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Dynamic Stochastic Optimization (Paperback, Softcover reprint of the original 1st ed. 2004)
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Dynamic Stochastic Optimization (Paperback, Softcover reprint of the original 1st ed. 2004)
Series: Lecture Notes in Economics and Mathematical Systems, 532
Expected to ship within 10 - 15 working days
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Uncertainties and changes are pervasive characteristics of modern
systems involving interactions between humans, economics, nature
and technology. These systems are often too complex to allow for
precise evaluations and, as a result, the lack of proper management
(control) may create significant risks. In order to develop robust
strategies we need approaches which explic itly deal with
uncertainties, risks and changing conditions. One rather general
approach is to characterize (explicitly or implicitly)
uncertainties by objec tive or subjective probabilities (measures
of confidence or belief). This leads us to stochastic optimization
problems which can rarely be solved by using the standard
deterministic optimization and optimal control methods. In the
stochastic optimization the accent is on problems with a large
number of deci sion and random variables, and consequently the
focus ofattention is directed to efficient solution procedures
rather than to (analytical) closed-form solu tions. Objective and
constraint functions of dynamic stochastic optimization problems
have the form of multidimensional integrals of rather involved in
that may have a nonsmooth and even discontinuous character - the
tegrands typical situation for "hit-or-miss" type of decision
making problems involving irreversibility ofdecisions or/and abrupt
changes ofthe system. In general, the exact evaluation of such
functions (as is assumed in the standard optimization and control
theory) is practically impossible. Also, the problem does not often
possess the separability properties that allow to derive the
standard in control theory recursive (Bellman) equations."
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