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 82
Page 185
... Lemma 7.1 we have : Lemma 7.2 . Assume ( 3.5 ) and ( 4.5 ) . Let W be as in Theorem 4.2 . Then W = V = V , for every v , and property ( DP ) holds . Proof . By Dynkin's formula ( 7.11 ) W ( t , x ) ≤ Etx { ° L ( s , x ( s ) , u ( s ) ...
... Lemma 7.1 we have : Lemma 7.2 . Assume ( 3.5 ) and ( 4.5 ) . Let W be as in Theorem 4.2 . Then W = V = V , for every v , and property ( DP ) holds . Proof . By Dynkin's formula ( 7.11 ) W ( t , x ) ≤ Etx { ° L ( s , x ( s ) , u ( s ) ...
Page 208
... Lemma 11.2 . ( a ) VR = WR . ( b ) Let up ( t , x ) be the unique point of UR at which -L ( t , x , v ) - D2WR ( t , x ) v has a maximum on UR . Then u EL and u is an optimal Markov control policy . • Proof . We need only show that uRE ...
... Lemma 11.2 . ( a ) VR = WR . ( b ) Let up ( t , x ) be the unique point of UR at which -L ( t , x , v ) - D2WR ( t , x ) v has a maximum on UR . Then u EL and u is an optimal Markov control policy . • Proof . We need only show that uRE ...
Page 381
... Lemma 5.2 , we obtain a corresponding statement about subsolutions , and an estimate for Vh from below . In order to apply Lemma 5.2 , let us consider separately the terminal and lateral parts of O * Q . As in I ( 3.6 ) we write For the ...
... Lemma 5.2 , we obtain a corresponding statement about subsolutions , and an estimate for Vh from below . In order to apply Lemma 5.2 , let us consider separately the terminal and lateral parts of O * Q . As in I ( 3.6 ) we write For the ...
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 Cą(Q calculus of variations Chapter classical solution consider constant continuous on Q convergence convex Corollary cylindrical region defined definition denote dynamic programming equation dynamic programming principle Dynkin formula Example exists exit finite first-order formulation Hamilton-Jacobi equation Hence HJB equation holds implies inequality initial data lateral boundary Lemma lim sup linear Lipschitz continuous Markov chain Markov control policy Markov processes maximum principle minimizing Moreover nonlinear obtain optimal control optimal control problem partial derivatives partial differential equation proof of Theorem prove result satisfies second-order Section semigroup stochastic differential equation Suppose t₁ test function Theorem 5.1 uniformly continuous unique value function variations problem Verification Theorem viscosity solution viscosity subsolution viscosity supersolution yields