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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Page 133
... diffusion processes on IR " . This will be discussed in the next section . The backward evolution operator is a second - order partial differ- ential operator of ... Markov diffusion processes on R"; stochastic differential equations.
... diffusion processes on IR " . This will be discussed in the next section . The backward evolution operator is a second - order partial differ- ential operator of ... Markov diffusion processes on R"; stochastic differential equations.
Page 136
... diffusion limit is called the heavy traffic limit . See Harrison [ Har ] concerning the use of heavy traffic limits for flow control . In other applications a diffusion limit is obtained for processes which are not Markov , or which are ...
... diffusion limit is called the heavy traffic limit . See Harrison [ Har ] concerning the use of heavy traffic limits for flow control . In other applications a diffusion limit is obtained for processes which are not Markov , or which are ...
Page 426
... diffusion processes , diffusion processes with infi- nite horizon , 171 controlled Markov processes , 139 , 223 dynamic programming principle abstract , 60 controlled Markov ... Markov diffusion processes , 133 conditioned , 426 Index.
... diffusion processes , diffusion processes with infi- nite horizon , 171 controlled Markov processes , 139 , 223 dynamic programming principle abstract , 60 controlled Markov ... Markov diffusion processes , 133 conditioned , 426 Index.
Contents
Viscosity Solutions | 53 |
Controlled Markov Diffusions in R | 157 |
SecondOrder Case | 213 |
Copyright | |
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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 c₁ C¹(Q calculus of variations Chapter classical solution consider constant convergence convex Corollary cylindrical region D₂V defined definition denote dynamic programming equation dynamic programming principle Dynkin formula Example exists exit finite first-order formulation given Hamilton-Jacobi equation Hence HJB equation holds implies inequality initial condition initial data lateral boundary Lebesgue left endpoint Lemma linear Lipschitz continuous Markov chain Markov control policy Markov processes maximum principle minimizing Moreover nonlinear obtain optimal control optimal control problems partial derivatives partial differential equation proof of Theorem prove R₁ reference probability system result satisfies second-order Section stochastic control stochastic differential equation Suppose t₁ Theorem 5.1 tion unique value function variations problem Verification Theorem viscosity solution viscosity subsolution viscosity supersolution yields