stochastic control

Friday, June 15, 2018 - 9:00am - 9:30am
Ruihua Liu (University of Dayton)
In this talk we present some results on optimal asset allocation with stochastic interest rates in regime-switching models. A class of stochastic optimal control problems with Markovian regime-switching is formulated for which a verification theorem is provided. The theory is applied to solve two portfolio optimization problems (a portfolio of stock and savings account and a portfolio of mixed stock, bond and savings account) while a regime-switching Vasicek model is assumed for the interest rate.
Wednesday, May 9, 2018 - 4:00pm - 4:30pm
Jiequn Han (Princeton University)
Developing algorithms for solving high-dimensional stochastic control problems and high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notorious difficulty known as the curse of dimensionality. In the first part of this talk, we develop a deep learning-based approach that directly solves high-dimensional stochastic control problems based on Monte-Carlo sampling.
Wednesday, May 16, 2018 - 10:00am - 10:50am
Martin Reiman (Columbia University)
The assemble-to-order (ATO) system is a classical model in inventory theory, where multiple components are used to produce multiple products. All components are obtained from an uncapacitated supplier after a (component dependent) deterministic lead time, while demand for the products forms a compound Poisson process. Assembly is assumed to be performed instantaneously, so all inventory is held as components rather than finished products. Demand that is not met immediately is backlogged.
Friday, May 11, 2018 - 11:00am - 11:50am
Peter Caines (McGill University)
This work introduces Graphon Mean Field Game (GMFG) theory for the analysis of
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