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 27
Page 36
... lateral boundary condition V = g to be enforced at all ( t , x ) E [ to , t1 ) × 80. Let us first assume , as in ( 3.10 ) , ( 8.12 ) g = 0 , L≥0 , 0 . Then , as already noted in the discussion following ( 5.19 ) , V ( t , x ) = 0 for ...
... lateral boundary condition V = g to be enforced at all ( t , x ) E [ to , t1 ) × 80. Let us first assume , as in ( 3.10 ) , ( 8.12 ) g = 0 , L≥0 , 0 . Then , as already noted in the discussion following ( 5.19 ) , V ( t , x ) = 0 for ...
Page 107
... lateral boundary condition ( 9.3a ) if it is a viscosity supersolution of ( 8.1 ) in Q and for each wε C∞ ( Q ) , ( 13.5 ) max მ { - w ( t , x ) − + H ( t , x , D2w ( t , x ) ) , W ( t , x ) − g ( t , x ) } ≥ 0 , Ət - } : at every ...
... lateral boundary condition ( 9.3a ) if it is a viscosity supersolution of ( 8.1 ) in Q and for each wε C∞ ( Q ) , ( 13.5 ) max მ { - w ( t , x ) − + H ( t , x , D2w ( t , x ) ) , W ( t , x ) − g ( t , x ) } ≥ 0 , Ət - } : at every ...
Page 381
... lateral parts of O * Q . As in I ( 3.6 ) we write For the terminal part of Lemma 5.3 . lim to x ЄŌ . ( 8 , y ) → ( tı , x ) h10 ¥ ( x ) = ¥ ( t1 , x ) . * Q we obtain ( 5.5 ) from the following lemma . Vh ( s , y ) = ( x ) uniformly ...
... lateral parts of O * Q . As in I ( 3.6 ) we write For the terminal part of Lemma 5.3 . lim to x ЄŌ . ( 8 , y ) → ( tı , x ) h10 ¥ ( x ) = ¥ ( t1 , x ) . * Q we obtain ( 5.5 ) from the following lemma . Vh ( s , y ) = ( x ) uniformly ...
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 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