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 132
... term on the right side of ( 4.6 ) comes from the deterministic evolution of x ( s ) during intervals of constancy of z ( s ) , as in Example ( b ) , and the last term comes from the parameter process z ( 8 ) . Example ( c ) is a special ...
... term on the right side of ( 4.6 ) comes from the deterministic evolution of x ( s ) during intervals of constancy of z ( s ) , as in Example ( b ) , and the last term comes from the parameter process z ( 8 ) . Example ( c ) is a special ...
Page 144
... term in ( 8.15 ) is quadratic in DW . The dynamic program- ming equation is linearizable by the following transformation . Let Þ ( t , x ) = exp ( −W ( t , x ) ] , $ ( x ) = exp ( −√ ( x ) ] . Then ( 8.15 ) , ( 8.2 ) become ( 8.17 ) ...
... term in ( 8.15 ) is quadratic in DW . The dynamic program- ming equation is linearizable by the following transformation . Let Þ ( t , x ) = exp ( −W ( t , x ) ] , $ ( x ) = exp ( −√ ( x ) ] . Then ( 8.15 ) , ( 8.2 ) become ( 8.17 ) ...
Page 178
... term ( x ) on the right side is unaffected by the control u ( · ) . Hence to minimize J is equivalent to minimizing the last term . We assume that L is continuous on Q。× U and that ( 6.3 ) | L ( t , x , v ) | ≤ C3 ( 1 + | x | * ) for ...
... term ( x ) on the right side is unaffected by the control u ( · ) . Hence to minimize J is equivalent to minimizing the last term . We assume that L is continuous on Q。× U and that ( 6.3 ) | L ( t , x , v ) | ≤ C3 ( 1 + | x | * ) for ...
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