## 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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Results 1-3 of 52

Page 203

Problems with affine -

Problems with affine -

**convex**structure . Let us now mention a spe cial class of problems for which V ( t , ' ) is a**convex**function on R " . Convexity of V ( t , ' ) is equivalent to A2V > 0. One then has a bound for 14 V , which will ...Page 229

We say that $ E C ( G ) is semiconvex if for every bounded subset B of G there is a constant K ( B ) > 0 such that , B ( X ) = 4 ( X ) + K ( B ) | X | 2 is

We say that $ E C ( G ) is semiconvex if for every bounded subset B of G there is a constant K ( B ) > 0 such that , B ( X ) = 4 ( X ) + K ( B ) | X | 2 is

**convex**on every**convex**subset of B. We say that y E C ( G ) is semiconcave if -2 ...Page 326

The Verification Theorem holds for any domain O which is not necessarily

The Verification Theorem holds for any domain O which is not necessarily

**convex**. Indeed let Ā , be the set of all ( ( - ) , û ( . ) ) E Â , satisfying z ( t ) + 1 [ 2 ( t + ) – z ( t ) ] E O for allt stede ( 0 , 1 ) .### 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