21 resources
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.
See which solvers are included with free personal GAMS and GAMSPy licenses and which additional solvers are available to academic GAMSPy users.
Compare free personal and academic GAMS and GAMSPy licenses, understand their permitted uses and limits, and learn how to generate and install a license.
Update (July 2026): The article has been extended and includes now result from SCIP Concurrent solver. SCIP-Conc is a parallel solver for mixed-integer programming, built on SCIP and has been around for some time. Recent activities for …
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.
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 …
Presentation slides covering the evolution of CONOPT, recent performance and usability improvements, new APIs, and derivative-based nonlinear optimization workflows.
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.
The Austrian Power Grid (APG) partnered with our consulting team to optimize its electricity transmission model. As a result, model-solving time was slashed by over 60%, memory usage dropped by 80%, and various infeasibilities were …
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 …
MINLP solvers typically have a rooting in academia. This article highlights the historic performance gains of two of them, BARON and SCIP.
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.
A recent webinar by Evangelos Panos demonstrates how to tune CPLEX options to get more bang for your buck when running large problems such as the TIMES ESM framework.
Global Dairy Trade trading event auctions are the leading global online marketplace for trading large volume dairy ingredients and for reference price discovery. The GDT Trading Events and GDT Pulse Auctions are powered by CRA’s Trading …
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**.
At the United States Military Academy in West Point, all students must graduate in four years. Each student's daily activities are a carefully regimented balance of academic, military, and physical requirements. The ~4,500 enrolled cadets …
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 …
We've got some new links from Pyomo and JuMP to GAMS, which allow efficient interfacing of GAMS with your Pyomo or JuMP models.
Supported by several TSOs, the 50Hertz Transmission GmbH, TSO of Eastern Germany and part of the Elia group, manages an internet platform to collect bids and execute described auctions of balancing reserves. A GAMS model helps them match …
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.