## 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 46

Page 198

**Similarly**, the generalized partial derivatives V , Vzie ; ( if they exist ) are defined as those functions in Loc ( Qo ) such that , for all ECO ( Qo ) , ( 10.2 ) Sol V drdt = - Vdxdt , -Sol Ete ( 10.3 ) . Vasa dædt үФzit , ; dordt .Page 235

**Similarly**Âm is uniformly bounded from above and since Am < Âm both Am and Âm are uniformly bounded . Also , any semiconvex or semiconcave function is locally Lipschitz continuous in the interior of its domain .Page 377

**Similarly**, V. is a viscosity supersolution . We assume that ( 3.1 ) and IV ( 2.2 ) hold . Let V ( t , x ) be the value function for the controlled diffusion process , as in IV ( 2.10 ) . We also make the following assumption , which ...### 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