Controlled Markov Processes and Viscosity SolutionsThis book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It covers dynamic programming for deterministic optimal control problems, as well as to the corresponding theory of viscosity solutions. New chapters in this second edition introduce the role of stochastic optimal control in portfolio optimization and in pricing derivatives in incomplete markets and two-controller, zero-sum differential games. |
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Results 6-10 of 78
Page 25
... Moreover , for suitable Kp , ( 7.3 ) — | f ( x , v ) − f ( y , v ) | ≤ K。| x − y | for all x , y Є R1 and v Є U such that | v | ≤ p . By a control we mean any U- valued , Lebesgue measurable function u ( · ) on [ 0 , ∞ ) such that ...
... Moreover , for suitable Kp , ( 7.3 ) — | f ( x , v ) − f ( y , v ) | ≤ K。| x − y | for all x , y Є R1 and v Є U such that | v | ≤ p . By a control we mean any U- valued , Lebesgue measurable function u ( · ) on [ 0 , ∞ ) such that ...
Page 27
... Moreover , an admissible control u ( · ) € Ux is 8 - optimal at the initial point x if and only if it is 8 - optimal at every x ( s ) with sЄ [ 0,7 ] . We continue with the proof of a verification theorem . Let W € C1 ( O ) sat- isfy ...
... Moreover , an admissible control u ( · ) € Ux is 8 - optimal at the initial point x if and only if it is 8 - optimal at every x ( s ) with sЄ [ 0,7 ] . We continue with the proof of a verification theorem . Let W € C1 ( O ) sat- isfy ...
Page 29
... Moreover, V (−x) = V (x) and V (·) is increasing for x ≥ 0. Thus, we look for a class C1 solution W of (7.18) with these properties. Equation (7.18) is equivalent for x ≥ 0 to 1 V (x) + 2 (V (x))2 − 1 2x2 + xV (x)=0, if V (x) ≤ 1 ...
... Moreover, V (−x) = V (x) and V (·) is increasing for x ≥ 0. Thus, we look for a class C1 solution W of (7.18) with these properties. Equation (7.18) is equivalent for x ≥ 0 to 1 V (x) + 2 (V (x))2 − 1 2x2 + xV (x)=0, if V (x) ≤ 1 ...
Page 33
... Moreover , x * ( - ) C2 ( [ t , t1 ] ) . See Theorem 9.2 . = In a calculus of variations problem , f ( t , x , v ) = v and U = IR " . The control at time s is u ( s ) = x ( s ) . The Hamiltonian H in ( 5.4 ) takes the form ( 8.4 ) H ( t ...
... Moreover , x * ( - ) C2 ( [ t , t1 ] ) . See Theorem 9.2 . = In a calculus of variations problem , f ( t , x , v ) = v and U = IR " . The control at time s is u ( s ) = x ( s ) . The Hamiltonian H in ( 5.4 ) takes the form ( 8.4 ) H ( t ...
Page 39
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Contents
1 | |
Viscosity Solutions | 57 |
Classical Solutions119 | 118 |
Controlled Markov Diffusions in R | 151 |
SecondOrder Case | 199 |
Logarithmic Transformations and Risk Sensitivity | 227 |
Singular Perturbations 261 | 260 |
Singular Stochastic Control | 293 |
Finite Difference Numerical Approximations | 321 |
Differential Games | 375 |
A Duality Relationships 397 | 396 |
References | 409 |
Index 425 | 424 |
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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 brownian motion C₁ C¹(Q calculus of variations Chapter classical solution consider constant constraint controlled Markov diffusion convergence convex Corollary defined definition denote differential games dynamic programming equation dynamic programming principle Dynkin formula Example exists exit finite formulation given Hence HJB equation holds implies inequality initial data Ishii Lemma linear Lipschitz continuous Markov chain Markov control policy Markov processes minimizing Moreover nonlinear obtain optimal control optimal control problem partial derivatives partial differential equation progressively measurable proof of Theorem prove reference probability system Remark result risk sensitive satisfies Section semigroup Soner stochastic control stochastic control problem stochastic differential equations subset Suppose t₁ Theorem 9.1 uniformly continuous unique value function Verification Theorem viscosity solution viscosity subsolution viscosity supersolution