This page contains videos that will help you get up to speed on, and get the most from, the Gurobi Optimizer. Click on the links below to learn more about each video.
|Adopting Optimization in Your Organization|
|Building the Business Case For Optimization ➤|
|Videos Introducing Gurobi|
|Tour of the Gurobi Optimizer ➤||Release overview: 7.0 ➤|
|Getting Started with Gurobi ➤||Release overview: 6.5 ➤|
|Gurobi Compute Server ➤||Release overview: 6.0 ➤|
|Videos on Modeling with Gurobi|
|Python I: Introduction to modeling with Python ➤||Python II: Advanced Algebraic Modeling with Python ➤|
|Using Gurobi and Anaconda to build models and python applications ➤||Modeling with the Gurobi Python Interface ➤|
|Introduction to modeling using Python and Gurobi ➤||The Gurobi Interactive Shell ➤|
|Switching to the Gurobi Solver ➤||Solving Quadratically-constrained Models ➤|
|Videos on Tuning Gurobi's Performance|
|Improving the Performance of the Gurobi Optimizer ➤||Using the Automatic Parameter Tuning Tool ➤|
|Finding better solutions in less time through effective parameter setting ➤|
|Videos on Parallel and Distributed Optimization|
|Parallel and Distributed Optimization with the Gurobi Optimizer ➤|
|Gurobi Partner Videos|
|Opalytics Cloud Platform ➤|
|Developing and Deploying Optimization Applications with AMPL ➤|
|Migrating from Excel-based planning tools to enterprise-ready optimization models and applicataions with ORConomy and Optano ➤|
In this 50 minute webinar you'll learn how successful companies are using optimization and persuasive support points to help build managerial and sernior leadership buy-in for optimization. In addition, you'll learn different ways in which optimization can drive value and actionable steps to help set up your optimzation projects for success.
This 50 minute video seminar takes you through a tour of the Gurobi Optimizer. The tour explores the interfaces that can be used to access the Gurobi Optimizer. See examples of how to work with the Gurobi matrix-based and object-oriented APIs, and learn how to use Gurobi through well-known modeling languages.
We've partnered with Abrèmod, LLC, to create a webinar geared toward newer users. The webinar topics include:
The webinar is available in three parts:
This one hour video seminar explains how you can enable client-server optimization applications with the new Gurobi Compute Server. This webinar provides an overview of the benefits of using a client-server architecture, how the Compute Server can be used, server installation and setup, using the client, and solving on the Gurobi Cloud.
This video covers the significant performance improvements, API enhancements, and new features users have asked for. The topics include significant performance enhancements across a range of models, support for multiple objectives with flexibility in how they are prioritized, Python modeling enhancements that simplify the translation of mathematical models into efficient implementations, new general constraints where you can enter commonly occurring constraints without having to translate them into linear constraints, a new intuitive and enhanced Gurobi Instant Cloud API that simplifies instance launching and integration of the Gurobi Instant Cloud into applications, and a number of additional enhancements. This video is also available in Deutsch, Español and 中文.
This video covers the significant performance improvements on MIP, LP, MIQP and MIQCP models, our new Instant Cloud service and our new interactive example models. In addition, this webinar reviews several useful new features including: Gurobi Recording which is useful to help you debug your program, Variable Hints which is a more flexible version of Warm Start, a simplification to our API that helps avoid most calls to the Update command, as well as a number of other features ranging from more precise control over Gurobi environment creation and desstruction, single-use license simplification, token server password protection, enhanced logging for distributed MIP and R interface enhancements, among others improvements. This video is also available in Deutsch and 中文.
This video covers the significant performance improvements on MIP, LP, MIQP and MIQCP models, and our new distributed MIP solver that allows you to use multiple machines to reduce solve times on difficult models. In addition, it covers our enhanced support for models with separable piecewise-linear objective functions, including an API for specifying these functions directly and a new simplex algorithm that solves these models faster. Lastly, it covers additional enhancements including a new distributed concurrent LP solver, support for models with more than two billion non-zeros, the ability to mark existing constraints as lazy, and more. This video is also available in Deutsch and 中文.
