In this webinar, attendees get a look at Gurobi 10.0. We summarize the performance improvements and highlight some of the underlying algorithmic advances, such as the network simplex algorithm, enhancements in concurrent LP, and optimization based bound tightening.
Moreover, attendees will learn about new features in Gurobi 10.0 like dashboards for the Cluster Manager, flexible deployment via new capabilities of our Web License Service (WLS), as well as a greatly improved matrix-friendly API for gurobipy.
Finally, we briefly introduce a number of open-source Python GitHub repositories that make it even easier to employ Gurobi. These allow the user to incorporate trained machine learning models into MIP model formulations, to simplify working with pandas data, or to analyze and to find the root cause of a numerical issue in an LP or MIP.
A PDF of the webinar slides can be found here.
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