【中文网络讲座】Gurobi 10.0 发布 -- 新亮点和技术创新
本次网络讲座中，Gurobi Optimization 公司的 CTO 和创始人顾宗浩博士将会介绍Gurobi 10.0 的新亮点和技术创新。讲座内容包括性能提升采用的新技术和新算法，例如网络单纯形算法，并发LP 求解算法的改进和基于优化的边界紧缩技术OBBT等。Gurobi 10.0将是第一个允许将用户的机器学习模型嵌入到数学规划模型中的商业求解器，极大括展了数据科学家调用运筹算法的能力，这部分功能在Github上开源提供。这部分内容也会在讲座中介绍，以及其他一些开源的效率工具，例如对 pandas 数据的建模支持等。
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ML and Optimization to determine best store network
BearingPoint has been using analytics to support its supply chain projects for many years. Optimisation is a key tool, especially for network optimisation projects, but typically would be combined with other analytical methods.
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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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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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Fireside Chat with Mike North, VP of Broadcast Planning & Scheduling for the NFL
In this fireside chat, Mike North, VP of Broadcast Planning and Scheduling at the NFL and Greg Glockner, VP of Engineering at Gurobi Optimization, discuss how they used mathematical optimization to create the 2019 schedule and some issues and constraints the NFL is facing with the 2020 schedule.
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Using MIP to Model Midstream Energy Assets
This webinar is centered around how East Daley Capital integrates Gurobi into their models. It will cover the business case from a high level, problem formulation as a Mixed Integer Program using Gurobi’s Python API, and the use of Gurobi features to assist in model development.
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Gurobi QCP and SOCP Optimizer Overview
This 50 minute video covers our new QCP and SOCP optimizer for solving quadratically-constrainted models with Gurobi. This new capability is built on top of an efficient Second-Order Cone Programming (SOCP) solver. This seminar will discuss the design choices we made in building this new optimizer, and the impact of these choices on overall performance and robustness.
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Avoiding Numerical Issues in Optimization Models
Models with numerical issues can lead to undesirable results: slow performance, wrong answers or inconsistent behavior.
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Where Data Meets Decisions: Part 1
Ever wonder how avocado pricing impacts buyers’ decisions? Or how you can discover lesser-known artists in your daily music playlists? Or how to assemble the ideal fantasy basketball team?
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