Matrix-friendly Modeling with Gurobipy

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.

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