This research monograph develops the Hamilton-Jacobi-Bellman theory via dynamic programming principle for a class of optimal control problems for stochastic hereditary differential equations (SHDEs) driven by a standard Brownian motion and with a bounded or an infinite but fading memory. These equations represent a class of stochastic infinite-dimensional systems that become increasingly important and have wide range of applications in physics, chemistry, biology, engineering and economics/finance. This monograph covers a very active research area. It can be used as a research reference for researchers and advanced graduate students who have special interest in optimal control theory and applications of stochastic hereditary systems.

Very active research area Chang bridges area of stochastic control and stochastic delay equations Includes supplementary material: sn.pub/extras

Klappentext

This research monograph develops the Hamilton-Jacobi-Bellman (HJB) theory through dynamic programming principle for a class of optimal control problems for stochastic hereditary differential systems. It is driven by a standard Brownian motion and with a bounded memory or an infinite but fading memory.

The optimal control problems treated in this book include optimal classical control and optimal stopping with a bounded memory and over finite time horizon.

This book can be used as an introduction for researchers and graduate students who have a special interest in learning and entering the research areas in stochastic control theory with memories. Each chapter contains a summary.

Mou-Hsiung Chang is a program manager at the Division of Mathematical Sciences for the U.S. Army Research Office.

Inhalt
and Summary.- Stochastic Hereditary Differential Equations.- Stochastic Calculus.- Optimal Classical Control.- Optimal Stopping.- Discrete Approximations.- Option Pricing.- Hereditary Portfolio Optimization.