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 54
... dynamic programming operator is viewed as the infinitesimal generator of a two parameter nonlinear semi- group ... principle and ( 3.11 ) is noth- ing but the derivation of the dynamic programing equation for a smooth value function ...
... dynamic programming operator is viewed as the infinitesimal generator of a two parameter nonlinear semi- group ... principle and ( 3.11 ) is noth- ing but the derivation of the dynamic programing equation for a smooth value function ...
Page 54
... dynamic programming operator is viewed as the infinitesimal generator of a two parameter nonlinear semi- group ... principle and ( 3.11 ) is noth- ing but the derivation of the dynamic programing equation for a smooth value function ...
... dynamic programming operator is viewed as the infinitesimal generator of a two parameter nonlinear semi- group ... principle and ( 3.11 ) is noth- ing but the derivation of the dynamic programing equation for a smooth value function ...
Page 152
... dynamic programming equation ( 7.5 ) in the form II ( 3.12 ) , proceeding ... principle holds , from which it follows that the value function is a ... dynamic programming principle holds leads to a value function which is the same as the ...
... dynamic programming equation ( 7.5 ) in the form II ( 3.12 ) , proceeding ... principle holds , from which it follows that the value function is a ... dynamic programming principle holds leads to a value function which is the same as the ...
Contents
Viscosity Solutions | 53 |
Controlled Markov Diffusions in R | 157 |
SecondOrder Case | 213 |
Copyright | |
7 other sections not shown
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 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