WEBINAR / EVENT
Webinar: Matrix-friendly Modeling with Gurobipy
Starting with Gurobi version 10.0, gurobipy makes it very easy to build optimization models that are naturally formulated with multi-dimensional constraints.
December 18 2022
WEBINAR / EVENT
Webinar: Matrix-friendly Modeling with Gurobipy
Starting with Gurobi version 10.0, gurobipy makes it very easy to build optimization models that are naturally formulated with multi-dimensional constraints.
December 18 2022
WEBINAR / EVENT
Webinar: Matrix-friendly Modeling with Gurobipy
Starting with Gurobi version 10.0, gurobipy makes it very easy to build optimization models that are naturally formulated with multi-dimensional constraints.
December 18 2022
Event Recap
Starting with Gurobi version 10.0, gurobipy makes it very easy to build optimization models that are naturally formulated with multi-dimensional constraints. In particular you can use familiar concepts from NumPy like dimensions, shape, vectorization and broadcasting in combination with gurobipy’s matrix-friendly objects to construct your optimization model. In this webinar we will walk you through this new functionality, discuss performance aspects, and present best practice code patterns.
Access the Jupyter Notebook Modeling Example
How to run Jupyter Notebook Modeling Example
Save webinar_matrixfriendly_final.ipynb from this email then open it in Jupyter Notebook
To run the example the first time, choose “Runtime” and then click “Run all”.
All the cells in the Jupyter Notebook will be executed. This may take some time because there are several timing runs in the notebook.
The example will install the gurobipy package, which includes a limited Gurobi license that allows you to solve small models.
You can also modify and re-run individual cells.
For subsequent runs, choose “Runtime” and click “on “Restart and run all”.
Feel free to explore all of the examples Robert shared.
*If you are new to Jupyter Notebook, Install Jupyter Notebook and familiarize yourself with the documentation.
Event Recap
Starting with Gurobi version 10.0, gurobipy makes it very easy to build optimization models that are naturally formulated with multi-dimensional constraints. In particular you can use familiar concepts from NumPy like dimensions, shape, vectorization and broadcasting in combination with gurobipy’s matrix-friendly objects to construct your optimization model. In this webinar we will walk you through this new functionality, discuss performance aspects, and present best practice code patterns.
Access the Jupyter Notebook Modeling Example
How to run Jupyter Notebook Modeling Example
Save webinar_matrixfriendly_final.ipynb from this email then open it in Jupyter Notebook
To run the example the first time, choose “Runtime” and then click “Run all”.
All the cells in the Jupyter Notebook will be executed. This may take some time because there are several timing runs in the notebook.
The example will install the gurobipy package, which includes a limited Gurobi license that allows you to solve small models.
You can also modify and re-run individual cells.
For subsequent runs, choose “Runtime” and click “on “Restart and run all”.
Feel free to explore all of the examples Robert shared.
*If you are new to Jupyter Notebook, Install Jupyter Notebook and familiarize yourself with the documentation.
Event Recap
Starting with Gurobi version 10.0, gurobipy makes it very easy to build optimization models that are naturally formulated with multi-dimensional constraints. In particular you can use familiar concepts from NumPy like dimensions, shape, vectorization and broadcasting in combination with gurobipy’s matrix-friendly objects to construct your optimization model. In this webinar we will walk you through this new functionality, discuss performance aspects, and present best practice code patterns.
Access the Jupyter Notebook Modeling Example
How to run Jupyter Notebook Modeling Example
Save webinar_matrixfriendly_final.ipynb from this email then open it in Jupyter Notebook
To run the example the first time, choose “Runtime” and then click “Run all”.
All the cells in the Jupyter Notebook will be executed. This may take some time because there are several timing runs in the notebook.
The example will install the gurobipy package, which includes a limited Gurobi license that allows you to solve small models.
You can also modify and re-run individual cells.
For subsequent runs, choose “Runtime” and click “on “Restart and run all”.
Feel free to explore all of the examples Robert shared.
*If you are new to Jupyter Notebook, Install Jupyter Notebook and familiarize yourself with the documentation.
Speakers
Meet Your Expert Speaker
Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.
