## 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. |

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Page 61

Note that , if w is any test function , then w ( t , x ) is defined for all ( t , x ) € [ to , tı ] even though ( 3.11 ) is

Note that , if w is any test function , then w ( t , x ) is defined for all ( t , x ) € [ to , tı ] even though ( 3.11 ) is

**required**to hold only for ( t , x ) E Q. Like the choice of C , the exact choice of D is not important .Page 100

VEU - W. First we give a definition of viscosity sub- and supersolutions of ( 11.2 ) in 0. This definition is obtained by simply substituting the form ( 11.1 ) into Definition 4.2 . The following definition does not

VEU - W. First we give a definition of viscosity sub- and supersolutions of ( 11.2 ) in 0. This definition is obtained by simply substituting the form ( 11.1 ) into Definition 4.2 . The following definition does not

**require**H to satisfy ...Page 103

So we make the following definitions , which

So we make the following definitions , which

**require**neither V nor the boundary ao to be differentiable . Definition 12.1 . We say that WE C ( Q ) is a viscosity supersolution of I ( 5.3 ...### 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