15 resources
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.
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 …
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.
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.
In the third part of our GAMS MIRO walkthrough, we will fine-tune our application with custom code. We'll show you how to create a custom renderer, widget, and importer/exporter.
In the second part of our GAMS MIRO walkthrough, we will explore all the configuration options available, from changing general settings to defining default views. We'll also demonstrate how to add an interactive dashboard to display our …
In the first part of our GAMS MIRO walkthrough, we will guide you through converting a GAMSPy model into a fully functional web application. We'll also show you how to set up MIRO for rapid prototyping and introduce you to the powerful …
If you use multiple web services, you probably know how difficult it can be to manage multiple accounts and passwords, especially in corporate environments. GAMS Engine allows you to use LDAP or OAuth2 for single sign on and make user …
Are you ready to transform your GAMS modeling class with modern tools and streamline grading? In this blog post, our guest author Christophe Gouel from INRAE explains how he adjusted his workflow.
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 …
Learn how to use GitHub Actions with GAMS Engine to run automated tests for your optimization models.
GAMS and the packaged solvers impose restrictions with regards to the problem sizes that can be solved with a free demo or community license. For academic users, there is a free alternative to run large models: **NEOS server**.
Optimization problems occasionally yield unbounded solutions. To find the cause one can modify the model and solve it to gain information. This is done through the imposition of “artificially” large bounds. Linear programming solvers …
Learn how to diagnose and correct numerical scaling problems in GAMS models using scaling attributes, GAMSCHK, CONVERT, and solver diagnostics.
Learn how to diagnose infeasible GAMS models by adding artificial variables, analyzing distorted marginals, and isolating conflicting equations and variable bounds.