, and refine models with AMPL and Python-based tools.
→ Over 100 coding exercises to practice real-world applications.
→ Free access to AMPL and solvers for hands-on learning.
→ Accompanying datasets, code examples, and instructional videos to reinforce key concepts.
AMPL is committed to democratizing optimization software for non-commercial settings. Get the full AMPL system with industry leading solvers for free use in Academia.
