Dynamic programming algorithms are developed for optimal capital allocation subject to budget constraints. We extend the work of Weingartner [17] and Weingartner and Ness [19] by including multilevel ...

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 ...

This paper investigates conditions under which stochastic dynamic programs easily reduce to static deterministic programs. The conditions, though strict, are still rich enough to aid in the solution ...

Dynamic Programming and Optimal Control is offered within DMAVT and attracts in excess of 300 students per year from a wide variety of disciplines. It is an integral part of the Robotics, System and ...

Whether you're just starting out as a coder or want to advance your coding skills, tackling programming problems is part of the plan. Convenient, free, and even fun, coding problem websites challenge ...

Bio-inspired computational methods have gained popularity recently. These methods mimic the seemingly complex behavior of ...

This paper proposes a new deep-learning-based algorithm for high-dimensional Bermudan option pricing. To the best of our knowledge, this is the first study of the arbitrary-order discretization scheme ...