Ronald van der Velden

PuLP is one of the modeling frameworks that support Gurobi. In this article, we describe how to use Gurobi through PuLP. For more information, or when you encounter PuLP-related issues, please have...

Pyomo is one of the modeling frameworks that support Gurobi. In this article, we describe how to use Gurobi through Pyomo. For more information, or when you encounter Pyomo-related issues, please h...

Since Gurobi is essentially a library for performing mathematical optimization, it will always be integrated in an application. The first benchmark in benchmarking Gurobi against other solvers is t...

When you're evaluating Gurobi, there are several things you could measure as a way to quantify performance. In fact, there is not one correct answer to the question in the title of this article. In...

Components and responsibilitiesIn any architecture involving Gurobi, we have three key responsibilities: Constructing mathematical models (and processing solutions) Solving these models Licensing W...

Mathematical optimization is a compute-intensive task. For this reason, the choice of (virtual) hardware is quite relevant as it may have an impact on the performance of your model and solver. Unfo...

IntroductionGurobi can be used in many different architectures, ranging from development on a single laptop or Databricks notebook, to a cluster of Compute Server instances that together solve mode...

IntroductionGurobi can be accessed through our own API in many programming languages, including gurobipy for Python. Although we recommend using the official APIs, many of our users come from a sit...

In the context of Gurobi, we’re typically looking at (1) performance for solving a single model (2) performance when solving multiple models concurrently. In that context:Vertical scaling for a sin...

In some long-running optimization jobs, it may be desired to interrupt the job based on conditions that may or may not depend on the state of the optimization job itself. While Gurobi provides call...