## 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 49

Page 20

1 1 1 1 1 1 1 1 Theorem ( [ EG ) or ( Zi ] ) every locally

1 1 1 1 1 1 1 1 Theorem ( [ EG ) or ( Zi ] ) every locally

**Lipschitz**function is differentiable at almost all points ( t , x ) E Q. Definition . W is a generalized solution to the dynamic programming equation in Q if W is locally ...Page 98

Then the value function V is

Then the value function V is

**Lipschitz**continuous on Q and satisfies ( 10.5 ) . The condition ( 10.6 ) implies I ( 3.11 ) . Therefore the two exit control prob lems defined in Section 1.3 ( classes B and B ' ) are equivalent ( see ...Page 393

Appendix C Extension of

Appendix C Extension of

**Lipschitz**Continuous Functions ; Smoothing In Sections VI.2 and VI.5 we used a result about**Lipschitz**continuous extensions of functions . Let K CRM and let g : K + Rm be**Lipschitz**continuous , with**Lipschitz**...### What people are saying - Write a review

We haven't found any reviews in the usual places.

### 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