13 resources
Moving data between environments is a relic of the past; with GAMS and GAMSPy, you can formulate and solve large-scale optimization models without ever duplicating data or leaving your secure environment. Databricks has become the central …
We are happy to present the winning project of our GAMSPy Student Competition Fall 2025. Nikhil Trivedi, an undergraduate student from the University of Wisconsin-Madison, took first place with a project that tackles the critical …
A hands-on comparison of ReLU modeling in GAMSPy, MIP vs NLP, for adversarial verification on realistic networks. The workflow, code snippets, and trade-off between speed and guarantees are all explained in details.
Workshop slides introducing mathematical optimization with GAMSPy, from modeling fundamentals and solver selection to data handling and practical Python workflows.
Discover how to formulate and solve mathematical optimization models in Python with GAMSPy in this webinar and live tutorial from GAMS experts.
Presentation slides showing how GAMSPy embeds trained neural networks into mathematical optimization models through automated reformulations.
Workshop slides introducing GAMSPy modeling fundamentals, design concepts, Python workflows, solver access, and migration from GAMS to GAMSPy.
To support their carbon neutrality ambitions, the global energy company TotalEnergies developed a complex optimization model for Carbon Capture and Storage (CCS). This highly sophisticated model, capable of managing the complex CO2 …
We are happy to present the winning project of our 2024/2025 GAMSPy Student Competition. Yiheng Su, a PhD student from the University of Wisconsin-Madison, developed an optimization model to plan the most efficient and enjoyable day at …
Learn how GAMSPy revolutionizes mathematical optimization by seamlessly integrating the power of Python's expressive language with the efficiency of GAMS, offering a streamlined and high-performance solution for complex modeling challenges.
GAMS is good at finding decision variable values that provide the optimal solution to a problem for a given set of scenario parameters. It is more difficult to find a strategy that can test combinations of potentially many parameters in an …
Our new data-centric GAMS Transfer API allows convenient exchange of large data sets between GAMS and Matlab or Python, and uses the native data structures of those platforms. This Python example shows how easy it is to use GAMS Transfer.
We've got some new links from Pyomo and JuMP to GAMS, which allow efficient interfacing of GAMS with your Pyomo or JuMP models.