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 1-5 of 79
Page 11
... obtain a nonlinear partial differential equation satisfied by the value function . In general however , the value function is not differentiable . In that case a notion of “ weak " solutions to this equation is needed . This will be the ...
... obtain a nonlinear partial differential equation satisfied by the value function . In general however , the value function is not differentiable . In that case a notion of “ weak " solutions to this equation is needed . This will be the ...
Page 14
... = x · P(t)x for some symmetric matrix P(t). We substitute W(t, x) into (5.11) to obtain - ∂ ∂t W(t, x) + H(t, x, DxW(t, x)) = -x · ∂ ∂t P(t)x + N −1(t)B (t)P(t)x · B (t)P(t)x -2A(t)x · P(t)x - x · M(t)x ∂ = x · P(t) + P(t)B(t)N ...
... = x · P(t)x for some symmetric matrix P(t). We substitute W(t, x) into (5.11) to obtain - ∂ ∂t W(t, x) + H(t, x, DxW(t, x)) = -x · ∂ ∂t P(t)x + N −1(t)B (t)P(t)x · B (t)P(t)x -2A(t)x · P(t)x - x · M(t)x ∂ = x · P(t) + P(t)B(t)N ...
Page 15
... obtain d ds -x * ( s ) = [ A ( s ) – B ( s ) N − 1 ( s ) B ′ ( s ) P ( s ) ] x * ( s ) . – W ( t , x ) for This equation has a unique solution satisfying the initial condition x * ( t ) = x . Thus there is a unique control u ...
... obtain d ds -x * ( s ) = [ A ( s ) – B ( s ) N − 1 ( s ) B ′ ( s ) P ( s ) ] x * ( s ) . – W ( t , x ) for This equation has a unique solution satisfying the initial condition x * ( t ) = x . Thus there is a unique control u ...
Page 22
... obtain n Pi ( r ) = Σ Zij ( r ) Pj ( r ) n = j = 1 t1 d n ds Zij ds - Σ = v ( @ ) ( B ) ( 6 ) - [ " & { Σ = ( 0 ) P } ( ) } de = j = 1 მ მე ; მ J ( r , x * ( r ) ; u * ) = -V ( r , x * ( r ) ) . მე ; ப When the value function is ...
... obtain n Pi ( r ) = Σ Zij ( r ) Pj ( r ) n = j = 1 t1 d n ds Zij ds - Σ = v ( @ ) ( B ) ( 6 ) - [ " & { Σ = ( 0 ) P } ( ) } de = j = 1 მ მე ; მ J ( r , x * ( r ) ; u * ) = -V ( r , x * ( r ) ) . მე ; ப When the value function is ...
Page 24
... obtain for all v € U ( 6.16 ) 0 ≤ L ( s , x * ( s ) , v ) + P ( s ) · ƒ ( s , x * ( s ) , v ) • - L ( s , x * ( s ) , u * ( s ) ) − P ( s ) · ƒ ( s , x * ( s ) , u * ( s ) ) . By ( 5.4 ) , this is equivalent to ( 6.3 ) . 1.7 ...
... obtain for all v € U ( 6.16 ) 0 ≤ L ( s , x * ( s ) , v ) + P ( s ) · ƒ ( s , x * ( s ) , v ) • - L ( s , x * ( s ) , u * ( s ) ) − P ( s ) · ƒ ( s , x * ( s ) , u * ( s ) ) . By ( 5.4 ) , this is equivalent to ( 6.3 ) . 1.7 ...
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