Join us at INFORMS!
Gurobi Optimization will be presenting and exhibiting at the 2023 INFORMS Business Analytics Conference. This conference will be held in Aurora, CO from April 16th – 18th.
Visit us at Booth 207 to:
Learn about the new features and performance enhancements in the Gurobi Optimizer 10.0.
Chat with our optimization experts.
Play The Burrito Optimization Game and stand a chance to win cool prizes.
Check out all of the sessions where you can find Gurobi team members presenting at INFORMS below!
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Gurobi Technology Workshop |
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Gurobi 10.0 Helping You to Build, Solve, and Deploy Optimization Models |
| Presented by: |
Dan Jeffrey
Xavier Nodet
Zed Dean
Jerry Yurchisin |
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| Time: |
3:00pm – 4:45pm |
| Date: |
Sunday, April 16th |
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In this workshop, attendees will learn about our latest release, Gurobi 10.0, and our open-source Python projects that make it easier for users to build models with machine learning models and pandas. We’ll touch on exciting performance improvements to Gurobi’s core algorithms in Gurobi 10.0. We’ll also highlight features that make the model-building and solving process easier.
In the second half of the workshop, attendees will learn about Gurobi’s experimental, open-source Python packages: Gurobi Machine Learning and Gurobipy Pandas. Gurobi Machine Learning allows users to add trained machine learning models as constraints to Gurobi models. Gurobipy Pandas enables users to easily build Gurobi models from Pandas dataframes.
Finally, we will explore a notebook example that combines machine learning and optimization to demonstrate how to use these new features.
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Gurobi Tutorial |
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Gurobi Machine Learning: Incorporate your Machine Learning Models into Optimization
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| Presented by: |
Alison Cozad, PhD.
Zed Dean |
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| Time: |
9:10- 10am |
| Date: |
Tuesday, April 18th |
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Gurobi is making it easier to plug your predictive models directly into your optimization model. The Gurobi Machine Learning is an experimental, open-source Python package that allows users to add trained machine learning regressors as a constraint to a Gurobi model (e.g., from scikit-learn, TensorFlow/Keras, or PyTorch). Thus, allowing for tighter integration between trained predictions and optimal decision-making.
This tutorial will introduce the Gurobi Machine Learning package and how it fits into an optimization application. Then we will explore how these machine-learning models are incorporated into a Gurobi model through a couple of examples. |
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Gurobi Tutorial |
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Gurobi’s Newest Educational Resources: Where Data Meets Decisions – An Overview of Our Free Jupyter Notebook Data Science Example Library
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| Presented by: |
Rahul Swamy
Jerry Yurchisin |
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| Time: |
10:30 – 11:20 AM |
| Date: |
Monday, April 17th |
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How can you use different prediction models for avocado price optimization? How can you identify plagiarism with text similarity? How can you effectively plan for airline disruption in time of continual flight delays and cancelations? How can you discover lesser-known artists in your daily music playlists? How can you build the perfect fantasy basketball team?
By combining data science tools and mathematical optimization.
In this session, Gurobi will introduce several of our newest (and free) educational examples that students and instructors can use to learn and teach real-world applications of combined data science and optimization problem solving. We will review our new data science library of Python Notebook Examples that combine predictive and prescriptive analytics and offer new data science learners an entry point into problem-solving with optimization.
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