Tuesday, April 20, 2021

Data-driven distributionally robust optimization with MOSEK

We posted a video where our very own Utkarsh Detha accompanied by the authors of the award-winning paper  "Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations", Assistant Prof. Peyman Mohajerin Esfahani and Prof. Daniel Kuhn, discuss the work presented in that paper.

We also show how to quickly and easily implement such an approach using our Fusion API for Python. This video focuses on the key new feature: parameters in Fusion. Parametric Fusion allows MOSEK to rapidly resolve a model, and combined with the warm-start capability of the Simplex optimizer, it becomes a powerful tool in every optimizer's garage.

See: the video, our notebook on the same topic, the research paper.