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. |
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Results 1-3 of 72
Page vii
... Chapter I , and to the corresponding theory of viscosity solutions in Chapter II . A rather elementary introduc- tion to dynamic programming for controlled Markov processes is provided in Chapter III . This is followed by the more ...
... Chapter I , and to the corresponding theory of viscosity solutions in Chapter II . A rather elementary introduc- tion to dynamic programming for controlled Markov processes is provided in Chapter III . This is followed by the more ...
Page 55
... ( Chapter VII ) and in the numerical analysis of the control problems ( Chapter IX ) . To characterize a viscosity solution uniquely we need to specify the so- lution at the terminal time and at the boundary of the state space . In some ...
... ( Chapter VII ) and in the numerical analysis of the control problems ( Chapter IX ) . To characterize a viscosity solution uniquely we need to specify the so- lution at the terminal time and at the boundary of the state space . In some ...
Page 213
... chapter we study the exit time control of a Markov diffusion pro- cess as formulated in Section IV.2 . With the exception of the last section , we assume that the state space is a bounded finite - dimensional set . The main purpose of ...
... chapter we study the exit time control of a Markov diffusion pro- cess as formulated in Section IV.2 . With the exception of the last section , we assume that the state space is a bounded finite - dimensional set . The main purpose of ...
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 control assume assumptions boundary condition boundary data bounded brownian motion C₁ Cą(Q calculus of variations Chapter classical solution consider continuous on Q controlled Markov diffusion convergence convex Corollary D₂V defined definition denote deterministic dynamic programming equation dynamic programming principle Dynkin formula Example exists exit finite formula Hence HJB equation holds implies inequality initial data lateral boundary Lebesgue Lemma lim sup linear Lipschitz continuous Markov chain Markov control policy Markov processes maximum principle minimizing Moreover nonlinear obtain optimal control optimal Markov control partial derivatives partial differential equation proof of Theorem prove reference probability system Remark result satisfies second-order Section semiconvex stochastic control stochastic differential equation Suppose t₁ Theorem 5.1 uniformly continuous unique value function Verification Theorem viscosity solution viscosity subsolution viscosity supersolution yields