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. |
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... H. Fleming AND APPLIED H.M. Soner PROBABILITY 25 Controlled Markov Processes and Viscosity Solutions Second Edition Springer Controlled Markov Processes and Viscosity Solutions Controlled Markov Processes and. Front Cover.
... H. Fleming AND APPLIED H.M. Soner PROBABILITY 25 Controlled Markov Processes and Viscosity Solutions Second Edition Springer Controlled Markov Processes and Viscosity Solutions Controlled Markov Processes and. Front Cover.
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Wendell H. Fleming, Halil Mete Soner. Controlled Markov Processes and Viscosity Solutions Controlled Markov Processes and Viscosity Solutions Second Edition Springer.
Wendell H. Fleming, Halil Mete Soner. Controlled Markov Processes and Viscosity Solutions Controlled Markov Processes and Viscosity Solutions Second Edition Springer.
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Wendell H. Fleming, Halil Mete Soner. Controlled Markov Processes and Viscosity Solutions Second Edition Springer Div. Applied Mathematics Brown University 182 George Street Providence, RI. Wendell H. Fleming , H. Mete Soner.
Wendell H. Fleming, Halil Mete Soner. Controlled Markov Processes and Viscosity Solutions Second Edition Springer Div. Applied Mathematics Brown University 182 George Street Providence, RI. Wendell H. Fleming , H. Mete Soner.
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... Markov Processes : Classical Solutions119 III.1 Introduction . 119 III.2 Markov processes and their evolution operators . 120 III.3 Autonomous ( time - homogeneous ) Markov processes 123 III.4 Classes of Markov processes . 124 III.5 Markov ...
... Markov Processes : Classical Solutions119 III.1 Introduction . 119 III.2 Markov processes and their evolution operators . 120 III.3 Autonomous ( time - homogeneous ) Markov processes 123 III.4 Classes of Markov processes . 124 III.5 Markov ...
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... Markov diffusions VI.4 Auxiliary stochastic control problem VI.5 Bounded region Q VI.6 Small noise limits VI.7 H - infinity norm of a nonlinear system VI.8 Risk sensitive control . . . . VI.9 Logarithmic transformations for Markov processes ...
... Markov diffusions VI.4 Auxiliary stochastic control problem VI.5 Bounded region Q VI.6 Small noise limits VI.7 H - infinity norm of a nonlinear system VI.8 Risk sensitive control . . . . VI.9 Logarithmic transformations for Markov processes ...
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