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 34
... called a Hamilton - Jacobi equation . If there are no constraints on x ( t1 ) , i.e. , M = R " , then ( 8.10 ) V ( t1 , x ) = v ( x ) , x Є R " . Boundary conditions of type ( 8.10 ) , prescribing V at a fixed time t1 , are called ...
... called a Hamilton - Jacobi equation . If there are no constraints on x ( t1 ) , i.e. , M = R " , then ( 8.10 ) V ( t1 , x ) = v ( x ) , x Є R " . Boundary conditions of type ( 8.10 ) , prescribing V at a fixed time t1 , are called ...
Page 129
... called a backward evolution equation . If it has a solution Є D ( A ) which also satisfies ( 2.11 ) , then by the Dynkin formula ( 2.7 ) with st1 ( 2.12 ) Q ( t , x ) = Etz { [ " l ( 3 , x ( 8 ) ) ds + * ( x ( t1 ) ) } · This gives a ...
... called a backward evolution equation . If it has a solution Є D ( A ) which also satisfies ( 2.11 ) , then by the Dynkin formula ( 2.7 ) with st1 ( 2.12 ) Q ( t , x ) = Etz { [ " l ( 3 , x ( 8 ) ) ds + * ( x ( t1 ) ) } · This gives a ...
Page 136
... 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 Markov on a higher ...
... 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 Markov on a higher ...
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