Webinar Overview

The importance of incorporating uncertainty into optimization problems has always been known; however, both the theory and software were not up to the challenge to provide meaningful models that could be solved within a reasonable run time. Over the last 15 years, the continuous improvements made to the theoretical as well as the algorithmic area of stochastic and mixed integer linear optimization have changed this situation dramatically. In this recording, we focus on stochastic optimization models and easy-to-understand algorithms, amenable to being easily solved with Gurobi. The intended audience for this webinar includes those with a background in optimization and knowledge on basic probability and statistics. This 35-minute video recording consists of:

  • A quick introduction to stochastic optimization
  • Types of stochastic optimization problems
  • Types of models that can be solved easily: two-stage stochastic problems with expected value and coherent risk measures
  • Overview of the main algorithms: sample average approximation
  • Examples of common problems with Gurobi

Presented Materials

You can download the materials associated with this webinar here.

Meet the Experts

What's
New at Gurobi

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.
 Learn More
Event
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.
 Learn More
new content
Cost Savings & Business Benefits for Gurobi Customers
2022 Total Economic Impact™ Study Reveals A 518% ROI with Gurobi
 Learn More