Robert Luce
Senior Director of Optimizer R&D
Dr. Luce is an experienced researcher in applied mathematics, and author of numerous publications in the fields of numerical linear algebra and optimization. He holds a Ph.D. from Technical University of Berlin.
Alison Cozad
Optimization Support Manager
Dr. Alison Cozad holds a Ph.D. in Chemical Engineering from Carnegie Mellon University where she leveraged mixed-integer and semi-infinite optimization methods to improve machine learning algorithms. Prior to joining Gurobi, she held multiple roles at ExxonMobil, including as a Senior Data Science Lead and Real-time Optimization Engineer.
In her free time, Alison loves making things from CNC woodworking to electronics to cheese making to sock puppetry.
Maliheh Aramon
Senior Optimization Engineer
Maliheh received her PhD in Operations Research from the University of Toronto in 2014. During her PhD, she studied the interdependency between long-term and short-term optimization decisions in the context of maintenance and scheduling problems.
Prior to joining Gurobi, she worked for 1QB Information Technologies (1QBit) as an Optimization Research Lead. Her work focused on developing algorithms and tools that enable organizations to leverage both quantum and classical hardware efficiently to solve real-world problems in the fields of life sciences, energy, and finance.
Maliheh is a keen reader and enjoys reading novels. She also enjoys hiking in the beautiful Vancouver mountains.
Speakers
Meet Your Expert Speaker
Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.
Robert Luce
Senior Director of Optimizer R&D
Dr. Luce is an experienced researcher in applied mathematics, and author of numerous publications in the fields of numerical linear algebra and optimization. He holds a Ph.D. from Technical University of Berlin.
Alison Cozad
Optimization Support Manager
Dr. Alison Cozad holds a Ph.D. in Chemical Engineering from Carnegie Mellon University where she leveraged mixed-integer and semi-infinite optimization methods to improve machine learning algorithms. Prior to joining Gurobi, she held multiple roles at ExxonMobil, including as a Senior Data Science Lead and Real-time Optimization Engineer.
In her free time, Alison loves making things from CNC woodworking to electronics to cheese making to sock puppetry.
Maliheh Aramon
Senior Optimization Engineer
Maliheh received her PhD in Operations Research from the University of Toronto in 2014. During her PhD, she studied the interdependency between long-term and short-term optimization decisions in the context of maintenance and scheduling problems.
Prior to joining Gurobi, she worked for 1QB Information Technologies (1QBit) as an Optimization Research Lead. Her work focused on developing algorithms and tools that enable organizations to leverage both quantum and classical hardware efficiently to solve real-world problems in the fields of life sciences, energy, and finance.
Maliheh is a keen reader and enjoys reading novels. She also enjoys hiking in the beautiful Vancouver mountains.
Speakers
Meet Your Expert Speaker
Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.
Senior Director of Optimizer R&D
Robert Luce
Dr. Luce is an experienced researcher in applied mathematics, and author of numerous publications in the fields of numerical linear algebra and optimization. He holds a Ph.D. from Technical University of Berlin.
Optimization Support Manager
Alison Cozad
Dr. Alison Cozad holds a Ph.D. in Chemical Engineering from Carnegie Mellon University where she leveraged mixed-integer and semi-infinite optimization methods to improve machine learning algorithms. Prior to joining Gurobi, she held multiple roles at ExxonMobil, including as a Senior Data Science Lead and Real-time Optimization Engineer.
In her free time, Alison loves making things from CNC woodworking to electronics to cheese making to sock puppetry.
Senior Optimization Engineer
Maliheh Aramon
Maliheh received her PhD in Operations Research from the University of Toronto in 2014. During her PhD, she studied the interdependency between long-term and short-term optimization decisions in the context of maintenance and scheduling problems.
Prior to joining Gurobi, she worked for 1QB Information Technologies (1QBit) as an Optimization Research Lead. Her work focused on developing algorithms and tools that enable organizations to leverage both quantum and classical hardware efficiently to solve real-world problems in the fields of life sciences, energy, and finance.
Maliheh is a keen reader and enjoys reading novels. She also enjoys hiking in the beautiful Vancouver mountains.
