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Optimization under Uncertainty

  • Alexander S. Belenky
Part of the Applied Optimization book series (APOP, volume 20)

Abstract

In the preceding section, it was assumed that all parameters in the description of the function f (x) and in that of the set of admissible variable values M were deterministic numerical parameters. In problems of stochastic programming, all or some of the parameters are assumed to be random. Problems of stochastic programming differ in the type, amount, and sequence of data about these random quantities obtained from measuring or observation. These problems are studied by two basic groups of stochastic programming methods implementing the passive and active approaches [1].

Keywords

Membership Function Fuzzy Number Linear Programming Problem Stochastic Programming Cooperative Game 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • Alexander S. Belenky

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