The GAMS blog

Solving Optimization Models on Kubernetes using Engine K

May 18, 2026 Engine
GAMS Engine K is our enterprise-grade job and user management system for running GAMS optimization models on Kubernetes. It is built for high-demand environments that require maximum scalability, flexibility, and resilience.
GAMS/GAMSPy and Databricks walkthrough

May 11, 2026 GAMS GAMSPy Features
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 hub for enterprise data-managed in Delta tables, governed by Unity Catalog, and queryable via SQL. While the integration is straightforward, navigating the initial setup can be a hurdle. This walkthrough is designed to clear that path, providing a definitive guide for basic GAMS and GAMSPy use cases in the modern data stack.
The Gamification of Optimization Hackathon at AGU: Powering Creative Game Design with GAMSPy

Apr 22, 2026 GAMSPy Python
We are excited to share the results of our Gamification of Optimization Hackathon held at Abdullah Gül University (AGU). Over 68 students across 25 teams spent a high-energy 12-hour sprint transforming complex mathematical constraints into engaging, ready-to-play games using GAMSPy. From rockstar concert planning to smart agricultural water management, discover how these innovative projects bridged the gap between academic theory and creative game design.
Transforming Mathematical Models into Enterprise Assets with GAMS EngineOne

Apr 20, 2026 Engine White paper
Mathematical optimization drives modern industrial efficiency, but many organizations face a Deployment Gap: the disconnect between a model solved on an engineer’s laptop and a scalable enterprise service.
Expanding the Focus: Introducing the MIPfeas Benchmark

Mar 17, 2026 Last updated: May 19, 2026 Solvers

Update (May 2026): The article has been extended and includes now result from the ReXi solver. ReXi is a parallel solver for mixed-integer programming, built on SCIP and SoPlex, focused on quickly finding strong primal solutions. ReXi is a private research project by Gioni Mexi and Daniel Rehfeldt . Moreover, the CPU based solvers runs have been moved to a fast CPU. The instances are small enough to run on the faster desktop CPU Intel Core Ultra 9 285K rather than the server CPU AMD EPYC 9B45.
Optimizing Weather Radar Networks for Texas: The Winning GAMSPy Student Competition Fall 2025 Project

Feb 8, 2026 Gamspy Python
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 meteorological challenge of improving early warning systems. Framing the problem as a Facility Location Problem (FLP), Nikhil took an innovative approach that combined physics and optimization in GAMSPy.
The Year 2025 for GAMS Solvers

Jan 26, 2026 Solvers
GAMS includes around 30 mathematical programming solvers with its distribution, many of them receiving constant updates. In this short article, we revisit major updates to these solvers from 2025.
Gurobi Returns to GAMS and GAMSPy

Nov 24, 2025 GAMS GAMSPy Solvers
What if your optimization runs finished in half the time - or if a complex MIP you thought was intractable now converged in minutes? Gurobi returns to the GAMS ecosystem and users once again have access to one of the leading high-performance solvers.
Speed vs. Guarantees: A Practical MIP–NLP Trade-off for NN Robustness

Nov 12, 2025 GAMSPy
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.
Turning Solver Complexity into Solver Power: The GAMS Advantage

Nov 10, 2025 GAMS GAMSPy Solvers
Mathematical optimization depends on solvers - yet using them effectively can be daunting. At GAMS, we turn solver complexity into solver power, providing both a unified modeling interface and deep, decades-long expertise in solver development.

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Authors

Ahmed Alqershi

Atharv Bhosekar

Adam Christensen

Aileen Homey

Alexander Meeraus

André Schnabel

Bruce McCarl

Burak Usul

Christophe Gouel

Clemens Westphal

Evangelos Panos

Fred Fiand

Freddy Proske

George Franco

Hans Lofgren

Justine Broihan

Julien Harou

Janina Hasselbring

Leonard Kunz

Logan Randolph

Lutz Westermann

Mohammed Basheer

Marius Bützler

Michael Bussieck

Merve Demirci

Mateo Gallia

Michael Horstmann

Muhammet Soytürk

Renke Kuhlmann

Robin Schuchmann

Steve Dirkse

Stefan Mann

Stefan Vigerske