## 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

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Page 4

If there is no constraint on the forcing term ( U = R ' ) , this is a particular case of the linear quadratic regulator problem considered in

If there is no constraint on the forcing term ( U = R ' ) , this is a particular case of the linear quadratic regulator problem considered in

**Example**2.3 . If U = ( -a , a ] with a < oo , it is an**example**of a linear regulator problem ...Page 29

Remark 7.2 will be used in the next

Remark 7.2 will be used in the next

**example**, in which is the interval ( 0,00 ) .**Example**7.3 . This**example**and**Example**7.4 concern the consumption and investment behavior of a single agent . In the simplest version of the model ...Page 31

**Example**7.4 . Continuing**Example**7.3 , let us now suppose that the agent has access to two assets . Each asset has a fixed rate of return , with respective interest rates a and A ( 0 < a < A ) . At each times , the agent has to adjust ...### 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