Derivative of set measure functions and its application (theoretical bases of investment objectives)
DOI:
https://doi.org/10.15802/stp2014/25870Keywords:
algebra of sets, set function over a measure, derivative set function over a measure, sets sequence limitAbstract
Purpose. It is necessary to develop the theoretical fundamentals for solving the investment objectives presented in the form of set function as vector optimization tasks or tasks of constrained extremum. Methodology. Set functions and their derivatives of measure are used as research of investment objectives. Necessary condition of set function minimum is proved. In the tasks for constrained extremum the method of Lagrange is used. It is shown that this method can also be used for the set function. It is used the measure for proof, which generalizes the Lebesgue measure, and the concept of set sequence limit is introduced. It is noted that the introduced limit over a measure coincides with the classical Borel limit and can be used in order to prove the existence of derivative from set function over a measure on convergent of sets sequence. Findings. An algorithm of solving the investment objective for constrained extremum in relation to investment objectives was offered. Originality. Scientific novelty lies in the fact that in multivariate objects for constrained extremum one can refuse from immediate enumeration. One can use the proposed algorithm of constructing (selection) of options that allow building a convex linear envelope of Pareto solutions. This envelope will let the person who makes a decision (DM), select those options that are "better" from a position of DM, and consider some of the criteria, the formalization of which are difficult or can not be described in mathematical terms. Practical value. Results of the study provide the necessary theoretical substantiation of decision-making in investment objectives, when there is a significant number of an investment objects and immediate enumeration of options is very difficult on time costs even for modern computing techniques.
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