2024 INFORMS Annual Meeting
October 19-23, 2024

Our pre-conference workshop will cover a wide range of updates and practical tips, including a preview of new features that will be available with Gurobi 12.0, an overview of our new massive open online course (MOOC), and an interactive panel on GenAI and Optimization.

Gurobi 12.0 Overview: This talk will provide a preview of notable features in the upcoming Gurobi 12.0 release. Besides the usual performance comparisons against previous releases, we will consider advances in the global MINLP solver that was first introduced in the current version, 11.0. These include direct support for compound multivariate nonlinear expressions that eliminate the need for users to create auxiliary variables and constraints.

A Groundbreaking MOOC: We’ll also be presenting a short overview of “Introduction to Optimization Through the Lens of Data Science,” a groundbreaking massive-open online course (MOOC) developed by Gurobi in partnership with Dr. Joel Sokol, professor at Georgia Tech. This course provides a unique opportunity for anyone to enhance their skill sets and for educators to bring cutting-edge, practical knowledge into their classrooms.

Panel–“The Impact and Uses of Generative AI in Optimization: What’s Next?”: The workshop will conclude with a panel discussion featuring optimization experts from across industries, academia, and Gurobi as they discuss generative AI and optimization. All attendees will receive a special Gurobi t-shirt while supplies last.

Panel Participants

• Moderator: Dr. Greg Glockner, Gurobi Optimization
• Dr. Can Li, Assistant Professor at Purdue
• Dr. Warren Hearnes, Founder OptiML AI
• Dr. Irv Lusting, Optimization Principal, Princeton Consulting

The content presented is most relevant to: Associate (Early Career); Professional (Mid-Career); and Executive (Senior Level).

1. Term-based modeling involves constructing models with individual variables and constraints for detailed control.
2. Matrix-based expressions utilize linear algebra for efficient, large-scale model construction.
3. Data-first methods leverage pandas DataFrames and Series, integrating seamlessly with data-centric workflows.

In this workshop, we will walk you through the principles, best practices, and practical examples for each method. By the end of the workshop, you will be equipped to choose and implement the most suitable approach for your optimization projects. Join us to master Gurobi’s powerful Python APIs and enhance your modeling skills.

This talk will discuss new features and enhancements in Gurobi 12, including performance improvements. While considering all problem types that Gurobi solves, the talk will focus on the advances in the Gurobi Global MINLP Solver, including its direct support for modeling nonconvex multivariate nonlinear functions.

In this showcase, we will share insights and lessons learned from helping Gurobi customers from a wide range of industries adjust their optimization models to improve solver performance and numerical behavior. We will look at the challenges that we see most often in LP, MIP and MINLP models, and discuss our approach and typical recommendations to help address them. We will also consider some well-known modeling “rules of thumb” and discuss how applicable they are in 2024.

Version 1.0 of the gurobi-modelanalyzer open source Python package became available in late 2023. The primary component of the package was a function that provided a row or column based explanation of ill
conditioned basis matrices. While the package has had numerous successful use cases, potential challenges remained regarding faster computation time and smaller explanations. This talk will describe the improvements made in version 1.1 of the package that address these challenges.