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 64
... equivalent definition is obtained by replacing D with C∞ ( Q ) . Indeed this is always the case when Gt is a partial differential operator . See Theorem 6.1 below . However , if Σ is not compact and Gt is not a partial differential ...
... equivalent definition is obtained by replacing D with C∞ ( Q ) . Indeed this is always the case when Gt is a partial differential operator . See Theorem 6.1 below . However , if Σ is not compact and Gt is not a partial differential ...
Page 72
... equivalent for any partial differen- tial equation with maximum principle . From the above proof it is also clear that in Definition 4.2 we could use test functions from any function space between C1,2 ( Q ) and C∞ ( Q ) . Therefore ...
... equivalent for any partial differen- tial equation with maximum principle . From the above proof it is also clear that in Definition 4.2 we could use test functions from any function space between C1,2 ( Q ) and C∞ ( Q ) . Therefore ...
Page 198
... equivalent interpretation of generalized partial deriva- tives is in terms of the Schwartz theory of distributions [ Zi ] . Any function in Loc ( Qo ) has ( by definition ) partial derivatives of all orders in the Schwartz distribution ...
... equivalent interpretation of generalized partial deriva- tives is in terms of the Schwartz theory of distributions [ Zi ] . Any function in Loc ( Qo ) has ( by definition ) partial derivatives of all orders in the Schwartz distribution ...
Contents
Viscosity Solutions | 53 |
Controlled Markov Diffusions in R | 157 |
SecondOrder Case | 213 |
Copyright | |
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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