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.
You can download the slides presented in this webinar here.