Two-scale stochastic systems : asymptotic analysis and control
著者
書誌事項
Two-scale stochastic systems : asymptotic analysis and control
（Applications of mathematics, 49）
Springer, c2003
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注記
Bibliography: p. [259]-263
Includes index
内容説明・目次
内容説明
In many complex systems one can distinguish "fast" and "slow" processes with radically di?erent velocities. In mathematical models based on di?er- tialequations,suchtwo-scalesystemscanbedescribedbyintroducingexpl- itly a small parameter?on the left-hand side ofstate equationsfor the "fast" variables,and these equationsare referredto assingularly perturbed. Surpr- ingly, this kind of equation attracted attention relatively recently (the idea of distinguishing "fast" and "slow" movements is, apparently, much older). Robert O'Malley, in comments to his book, attributes the originof the whole historyofsingularperturbationsto the celebratedpaperofPrandtl[79]. This was an extremely short note, the text of his talk at the Third International Mathematical Congress in 1904: the young author believed that it had to be literally identical with his ten-minute long oral presentation. In spite of its length, it had a tremendous impact on the subsequent development. Many famous mathematicians contributed to the discipline, having numerous and important applications. We mention here only the name of A. N.
Tikhonov, whodevelopedattheendofthe1940sinhisdoctoralthesisabeautifultheory for non-linear systems where the fast variables can almost reach their eq- librium states while the slow variables still remain near their initial values: the aerodynamics of a winged object like a plane or the "Katiusha" rocket may serve an example of such a system. It is generally accepted that the probabilistic modeling of real-world p- cesses is more adequate than the deterministic modeling.
目次
0 Warm-up.- 1 Toolbox: Moment Bounds for Solutions of Stable SDEs.- 2 The Tikhonov Theory for SDEs.- 3 Large Deviations.- 4 Uniform Expansions for Two-Scale Systems.- 5 Two-Scale Optimal Control Problems.- 6 Applications.- A.1 Basic Facts About SDEs.- A.1.1 Existence and Uniqueness of Strong Solutions for SDEs with Random Coefficients.- A.1.2 Existence and Uniqueness with a Lyapunov Function.- A.1.3 Moment Bounds for Linear SDEs.- A.1.4 The Novikov Condition.- A.2 Exponential Bounds for Fundamental Matrices.- A.2.1 Uniform Bound in the Time-Homogeneous Case.- A.2.2 Nonhomogeneous Case.- A.2.3 Models with Singular Perturbations.- A.3 Total Variation Distance and Hellinger Processes.- A.3.1 Total Variation Distance and Hellinger Integrals.- A.3.2 The Hellinger Processes.- A.3.3 Example: Diffusion-Type Processes.- A.4 Hausdorff Metric.- A.5 Measurable Selection.- A.5.1 Aumann Theorem.- A.5.2 Filippov Implicit Function Lemma.- A.5.3 Measurable Version of the Caratheodory Theorem.- A.6.1 Notations and Preliminaries.- A.6.2 Integration of Stochastic Kernels.- A.6.3 Distributions of Integrals.- A.6.4 Compactness of the Limit of Attainability Sets.- A.6.5 Supports of Conditional Distributions.- A.7 The Komlos Theorem.- Historical Notes.- References.
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