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 142
... linear regulator ) . This is a stochastic per- turbation of the linear quadratic regulator problem ( Example I.5.1 ) . The stochastic differential equations for x ( s ) are linear : ( 8.6 ) dx = [ A ( s ) x ( s ) + B ( s ) u ( s ) ] ds ...
... linear regulator ) . This is a stochastic per- turbation of the linear quadratic regulator problem ( Example I.5.1 ) . The stochastic differential equations for x ( s ) are linear : ( 8.6 ) dx = [ A ( s ) x ( s ) + B ( s ) u ( s ) ] ds ...
Page 155
... linear regulator problem ( Example 8.1 ) . That model has several features which are appealing from the viewpoint of engineering applications . The problem reduces to solving a matrix Riccati equation , and optimal control policies are ...
... linear regulator problem ( Example 8.1 ) . That model has several features which are appealing from the viewpoint of engineering applications . The problem reduces to solving a matrix Riccati equation , and optimal control policies are ...
Page 270
... linear partial differential equation ( 6.3 ) with ( 6.4 € ) . Theorem 6.1 . There exists K such that ( 6.16 ) | Vε ( t , x ) – V ° ( t , x ) | ≤ Kɛ , ( t , x ) Є Qo . € Proof . Consider the stochastic control problem with state ...
... linear partial differential equation ( 6.3 ) with ( 6.4 € ) . Theorem 6.1 . There exists K such that ( 6.16 ) | Vε ( t , x ) – V ° ( t , x ) | ≤ Kɛ , ( t , x ) Є Qo . € Proof . Consider the stochastic control problem with state ...
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