How Optimization Modeling Creates Value for an Organization
Learn how to better showcase the value of optimization in your organization.
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Combining Optimization with Machine Learning Webinar, Part 1
Combining Optimization with Machine Learning for Better Decisions.
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Enabling Client-Server Optimization Applications With the New Gurobi Compute Server
This one hour video seminar explains how you can enable Client-Server optimization applications with the new Gurobi Compute Server.
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Non-Convex Quadratic Optimization
This video shows one of the major new feature in Gurobi 9.0, the new bilinear solver, which allows users to solve problems with non-convex quadratic objectives and constraints such as QPs, QCPs, MIQPs, and MIQCPs.
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Intro to Performance Tuning for Optimization Systems with Gurobi
Speed is key for most users that embed Gurobi into their own application infrastructure. Input data is transformed into high quality planning solutions and results need to be delivered in a timely manner as part of a robust and reliable system architecture.
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Combining Optimization with Machine Learning Webinar, Part 2
Combining Optimization with Machine Learning for Better Decisions
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Getting Started with Gurobi, Part 1 of 3
An introduction to math programming and building a model to use with Gurobi.
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Products of Variables in Mixed Integer Programming
Products of problem variables appear naturally in quadratic programs. Special preprocessing, linearization and cutting plane techniques are available to deal with such products. If at least one of the two variables in a product is binary, then the product can be modeled using a set of linear constraints. As a consequence, there are many mixed integer linear programs (MILPs) that actually contain products of variables hidden in their constraint structure. Rediscovering these product relationships between the variables enables us to exploit the solving techniques for product terms.
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Solving Simple Stochastic Optimization Problems with Gurobi
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.
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