Gurobi Python Interface: Matrix-friendly Modeling Techniques

Webinar Summary

The recent release of Gurobi 9.0 includes a new set of API functions within the Gurobi Python interface (“gurobipy”) that support matrix-oriented modeling. These new API functions greatly improve and simplify the process of building optimization models using matrix and vector expressions. Users can now define linear and quadratic constraints directly from matrix representations such as Numpy ndarrays or Scipy sparse matrices as well as retrieve result data (such as solutions) directly as ndarrays.

In the webinar, we will:

• compare the traditional, term-based modeling API with this newly introduced matrix-friendly API,
• discuss the advantages of both approaches,
• show typical usage patterns
• and provide guidelines for achieving good modeling performance.

Presenter

Presenting this webinar is Dr. Robert Luce, Developer at Gurobi Optimization. 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.

Presented Materials

You can download the slides presented in this webinar here.