Abstract:
The paper formulates the general problem of a decision making under uncertainty of the situation, requirements and with the decision itself being random. It is shown that typical optimization problems follow such as stochastic approximation, random search, stochastic programming, integer programming and vectorial optimization follow as particular cases. The condition of local improvements lead to algorithms of decision selection whereby the decision is updated with additional current data. Examples of the algorithms are given.
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