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Marrying Stochastic Control and Machine Learning: from Bermudan Options to Natural Gas Storage and Microgrids


Sep 17, 2018 - 1:50pm to 2:55pm


Rettaliata Engineering Center, Room 104


Mike Ludkovski
Department of Statistics and Applied Probability, University of California at Santa Barbara


Simulation-based strategies bring the machine learning toolbox to numerical resolution of stochastic control models. I will begin by reviewing the history of this idea, starting with the seminal work by Longstaff-Schwartz and through the popularized Regression Monte Carlo framework. I will then describe the  Dynamic Emulation Algorithm (DEA) that we developed, which unifies the different existing approaches in a single modular template and emphasizes the two central aspects of regression architecture and experimental design. Among novel DEA implementations, I will discuss Gaussian process regression, as well as alternative simulation designs (space-filling, sequential, adaptive, batched). The DEA template is illustrated with multiple examples drawing from Bermudan option pricing, natural gas storage valuation, and optimal control of back-up generator in a power microgrid. This is partly joint work with Aditya Maheshwari (UCSB).

Event Type: 

Department of Applied Mathematics - Colloquia