Visit our Booth 35/36 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.
Hear from Gurobi Presenters at INFORMS, including:
Zonghao Gu, PhD, Chief Technology Officer and Co-Founder of Gurobi Optimization
Ed Klotz, PhD, Senior Mathematical Optimization Specialist
Alison Cozad, PhD, Optimization Engineer
Juan Antonio Orozco Guzmán, Optimization Engineer
Lindsay Montanari, Academic Program Director
Our Gurobi 10.0 Pre-Conference Workshop on Saturday, October 15th will include a Panel Discussion on “Optimization Myths, Misconceptions, and Imperfect Understanding” with the following members across industry and academia:
Dimitri Papageorgiou, Senior Research Engineer at ExxonMobil
Juan Antonio Orozco Guzmán, Optimization Engineer at Gurobi Optimization
Thiago Serra, Assistant Professor of Business Analytics at Bucknell University
Jean-Paul Watson, Senior Research Scientist at Lawrence Livermore National Laboratory
Diana Ramirez-Rios, Assistant Professor of Industrial and Systems Engineering at University at Buffalo
Moderated by Dr. Alison Cozad, Optimization Engineer, Gurobi Optimization
Check out all of the sessions where you can find Gurobi team members presenting at INFORMS below!
| Gurobi Technology Workshop | |
|---|---|
| Session: | Technology Workshop: Gurobi 10.0 Helping You to Build, Solve, and Deploy Optimization Models |
| Presented by: | Dr. Alison Cozad, Dr. Zonghao Gu, Dr. Ed Klotz, Lindsay Montanari |
| Location: | Convention Center, Room 105 |
| Time: | 4:00pm – 6:30pm |
| Date: | Saturday, October 15 |
| In this workshop, attendees will get a first look at our latest product release, Gurobi 10.0 — including exciting performance improvements to Gurobi’s core algorithms. We’ll also highlight features that will make the model-building and solving process easier. To help your model building process, we will share our new Python packages and Gurobipy enhancements that incorporate machine learning into model formulations, improve the matrix API to align with NumPy conventions, and make it even easier to use Pandas with Gurobi. To make your solving process easier, we will show how we’ve enhanced parameter tuning tool and show a tool to identify the source of a model’s numerical problems. The workshop will also include a panel discussion, with optimization experts across academia, industries, and Gurobi, who will discuss the origins, veracity, and impact of current optimization myths, misconceptions, and imperfect understandings. We’ll also be presenting a live demo of the Burrito Optimization Game (BurritoOptimizationGame.com), the educational resource that introduces players to the power and capabilities of mathematical optimization. |
| Gurobi Participated Session | |
|---|---|
| Session: | Gamifying Learning in Mathematical Optimization: The Burrito Optimization Game |
| Presented by: | Dr. Larry Snyder |
| Location: | CC – Room 208 |
| Time: | 8:00am – 9:15am |
| Date: | Monday, October 17 |
| The Gurobi Burrito Optimization Game is a new educational game designed to introduce learners to the power of optimization. Players choose locations for burrito trucks on a city map to maximize their revenue. The free web-based game can be used in OR, data science, or other courses, or as a standalone app. The game teaches users why optimization is valuable and important, why it’s difficult, and why solvers and other optimization algorithms are. In this talk, I will introduce the game and discuss learning outcomes that can emerge, including teachable moments such as the tradeoff between fixed costs and variable revenues, directional changes in the optimal solution as the data change, and why enumeration is not a practical approach. |
| Gurobi Tutorial | |
|---|---|
| Session: | Gurobi Tutorial: Introduction to Gurobipy: Gurobi for Python |
| Presented by: | Alison Cozad, PhD., Optimization Engineer, Juan Antonio Orozco Guzmán, Optimization Engineer |
| Location: | CC – Wabash 1 |
| Time: | 8:40am – 9:15am |
| Date: | Monday, October 17 |
