## Controlled Markov Processes and Viscosity SolutionsThis book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It covers dynamic programming for deterministic optimal control problems, as well as to the corresponding theory of viscosity solutions. New chapters in this second edition introduce the role of stochastic optimal control in portfolio optimization and in pricing derivatives in incomplete markets and two-controller, zero-sum differential games. |

### From inside the book

Results 1-5 of 89

Page

83 II.9 Uniqueness:

83 II.9 Uniqueness:

**first**order case . . . . . . . . . . . . . . . . . . . . . . . . . . 89 II.10 Continuity of the value function . . . . . . . . . . . . . . . . . . . . . . 99 viii III IV V II.11 Discounted cost with infinite horizon. Page

111 II.14 Uniqueness:

111 II.14 Uniqueness:

**first**-order case . . . . . . . . . . . . . . . . . . . . . . . . . . 114 II.15 Pontryagin's maximum principle (continued) . . . . . . . . . . . 115 II.16 Historical remarks . Page

Risk-sensitive stochastic control has been another active research area since the

Risk-sensitive stochastic control has been another active research area since the

**First**Edition of this book appeared. Chapter VI of the**First**Edition has been completely rewritten, to emphasize the relationships between logarithmic ... Page 2

The value function V for a deterministic optimal control problem satisfies, at least formally, a

The value function V for a deterministic optimal control problem satisfies, at least formally, a

**first**order nonlinear partial differential equation. See (5.3) or (7.10) below. In fact, the value function V often does not have the ... Page 5

Let us

Let us

**first**formulate the state dynamics for the control problem. Let Q0 = [t0,t1) × IRn and Q0 = [t0,t1] × IRn, the closure of Q0. Let U be a closed subset of m-dimensional IRm. We call U the control space.### What people are saying - Write a review

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### Contents

1 | |

Viscosity Solutions | 57 |

Differential Games | 375 |

A Duality Relationships 397 | 396 |

References | 409 |

### Other editions - View all

Controlled Markov Processes and Viscosity Solutions Wendell H. Fleming,Halil Mete Soner No preview available - 2006 |

### Common terms and phrases

admissible control assume assumptions boundary condition boundary data bounded brownian motion calculus of variations Chapter classical solution consider constant controlled Markov diffusion convergence convex Corollary cost function deﬁne deﬁnition denote differential games dynamic programming equation dynamic programming principle Dynkin formula Example exists exit ﬁnite ﬁrst formulation G Q0 Hamilton-Jacobi equation Hence HJB equation holds implies inequality initial data Ishii Lemma linear Lipschitz continuous Markov chain Markov control policy Markov processes maximum principle minimizing Moreover nonlinear obtain optimal control optimal control problem partial derivatives partial differential equation progressively measurable proof of Theorem prove reference probability system Remark result risk sensitive satisﬁes satisfying Section semigroup Soner stochastic control stochastic control problem stochastic differential equations subset Suppose Theorem 9.1 uniformly continuous unique value function Veriﬁcation Theorem viscosity solution viscosity subsolution viscosity supersolution