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Titlebook: Controlled Diffusion Processes; Nicolai V. Krylov Book 1980 Springer-Verlag Berlin Heidelberg 1980 diffusion.diffusion process.diffusion p

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書(shū)目名稱(chēng)Controlled Diffusion Processes
編輯Nicolai V. Krylov
視頻videohttp://file.papertrans.cn/238/237460/237460.mp4
概述Includes supplementary material:
叢書(shū)名稱(chēng)Stochastic Modelling and Applied Probability
圖書(shū)封面Titlebook: Controlled Diffusion Processes;  Nicolai V. Krylov Book 1980 Springer-Verlag Berlin Heidelberg 1980 diffusion.diffusion process.diffusion p
描述Stochastic control theory is a relatively young branch of mathematics. The beginning of its intensive development falls in the late 1950s and early 1960s. ~urin~ that period an extensive literature appeared on optimal stochastic control using the quadratic performance criterion (see references in Wonham [76]). At the same time, Girsanov [25] and Howard [26] made the first steps in constructing a general theory, based on Bellman‘s technique of dynamic programming, developed by him somewhat earlier [4]. Two types of engineering problems engendered two different parts of stochastic control theory. Problems of the first type are associated with multistep decision making in discrete time, and are treated in the theory of discrete stochastic dynamic programming. For more on this theory, we note in addition to the work of Howard and Bellman, mentioned above, the books by Derman [8], Mine and Osaki [55], and Dynkin and Yushkevich [12]. Another class of engineering problems which encouraged the development of the theory of stochastic control involves time continuous control of a dynamic system in the presence of random noise. The case where the system is described by a differential equation
出版日期Book 1980
關(guān)鍵詞diffusion; diffusion process; diffusion process (statistic); fully nonlinear equations; linear optimizat
版次1
doihttps://doi.org/10.1007/978-3-540-70914-5
isbn_softcover978-3-540-70913-8
isbn_ebook978-3-540-70914-5Series ISSN 0172-4568 Series E-ISSN 2197-439X
issn_series 0172-4568
copyrightSpringer-Verlag Berlin Heidelberg 1980
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General Properties of a Payoff Function,he feasibility of further reduction of a set of strategies to Markov strategies without decreasing the payoff. Considering the problem of optimal stopping of a controlled process, we describe in this chapter a subclass of stopping rules, which yield the same payoff performance as the class of all possible stopping rules.
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Book 198060s. ~urin~ that period an extensive literature appeared on optimal stochastic control using the quadratic performance criterion (see references in Wonham [76]). At the same time, Girsanov [25] and Howard [26] made the first steps in constructing a general theory, based on Bellman‘s technique of dyn
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https://doi.org/10.1007/978-3-662-40046-3he feasibility of further reduction of a set of strategies to Markov strategies without decreasing the payoff. Considering the problem of optimal stopping of a controlled process, we describe in this chapter a subclass of stopping rules, which yield the same payoff performance as the class of all possible stopping rules.
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The Construction of ,-Optimal Strategies,neering than Markov strategies (see Definition 3.17). However, from a theoretical point of view adjoint Markov strategies are more convenient in some respects than Markov strategies. Considering adjoint Markov strategies, we prove in Section 3 that a solution of the Bellman equation is a payoff function.
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