| Join us for this tutorial and discover why gurobipy is our most popular interface and how it can help you to harness the power of mathematical optimization. In this tutorial, we’ll walk through the modeling constructs and data structures that are necessary to translate a math formulation into a machine-readable format. Finally, we’ll review some of the best practices – and a few pet peeves – for deploying optimization models in Python. Be sure to bring your laptop if you’d like to follow along with us! |
| Gurobi Participated Session | |
|---|---|
| Session: | Diagnosis And Explanation Of Ill Conditioned Basis Matrices |
| Presented by: | Dr. Ed Klotz |
| Location: | M – Arizona |
| Time: | 11:00am – 12:15pm |
| Date: | Monday, October 17 |
| Ill conditioning in LP and MIP models remains a challenge for optimization practitioners. Several LP and MIP solvers offer functionality that provides explanations of infeasibility, but so far none have offered similar functionality to provide concise explanations of infeasibility. In this talk we will describe a method based on the reciprocal relationship between ill conditioning and. distance to singularity of a matrix to derive a certificate of ill conditioning. The certificate can then be used to filter out rows or columns of the matrix, resulting in a focused explanation of the cause of the ill conditioning. We will show results with this method on some models drawn from practical sources. |
| Gurobi Participated Session | |
|---|---|
| Session: | New Performance Techniques In The Gurobi Optimizers |
| Presented by: | Dr. Zonghao Gu, Dr. Ed Klotz |
| Location: | CC – Sagamore 6 |
| Time: | 12:30pm – 1:45pm |
| Date: | Monday, October 17 |
| This talk will discuss various new techniques or ideas to improve the performance to solve mixed integer programs, quadratic programs, and linear programs, in particular network flow problems, then shows the computational results for these techniques. |
| Gurobi Participated Session | |
|---|---|
| Session: | Where Optimization Meets Prediction: Novel Use-cases From Gurobi Optimization For Data Scientists |
| Presented by: | Rahul Swamy |
| Location: | CC – Exhibit Hall D |
| Time: | 12:30pm – 1:45pm |
| Date: | Monday, October 17 |
| Can optimization co-exist with prediction? This poster spotlights two novel use-cases – in a music recommendation system and in avocado supply-chain optimization – to highlight how predictive and prescriptive analytics can synergize to create wholesome technologies. |
| Gurobi Participated Session | |
|---|---|
| Session: | Automating Insights In The Burrito Optimization Game |
| Presented by: | Dr. Alison Cozad, Dr. Larry Snyder |
| Location: | CC – Wabash 3 |
| Time: | 8:00am – 9:15am |
| Date: | Tuesday, October 18 |
| In this talk, we showcase several methods to generate insights in Gurobi’s Burrito Optimization Game — a facility location optimization game. These automatically generated insights mimic how an expert in the game would compare their proposed solution to the optimized solution. By teaching how to assess the quality of their decisions, we encourage players to think critically about both their own decisions and the optimal solution. While these insights are aimed at teaching optimization concepts to those new to the field, we will use this as a foundation to discuss how to extend these ideas to industrial applications. By incorporating thoughtful post-solution analysis, we can help the users of our optimization applications understand solutions, gain insights into the physical system, and gain confidence in the results. |
| Gurobi Tutorial | |
|---|---|
| Session: | Gurobi Tutorial: Fundamentals of Ill Conditioning |
| Presented by: | Dr. Ed Klotz |
| Location: | CC – Wabash 1 |
| Time: | 2:40pm – 3:15pm |
| Date: | Tuesday, October 18 |
| Highly ill conditioned mathematical programming models can pose time-consuming challenges to practitioners regarding both solver run time and accuracy of solutions. This tutorial will present fundamental concepts and definitions that can help the practitioner more effectively assess the level of ill conditioning in a model and reduce development and maintenance work. Some common but easily remedied model characteristics that can cause ill conditioning will be described, and some practical examples will be discussed. |
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