Titlebook: Optimal Control of Random Sequences in Problems with Constraints; A. B. Piunovskiy Book 1997 Kluwer Academic Publishers 1997 Optimal contr

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Optimal Control of Random Sequences in Problems with Constraints978-94-011-5508-3

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Methods of Stochastic Optimal Control,e originated in the fifties and sixties [31, 38, 39, 40, 61, 70, 72, 112, 118, 144, 156, 157, 158, 210], they were stimulated by the famous monograph by R. Bellman [30]. The computational aspects of the stochastic optimal control form a new line of inquiry [23, 24, 113, 137, 139, 189, 217] but this is beyond the scope of the present monograph.

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Solvability of the Main Constrained Problem and Some Extensions, stationary selectors in the corresponding models). If one investigates a (homogeneous) Markov model then it is sufficient to consider only Markov (stationary) randomized strategies. The set of Markov selectors is sufficient for the convex Markov models. The elementary example illustrates all the theoretical reasonings.

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Optimal Control Problems with Constraints,orithm constructing a saddle point for the Lagrange function is detailed for problems with integral functionals. The elementary example illustrating all of the theoretical reasonings is presented at the end of the chapter.

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Book 1997the same time these processes are used for solving of many applied problems in the queueing theory, in mathematical economics. in the theory of controlled technical systems, etc. . In this connection, methods of the theory of controlled processes constitute the every day instrument of many specialis

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Introduction,raphs [36, 74]. Different controlled stochastic processes with continuous time were investigated in the books [1, 60, 91, 101, 131, 136, 145, 208]. Broadly speaking, the problem consists in the following. Let . be some control method, that is, a control strategy. We assume that there is pre-set real

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Methods of Stochastic Optimal Control, are only formulated; the proofs can be found in the literature cited. All the material presented, including the corresponding bibliography, in no way pretend to completeness; nonetheless, the first chapter contains practically all the information which will be necessary for the further investigatio

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Optimal Control Problems with Constraints, non-Markovian, with the finite and infinite horizon, with the discount factor and with the average loss. The investigation is based on the general theory of convex programming [151, 155, 191]: the Lagrange function is introduced, the corresponding version of the Kuhn-Tucker theorem is formulated, a