GAMS/GAMSPy and Snowflake walkthrough
Learn how to connect GAMS and GAMSPy with Snowflake to read model inputs, solve an optimization problem, and write results back to Snowflake.
Overview
18 resources
Navigating Trade-Offs: Introducing the GAMS Multi-Objective Optimization (MOO) Framework
Learn how the GAMS Multi-Objective Optimization framework makes it easier to explore trade-offs between competing objectives and generate Pareto fronts without custom implementations.
Turning CGE Models into Policy Platforms with GAMS MIRO
CGE models are powerful tools for policy analysis, but their complexity can limit their reach. This post explains how GAMS MIRO helps turn CGE models into interactive policy platforms for scenario design, comparison, visualization, and …
Free GAMS and GAMSPy Licenses for Personal and Academic Use
Compare free personal and academic GAMS and GAMSPy licenses, understand their permitted uses and limits, and learn how to generate and install a license.
Solving Optimization Models on Kubernetes using Engine K
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.
Transforming Mathematical Models into Enterprise Assets with GAMS EngineOne
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
Update (October 2026): This update brings all solvers up to their latest versions and directly resolves a key concern regarding the MIPfeas benchmark: hardware discrepancies across runs. Moving forward, all solvers are evaluated on a …
Turning Solver Complexity into Solver Power: The GAMS Advantage
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 …
GPU-Accelerated Optimization with GAMS and NVIDIA cuOpt
For a long time, GPUs were essential for various AI applications and other high-performance compute applications but had limited impact on mathematical optimization. That’s changing.
GAMS Transfer R
GAMS Transfer R was first released with in August 2022 and has been included in all subsequent GAMS releases. Recently, we made GAMS Transfer R open-source and available on CRAN. In this blog post, we will provide a brief overview of GAMS …
Why Your Business Needs Optimization
This blog post discusses the necessity of business optimization through the Binary Paintshop Problem, illustrating how businesses can minimize resource usage and improve efficiency by adopting optimization strategies. Using a simple …
GDX source code published on GitHub
Over 23 years after its inception, the GDX library is now ported to C++17 and its source code freely available on GitHub. This implicitly documents the layout of the GDX file format and gives everyone the ability to build and maintain their …
Introducing GAMSPy
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.
Progress in MINLP Solver Technology
MINLP solvers typically have a rooting in academia. This article highlights the historic performance gains of two of them, BARON and SCIP.
An Overview of Math Programming Solvers
Math programming solvers are the workhorses called upon by GAMS to produce optimal solutions to your mathematical models. This overview provides assistance when you are faced with selecting the correct solver for your particular model.
Introducing: GAMS Transfer
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.
Model Instances with more than 2³¹ Non-Zeros - Impact on Energy Systems Research
With GAMS 37, we now support the generation of model instances with more than 2³¹ non-zeros. We expect huge models to become more important in the future.
New and improved GAMS links for Pyomo and JuMP
We've got some new links from Pyomo and JuMP to GAMS, which allow efficient interfacing of GAMS with your Pyomo or JuMP models.