## 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 49

Page 3

We call tị the terminal time , h the running

We call tị the terminal time , h the running

**cost**, and ¥ the terminal**cost**. It is often assumed that h and 4 are convex functions , and that h ( 2 ) , 4 ( x ) have a unique minimum at x = 0.Page 129

In stochastic control theory , l is often called a running

In stochastic control theory , l is often called a running

**cost**function and y a terminal**cost**function . Formula ( 2.10 ) expresses ( t , x ) as a total expected**cost**over the time interval ( t , tı ) .Page 339

VIII.6 Portfolio selection with transaction

VIII.6 Portfolio selection with transaction

**costs**The purpose of this section is to formulate a portfolio selection ... However , this results in a transaction**cost**, which is linearly proportional in the size of the transaction .### 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 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