Parallel and Distributed Optimization with Gurobi
This 30 minute webinar introduces Gurobi’s capabilities relating to parallel and distributed optimization.
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Why Gurobi?
Why should you make Gurobi your solver of choice? Learn more about our company and what we offer.
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[Podcast] Data Science Based Decisions: Mixed-Integer Programming
Learn how machine learning and optimization can complement each other; the former making predictions about likely future business outcomes, and the latter suggesting appropriate actions to take in order to take advantage of these outcomes.
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Integrating Machine Learning with Mathematical Optimization: Resource Matching
Large professional services companies employ thousands of experts to deliver a wide variety of services, making labor the industry’s highest expense. Current manual processes and tools used within labor resources management present many limitations leading to poor demand fulfillment, low labor resource utilization, high project delivery costs, and poor customer satisfaction.
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Optimizing to Make Better Conservation Decisions
In this webinar, Dr. Jeffrey Hanson, Postdoctoral Scientist, Carleton University, will start with an introduction to systematic conservation planning, explain some of the key concepts and mathematical problems formulations for generating conservation plans, and highlight case studies from the literature. Dr. Richard Schuster, Director of Social Planning and Innovation, Nature Conservancy of Canada, will then compare exact algorithm solvers with conventional software for conservation planning, and talk about some real-world examples where exact algorithms solvers -- such as Gurobi -- have been used to help guide conservation decisions. By using exact algorithm solvers, conservation practitioners can quickly identify cost-effective conservation plans to inform decision making.
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Adding Optimization to Your Data Science Analytics Toolbox
This video presents real-world examples of Machine Learning and optimization in action, illustrating the value it can bring to your organization. It also provides you with next steps on how to get started with optimization as well as available resources.
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Developing and Deploying Optimization Applications with AMPL
Algebraic modeling languages like AMPL can be a quick and effective way to build optimization applications.
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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.
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Python I: Introduction to Modeling with Python
Python is a powerful and well-supported programming language that’s also a good choice for mathematical modeling. It has special features that make it easy to build and maintain optimization models.
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Python II: Advanced Algebraic Modeling with Python and Gurobi
The Gurobi Python interface combines the ease and expressiveness of a modeling language with the power and flexibility of a programming language. This part two of a three-part series builds on the ideas presented in our Python I webinar.
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