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 88
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... example, Theorem II.5.1 refers to Theorem 5.1 in Chapter II. Similarly, IV(3.7) refers to formula (3.7) of Chapter IV; and within Chapter IV we write simply (3.7) for such a reference. IRn denotes n-dimensional euclidean space, with ...
... example, Theorem II.5.1 refers to Theorem 5.1 in Chapter II. Similarly, IV(3.7) refers to formula (3.7) of Chapter IV; and within Chapter IV we write simply (3.7) for such a reference. IRn denotes n-dimensional euclidean space, with ...
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... example, we often consider Q3 G G1'2(G), where either G : Q or G : The spaces Cg'k(G), GŁ'k(G) are defined similarly as above. The gradient vector and matrix of second-order partial derivatives of ˘(t, are denoted by DIQ Didi, or ...
... example, we often consider Q3 G G1'2(G), where either G : Q or G : The spaces Cg'k(G), GŁ'k(G) are defined similarly as above. The gradient vector and matrix of second-order partial derivatives of ˘(t, are denoted by DIQ Didi, or ...
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... Examples. We start our discussion by giving some examples. In choosing examples, in this section and later in the book, we have included several highly simplified models chosen from such diverse applications as inventory theory, control ...
... Examples. We start our discussion by giving some examples. In choosing examples, in this section and later in the book, we have included several highly simplified models chosen from such diverse applications as inventory theory, control ...
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... Example 2.1. Consider the production planning of a factory producing n commodities. Let x i (s), u i (s) denote respectively the inventory level and production rate for commodity i = 1,···,n at time s. In this simple model we assume ...
... Example 2.1. Consider the production planning of a factory producing n commodities. Let x i (s), u i (s) denote respectively the inventory level and production rate for commodity i = 1,···,n at time s. In this simple model we assume ...
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... Example 2.3. If U = [−a, a] with a < ∞, it is an example of a linear regulator problem with a saturation constraint. One can also consider the problem of controlling the solution x(s)=(x 1(s), x2 (s)) to (2.4) on an infinite time ...
... Example 2.3. If U = [−a, a] with a < ∞, it is an example of a linear regulator problem with a saturation constraint. One can also consider the problem of controlling the solution x(s)=(x 1(s), x2 (s)) to (2.4) on an infinite time ...
Contents
1 | |
Viscosity Solutions | 57 |
Differential Games | 375 |
A Duality Relationships 397 | 396 |
References | 409 |
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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 calculus of variations Chapter classical solution consider constant controlled Markov diffusion convergence convex Corollary cost function define definition denote differential games dynamic programming equation dynamic programming principle Dynkin formula Example exists exit finite first formulation G Q0 Hamilton-Jacobi equation Hence HJB equation holds implies inequality initial data Ishii Lemma 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 progressively measurable proof of Theorem prove reference probability system Remark result risk sensitive satisfies satisfying Section semigroup Soner stochastic control stochastic control problem stochastic differential equations subset Suppose Theorem 9.1 uniformly continuous unique value function Verification Theorem viscosity solution viscosity subsolution viscosity supersolution