Improving the Performance of the Gurobi Optimizer
This 73 minute video seminar explains how to tune the performance of the Gurobi Optimizer.
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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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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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[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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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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