This 55 minute video, part one of what will be a three-part series, presents an introduction to using Python, Gurobi and Jupyter Notebooks. It covers the basics of model-building, including working with decision variables, constraints, objective function, sums and for-all loops.
This one-hour video, part two of what will be a three-part series, covers more advanced topics including data structures and loops, sum and for-all expressions, working with large data sets and building large-scale, high-performance applications using the Gurobi Python interface.
This 60 minute video presentation provides an overview of using Gurobi and Anaconda together to build models and python applications. It includes an overview of the Spyder IDE, Jupyter notebooks, using Pandas to manage data and Bokeh to visualize results.
This 75 minute video presentation demonstrates the capabilities of the Gurobi Interactive Shell. Working with the Gurobi Interactive Shell is the quickest way to get started with the Gurobi Optimizer. Learn all the basics of using the interactive shell: how to load and modify models, run the optimization algorithms, and much more.
This 50 minute webinar provides: a) an introduction to Python and Gurobi, as well as an introduction to using Jupyter notebooks, b) a solid modeling foundation with an overview of model building steps, solving models and analyzing solutions, and c) an interactive development process using actual models as examples.
The Gurobi Python interface allows you to build concise and efficient optimization models using high-level modeling constructs. This 50 minute video tutorial provides an overview of these capabilities, including detailed examples that show how to use the Python interface to build models that can be turned into full optimization applications.
In this one-hour webinar, co-presented by Gurobi and Abremod, we'll review:
These slides explain how to take software written for another optimization solver and convert it to use Gurobi Optimizer. This covers nearly every scenario: model files, modeling systems, matrix interfaces and object-oriented interfaces.
Our Gurobi 5.0 release added the ability to solve quadratically-constrained models (QCP and MIQCP). 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.
This 73 minute video seminar explains how to tune the performance of the Gurobi Optimizer. There are different factors that can contribute to slow performance in solving an optimization model; learn how to recognize the different performance bottlenecks and see some techniques to cope with each situation.
This 17 minute video seminar explains the impact parameters can have on performance, tips on how to think about using parameters to improve performance, and how to use the Automatic Parameter Tuning tool included free with Gurobi to improve performance.
In this one-hour webinar, given by Gurobi CTO and Co-founder, Dr. Zonghao Gu, we'll be talking about parameters for Gurobi optimizers and discussing how to find good parameter settings that improve performance and robustness to solve hard optimization problems.
This webinar introduces Gurobi's capabilities relating to parallel and distributed optimization, provides insight into when distributed optimization is useful and also provides a performance comparison both between using parallel and distribution optimization and between using fewer machines with more cores and more machines with fewer cores. This video is also available in Deutsch.
Developing optimization solutions often requires rapid deployment to users in order to take full advantage of the capabilities. Opalytics has developed a cloud platform that provides an easy-to-use user interface and powerful deployment capabilities for optimization models. It also provides a schema wrapper for Gurobi using Python APIs that makes it easy to deploy on the platform and therefore enables fast end-to-end custom optimization development. Dr. David Simchi-Levi will show one example - Network Risk Optimization based on work he did with Ford, that won the 2014 Daniel H. Wagner Prize for Excellence in Operations Research Practice.
Algebraic modeling languages like AMPL can be a quick and effective way to build optimization applications. In this one-hour webinar you'll learn: 1) how AMPL helps you to streamline the cycle of model formulation and testing, 2) how a simple AMPL model evolves to address more complex and realistic concerns, and 3) how the AMPL system helps you embed optimization into larger applications for deployment to users.
Excel is still the most widely used tool for strategic, tactical and operational purposes in companies around the world. However, many companies run into situations where they want better decisions with less work and in less time, but don’t necessarily have the internal OR expertise to build their own enterprise-ready optimization applications. This webinar provides tips on when to migrate, how to do so successfully, and on managing optimization projects.