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 46
Page
... stochastic differential equations 127 III.6 Controlled Markov processes 130 III.7 Dynamic programming : formal description 131 III.8 III.9 A Verification Theorem ; finite time horizon Infinite Time Horizon 134 139 III.10 Viscosity ...
... stochastic differential equations 127 III.6 Controlled Markov processes 130 III.7 Dynamic programming : formal description 131 III.8 III.9 A Verification Theorem ; finite time horizon Infinite Time Horizon 134 139 III.10 Viscosity ...
Page
... Differential game formulation . . .377 XI.4 Upper and lower value functions 381 XI.5 Dynamic programming principle ... Stochastic Differential Equations : Random Coefficients . . 403 References . 409 Index . 425 Preface to Second Edition ...
... Differential game formulation . . .377 XI.4 Upper and lower value functions 381 XI.5 Dynamic programming principle ... Stochastic Differential Equations : Random Coefficients . . 403 References . 409 Index . 425 Preface to Second Edition ...
Page
... stochastic control for con- tinuous time Markov processes and to the theory ... differential equation of second order, called a Hamilton – Jacobi – Bellman ... equations. Typically, the value function is not smooth enough to satisfy the ...
... stochastic control for con- tinuous time Markov processes and to the theory ... differential equation of second order, called a Hamilton – Jacobi – Bellman ... equations. Typically, the value function is not smooth enough to satisfy the ...
Page 2
... stochastic optimal control prob- lems. In dynamic programming, a value ... differential equation. See (5.3) or (7.10) below. In fact, the value ... equations can be interpreted as Hamilton-Jacobi equations, by using duality for convex ...
... stochastic optimal control prob- lems. In dynamic programming, a value ... differential equation. See (5.3) or (7.10) below. In fact, the value ... equations can be interpreted as Hamilton-Jacobi equations, by using duality for convex ...
Page 120
You have reached your viewing limit for this book.
You have reached your viewing limit for this book.
Contents
1 | |
Viscosity Solutions | 57 |
Classical Solutions119 | 118 |
Controlled Markov Diffusions in R | 151 |
SecondOrder Case | 199 |
Logarithmic Transformations and Risk Sensitivity | 227 |
Singular Perturbations 261 | 260 |
Singular Stochastic Control | 293 |
Finite Difference Numerical Approximations | 321 |
Differential Games | 375 |
A Duality Relationships 397 | 396 |
References | 409 |
Index 425 | 424 |
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 C₁ C¹(Q calculus of variations Chapter classical solution consider constant constraint controlled Markov diffusion convergence convex Corollary defined definition denote differential games dynamic programming equation dynamic programming principle Dynkin formula Example exists exit finite formulation given Hence HJB equation holds implies inequality initial data Ishii Lemma linear Lipschitz continuous Markov chain Markov control policy Markov processes 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 Section semigroup Soner stochastic control stochastic control problem stochastic differential equations subset Suppose t₁ Theorem 9.1 uniformly continuous unique value function Verification Theorem viscosity solution viscosity subsolution viscosity supersolution