Data-driven optimisation under uncertainty Part 2

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Data-driven optimisation under uncertainty Part 2

Following the series of talks by Edward Baker on Reinforcement Learning, we will review mathematical programming methods for problems with uncertain data. The goal is to highlight the possible connections between classical mathematical programming methods and recent deep learning techniques. The structure of this presentation will follow that of the paper "Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming by Ning & You, Computers & Chemical Engineering, 125, 434-448, 2019".

Presenter

  • Dr Alysson Costa
    Dr Alysson Costa, School of Mathematics and Statistics