## Controlled Markov Processes and Viscosity SolutionsThis book is intended as an introduction to optimal stochastic control for continuous time Markov processes and to the theory of viscosity solutions. |

### From inside the book

Results 1-3 of 69

Page vii

A rather elementary introduction to dynamic programming for controlled Markov processes is provided in

A rather elementary introduction to dynamic programming for controlled Markov processes is provided in

**Chapter**III . This is followed by the more technical**Chapters**IV and V , which are concerned with controlled Markov diffusions and ...Page 55

This unique characterization of the value function is especially important in the analysis of related singular perturbation problems (

This unique characterization of the value function is especially important in the analysis of related singular perturbation problems (

**Chapter**VII ) and in the numerical analysis of the control problems (**Chapter**IX ) .Page 213

V Viscosity Solutions : Second - Order Case V.1 Introduction In this

V Viscosity Solutions : Second - Order Case V.1 Introduction In this

**chapter**we study the exit time control of a Markov diffusion process as formulated in Section IV.2 . With the exception of the last section , we assume that the state ...### What people are saying - Write a review

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### Contents

Viscosity Solutions | 53 |

Controlled Markov Diffusions in R | 157 |

SecondOrder Case | 213 |

Copyright | |

7 other sections not shown

### Other editions - View all

Controlled Markov Processes and Viscosity Solutions Wendell H. Fleming,Halil Mete Soner Limited preview - 2006 |

Controlled Markov Processes and Viscosity Solutions Wendell H. Fleming,Halil Mete Soner No preview available - 2006 |

### Common terms and phrases

admissible apply approximation assume assumptions boundary condition bounded calculus called Chapter compact condition consider constant continuous control problem convergence convex Corollary corresponding cost defined definition denote depend derivatives deterministic difference discussion dynamic programming equation equivalent estimate Example exists exit fact finite fixed formula given gives Hence holds horizon implies inequality lateral Lemma limit linear Lipschitz Markov Markov diffusion Markov processes maximum measurable method minimizing Moreover nonlinear obtain operator optimal control partial differential equation particular positive principle probability proof prove Recall reference Remark replaced require respectively result satisfies Section Similarly smooth space step stochastic control stochastic differential equation subset sufficiently suitable supersolution Suppose term terminal Theorem 5.1 theory tion uniformly unique value function Verification viscosity solution viscosity subsolution yields