The Best of Both Worlds: Python Simplicity Meets GAMS Performance

On the quest for finding an algebraic modeling environment that comes without bitter pills to swallow, Tim Varelmann presents a solution that enables native support for ML in optimization models.

GAMSPy itself is open-source and comes with a demo-license for the GAMS engine under the hood. Check out & star the repository here: https://github.com/GAMS-dev/gamspy

Previous part: "How I Reduced 2,500 Lines of Optimization Code to Just 48 Lines" at

https://videos.bluebirdoptimization.com/video/how-i-reduced-2500-lines-of-optimization-code-to-just-48-lines-1-c1v4

First part: "The Secret to Writing Maintainable Optimization Models" at

https://videos.bluebirdoptimization.com/video/the-secret-to-writing-maintainable-optimization-models-4604

Blog post with comparative analysis shown: https://www.gams.com/blog/2023/07/performance-in-optimization-models-a-comparative-analysis-of-gams-pyomo-gurobipy-and-jump/