## 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 35

In general , if w ( s ) , P ( s ) are any solution to ( 8.8 ) , then x ( ) is called an extremal for the

In general , if w ( s ) , P ( s ) are any solution to ( 8.8 ) , then x ( ) is called an extremal for the

**calculus**of variations problem in the sense that it ...Page 51

When H is not convex in the p - variable , equation ( 10.8 ) is no longer related to a problem of

When H is not convex in the p - variable , equation ( 10.8 ) is no longer related to a problem of

**calculus**of variations . However , under some mild ...Page 205

IV.11 Stochastic

IV.11 Stochastic

**calculus**of variations In this section we consider a special class of problems , which are stochastic perturbations of certain problems in ...### 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 Rn | 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 zero