Stochastic Controls: Hamiltonian Systems and HJB Equations: 43 (Stochastic Modelling and Applied Probability, 43) - Softcover

Yong, Jiongmin; Zhou, Xun Yu

Softcover

ISBN 10: 1461271541 ISBN 13: 9781461271543

Publisher: Springer, 2012

Synopsis

As is well known, Pontryagin's maximum principle and Bellman's dynamic programming are the two principal and most commonly used approaches in solving stochastic optimal control problems. * An interesting phenomenon one can observe from the literature is that these two approaches have been developed separately and independently. Since both methods are used to investigate the same problems, a natural question one will ask is the fol­ lowing: (Q) What is the relationship betwccn the maximum principlc and dy­ namic programming in stochastic optimal controls? There did exist some researches (prior to the 1980s) on the relationship between these two. Nevertheless, the results usually werestated in heuristic terms and proved under rather restrictive assumptions, which were not satisfied in most cases. In the statement of a Pontryagin-type maximum principle there is an adjoint equation, which is an ordinary differential equation (ODE) in the (finite-dimensional) deterministic case and a stochastic differential equation (SDE) in the stochastic case. The system consisting of the adjoint equa­ tion, the original state equation, and the maximum condition is referred to as an (extended) Hamiltonian system. On the other hand, in Bellman's dynamic programming, there is a partial differential equation (PDE), of first order in the (finite-dimensional) deterministic case and of second or­ der in the stochastic case. This is known as a Hamilton-Jacobi-Bellman (HJB) equation.

About this title

About the Author

Jingrui Sun received his PhD in Mathematics from the University of Science and Technology of China in 2015. From 2015 to 2017, he was a Postdoctoral Fellow at the Hong Kong Polytechnic University and then a Research Fellow at the National University of Singapore. From 2017 to 2018, he was a Visiting Assistant Professor at the University of Central Florida, USA. Since the spring of 2019, he has been an Assistant Professor at the Southern University of Science and Technology, China. Dr. Sun has broad interests in the area of control theory and its applications. Aside from his primary research on stochastic optimal control and differential games, he is exploring forward and backward stochastic differential equations, stochastic analysis, and mathematical finance.

Jiongmin Yong received his PhD from Purdue University in 1986 and is currently a Professor of Mathematics at the University of Central Florida, USA. His main research interests include stochastic control, stochastic differential equations, and optimal control of partial differential equations. Professor Yong has co-authored the following influential books: "Stochastic Control: Hamiltonian Systems and HJB Equations" (with X. Y. Zhou, Springer 1999), "Forward-Backward Stochastic Differential Equations and Their Applications" (with J. Ma, Springer 1999), and "Optimal Control Theory for Infinite-Dimensional Systems" (with X. Li, Birkhauser 1995). His current interests include time-inconsistent stochastic control problems.

From the Back Cover

This monograph unifies the two key approaches in solving optimal control problems. The book will be of interest to researchers and graduate students in applied probability, control engineering, and econometrics.

Edition details

• Publisher: Springer
• Publication date: 2012
• Language: English
• ISBN 10: 1461271541
• ISBN 13: 9781461271543
• Binding: Paperback
• Number of pages: 439