## 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 251

Lions

Lions

**proved**that any viscosity solution is the value function of the related stochastic optimal control problem . ... Jensen was first to**prove**a uniqueness result for a general second - order equation [ J ] .Page 281

VII Singular Perturbations VII.1 Introduction In Section II.6 , we

VII Singular Perturbations VII.1 Introduction In Section II.6 , we

**proved**that any uniform limit of a sequence of ... Now consider a situation in which we want to**prove**the convergence of a sequence of viscosity solutions Ve to the ...Page 403

Appendix E A Result of Alexandrov The purpose of this appendix is to

Appendix E A Result of Alexandrov The purpose of this appendix is to

**prove**that semiconvex functions are almost everywhere twice differentiable . This is a classical result due to Alexandrov ( A1 ) . Our discussion closely follows the ...### 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