Download Full eBooks in PDF
Optimal Feedback For Stochastic Linear Quadratic Control And Backward Stochastic Riccati Equations In Infinite Dimensions Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Optimal Feedback For Stochastic Linear Quadratic Control And Backward Stochastic Riccati Equations In Infinite Dimensions book. This book definitely worth reading, it is an incredibly well-written.
Author : Qi Lü,Xu Zhang
Publisher : American Mathematical Society
Page : 120 pages
File Size : 45,6 Mb
Release : 2024-03-18
Category : Mathematics
ISBN : 9781470468750
View the abstract.
Author : Jingrui Sun,Jiongmin Yong
Publisher : Springer Nature
Page : 129 pages
File Size : 42,6 Mb
Release : 2020-06-29
Category : Mathematics
ISBN : 9783030209223
This book gathers the most essential results, including recent ones, on linear-quadratic optimal control problems, which represent an important aspect of stochastic control. It presents the results in the context of finite and infinite horizon problems, and discusses a number of new and interesting issues. Further, it precisely identifies, for the first time, the interconnections between three well-known, relevant issues – the existence of optimal controls, solvability of the optimality system, and solvability of the associated Riccati equation. Although the content is largely self-contained, readers should have a basic grasp of linear algebra, functional analysis and stochastic ordinary differential equations. The book is mainly intended for senior undergraduate and graduate students majoring in applied mathematics who are interested in stochastic control theory. However, it will also appeal to researchers in other related areas, such as engineering, management, finance/economics and the social sciences.
Author : Qi Lü,Xu Zhang
Publisher : Springer Nature
Page : 592 pages
File Size : 44,9 Mb
Release : 2021-10-19
Category : Science
ISBN : 9783030823313
This is the first book to systematically present control theory for stochastic distributed parameter systems, a comparatively new branch of mathematical control theory. The new phenomena and difficulties arising in the study of controllability and optimal control problems for this type of system are explained in detail. Interestingly enough, one has to develop new mathematical tools to solve some problems in this field, such as the global Carleman estimate for stochastic partial differential equations and the stochastic transposition method for backward stochastic evolution equations. In a certain sense, the stochastic distributed parameter control system is the most general control system in the context of classical physics. Accordingly, studying this field may also yield valuable insights into quantum control systems. A basic grasp of functional analysis, partial differential equations, and control theory for deterministic systems is the only prerequisite for reading this book.
Author : Jingrui Sun,Jiongmin Yong
Publisher : Springer Nature
Page : 138 pages
File Size : 45,5 Mb
Release : 2020-06-29
Category : Mathematics
ISBN : 9783030483067
This book gathers the most essential results, including recent ones, on linear-quadratic optimal control problems, which represent an important aspect of stochastic control. It presents results for two-player differential games and mean-field optimal control problems in the context of finite and infinite horizon problems, and discusses a number of new and interesting issues. Further, the book identifies, for the first time, the interconnections between the existence of open-loop and closed-loop Nash equilibria, solvability of the optimality system, and solvability of the associated Riccati equation, and also explores the open-loop solvability of mean-filed linear-quadratic optimal control problems. Although the content is largely self-contained, readers should have a basic grasp of linear algebra, functional analysis and stochastic ordinary differential equations. The book is mainly intended for senior undergraduate and graduate students majoring in applied mathematics who are interested in stochastic control theory. However, it will also appeal to researchers in other related areas, such as engineering, management, finance/economics and the social sciences.
Author : Bao Gang,Coron Jean-michel,Li Ta-tsien
Publisher : World Scientific
Page : 264 pages
File Size : 53,9 Mb
Release : 2019-04-08
Category : Mathematics
ISBN : 9789813276161
This book is a collection of lecture notes for the LIASFMA Hangzhou Autumn School on 'Control and Inverse Problems for Partial Differential Equations' which was held during October 17-22, 2016 at Zhejiang University, Hangzhou, China. This autumn school is one of the activities organized by Sino-French International Associate Laboratory in Applied Mathematics (LIASFMA). Established jointly by eight institutions in China and France in 2014, LIASFMA aims at providing a platform for many leading French and Chinese mathematicians to conduct in-depth researches, extensive exchanges, and student training in broad areas of applied mathematics.The book provides the readers with a unique and valuable opportunity to learn from and communicate with leading experts in control and inverse problems. And the readers are exposed not only to the basic theories and methods but also to the forefront of research directions in both fields.
Author : Nicolas Burq,Laurent Thomann
Publisher : American Mathematical Society
Page : 102 pages
File Size : 48,5 Mb
Release : 2024-05-15
Category : Mathematics
ISBN : 9781470469795
View the abstract.
Author : Sonja Cox,Martin Hutzenthaler,Arnulf Jentzen
Publisher : American Mathematical Society
Page : 102 pages
File Size : 43,9 Mb
Release : 2024-05-15
Category : Mathematics
ISBN : 9781470467012
View the abstract.
Author : Eva Elduque,Christian Geske,Moisés Herradón Cueto,Laurenţiu G. Maxim,Botong Wang
Publisher : American Mathematical Society
Page : 128 pages
File Size : 48,9 Mb
Release : 2024-05-15
Category : Mathematics
ISBN : 9781470469672
View the abstract.
Author : Gaëtan Borot,Vincent Bouchard,Nitin K. Chidambaram,Thomas Creutzig,Dmitry Noshchenko
Publisher : American Mathematical Society
Page : 120 pages
File Size : 44,8 Mb
Release : 2024-05-15
Category : Mathematics
ISBN : 9781470469061
View the abstract.
Author : Steven Boyer,Cameron McA. Gordon,Xingru Zhang
Publisher : American Mathematical Society
Page : 136 pages
File Size : 48,6 Mb
Release : 2024-04-17
Category : Mathematics
ISBN : 9781470468705
View the abstract.
Author : Qi Chen,Dongyi Wei,Zhifei Zhang
Publisher : American Mathematical Society
Page : 190 pages
File Size : 53,9 Mb
Release : 2024-05-15
Category : Mathematics
ISBN : 9781470468958
View the abstract.
Author : Sam Chow,Niclas Technau
Publisher : American Mathematical Society
Page : 86 pages
File Size : 53,5 Mb
Release : 2024-05-15
Category : Mathematics
ISBN : 9781470468798
View the abstract.
Author : Maxwell Stolarski
Publisher : American Mathematical Society
Page : 160 pages
File Size : 48,6 Mb
Release : 2024-04-17
Category : Mathematics
ISBN : 9781470468767
View the abstract.
Author : Anonim
Publisher : Unknown
Page : 128 pages
File Size : 48,8 Mb
Release : 2024-07-02
Category : Electronic
ISBN : 9789814475808
Author : Jiongmin Yong,Xun Yu Zhou
Publisher : Springer Science & Business Media
Page : 472 pages
File Size : 53,7 Mb
Release : 1999-06-22
Category : Mathematics
ISBN : 0387987231
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.