Dynamic stochastic matching problems arise in a variety of recent applications, ranging from ridesharing and online video games to kidney exchange. Such problems are naturally formulated as Markov ...

We propose dual decomposition and solution schemes for multistage CVaR-constrained problems. These schemes meet the need for handling multiple CVaR-constraints for different time frames and at ...

This course covers reinforcement learning aka dynamic programming, which is a modeling principle capturing dynamic environments and stochastic nature of events. The main goal is to learn dynamic ...

Decision making in stochastic and dynamic environments plays an essential role in many areas, including finance, robotics, game theory, revenue management and social networks. This course aims to gain ...

The Annals of Applied Probability, Vol. 28, No. 1 (February 2018), pp. 1-34 (34 pages) In this paper, we aim to develop the stochastic control theory of branching diffusion processes where both the ...

Water resource systems face uncertainties from climate variability, demand fluctuations and policy shifts. Stochastic optimisation offers mathematical frameworks to plan reservoir operations, ...