I need help using Gurobi. What kind of support is available?

Gurobi is a complex product, and some components and settings require expert knowledge. Whether you are migrating from a different solution or starting a project from scratch, we are happy to answer your “how to” questions and point you to the right resources.

Common customer requests:

  • How do I introduce multiple objectives to my optimization model?
  • I need to incorporate a complex business rule into our existing model. Can you recommend a way to model this or point me in the right direction?
  • I want to migrate my solution to Gurobi Instant Cloud. Can you help me extend my existing implementation?

How can I get help with performance tuning?

Gurobi is preconfigured with settings that generally work well across a broad set of models. However, it is likely that for a specific set of models, we can find specialized settings that perform even better. We search for such settings by combining our deep understanding of the solver with the power of a large computing cluster.

Common customer requests:

  • We need to solve an optimization problem every 5 minutes. What settings should we use to consistently find the best possible solutions in this timeframe?
  • Can you help us reduce the time it takes to find high-quality solutions for our model instances?
  • Is distributed optimization the right choice for our use case?
  • We are using a variety of custom parameter settings that improved performance when using previous versions of Gurobi. Are these settings still beneficial for the latest version of Gurobi?

How can I use Gurobi APIs most efficiently?

Using software libraries the right way can make a significant impact. We can help you use our APIs efficiently, thereby reducing the time required to construct and interact with the model.

Common customer requests:

  • Our model-building phase is far more time-consuming than the actual optimization process. Can you help us improve our Python implementation?
  • We have migrated our model to use Gurobi’s native API. Can you help us review our implementation?
  • Could you review this code snippet and let us know if we are calling these functions properly?

I need guidance around my architecture deployments. Can you help?

Modern applications leverage the power of scalable virtualized environments—but not all of these environments fit or scale well for optimization applications.

Whether you are building a new optimization application or updating an existing application, we can recommend architecture deployments that satisfy your technical needs. We make sure you are comfortable with any migration processes involved, and we can assist during upgrades to reduce the risk of service interruptions.

Common customer requests:

  • Can you please help us set up our Compute Servers in a high- availability deployment?
  • We are upgrading our current architecture to use microservices. Considering our models and business case, should we run our optimization processes directly on worker nodes or offload the computation to dedicated servers in the same cluster?
  • Considering our benchmark results and projected production optimization usage, what would you recommend for our on-premises architecture?

I’m building an optimization application, and I have some questions. Can you help?

As you develop your optimization application, you may encounter questions like why the solver behaves in a certain way or how a change to the model leads to a particular outcome. We can help you understand exactly what is happening.
Common customer requests:

  • The numeric statistics of my model seem reasonable. Why does the solve time vary significantly after the data changes slightly?
  • I doubled the number of cores used to solve my models. Why don’t I see significant speed improvements? What is more important for my model: a faster clock speed or more cores?
  • Can you help me understand what’s happening in the log file?
  • Why does Gurobi display warnings, and what can I do about it?

Where can I find enablement and implementation guidance?

After successfully tuning your models to achieve the best performance on your hardware, you may want to assess if further solution time reductions are possible. You are considering different ideas, such as reformulating your model, exploiting the knowledge of your problem, etc. We can help you by engaging in technical discussions based on your ideas and use case. Our goal is to make your implementation efforts more effective and get the most out of the solver.

Common customer requests:

  • We are not happy with the overall performance of our current implementation. Can you help us explore a decomposition approach for which we can leverage Gurobi’s performance on the resulting subproblems?
  • We have observed that for some of our model instances, feasible solutions are found quickly, but it is taking considerable time to prove optimality. Do you see any obvious improvements we can make to our formulation?
  • We have gained insight into our problem instances and would like to refine our solution strategy to reduce our solve times. Can you help review our callback implementation to incorporate custom cuts to the solution process and define a more advanced termination criterion?

Additional Insight

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Guidance for Your Journey

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What's
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News
Gurobi 10.0 Delivers Blazing-Fast Speed, Innovative Data Science Integration, and an Enterprise Development and Deployment Experience
Latest release enables data professionals to easily integrate machine learning models into optimization models to solve new types of problems.
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Webinar: What’s New in Gurobi 10.0
In this webinar, attendees will get a first look at our upcoming product release, Gurobi 10.0. We will summarize the performance improvements and highlight some of the underlying algorithmic advances, such as the network simplex algorithm, enhancements in concurrent LP, and optimization based bound tightening.
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