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Non-Convex Quadratic Optimization

With the release of Gurobi 9.0’s addition of a new bilinear solver, the Gurobi Optimizer now supports non-convex quadratic optimization. This groundbreaking new capability allows users to solve problems with non-convex quadratic constraints and objectives – enabling them to find globally optimal solutions to classic bilinear pooling and blending problems and continuous manufacturing problems.

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New Directions for Optimization

In this video, learn about the motivation for some of the recent features added to the Gurobi Optimizer as well as recent developments in the field and how they are influencing our thinking on potential future directions.

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Using the Automatic Parameter Tuning Tool

This 17 minute video seminar explains the importance of parameters and how to use the Automatic Parameter Tuning Tool to help you maximize Gurobi’s performance.

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Integer Linear Programming in Computational and Systems Biology

Watch this video to learn about a fascinating, non-traditional use of integer linear programming in Computational and Systems Biology.

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How Optimization Modeling Creates Value for an Organization

Learn how to better showcase the value of optimization in your organization.

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Models with Products of Binary Variables

Watch this webinar to learn about models with products of binary variables.

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Ask the Experts: Technician Routing & Scheduling Problem

In this session, we will share the latest Jupyter Notebook Modeling Example featuring a Technician Routing & Scheduling Demo. We will showcase a mixed-integer programming model to simultaneously optimize the technician routing and scheduling (TRS) decisions at a telecommunications firm. Example scenarios and solution visualization will be demonstrated.

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Better Solutions Through Effective Parameter Setting

One hour webinar, given by Gurobi CTO and Co-founder, Dr. Zonghao Gu

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COVID-19 Hospital Capacity Management using Mathematical Models

In this webinar Assistant Professor Kimia Ghobadi, Department of Civil and Systems Engineering at Johns Hopkins University, discusses the mathematical models her team developed to match COVID-19 demand with available resources in a network of hospitals through patient transfer.

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