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Construction of aggregation operators for automated decision making via optimal interpolation and global optimization
This paper examines methods of pointwise construction of aggregation
operators via optimal interpolation. It is shown that several types
of application-specific requirements lead to interpolatory type
constraints on the aggregation function. These constraints are
translated into global optimization problems, which are the focus of
this paper. We present several methods of reduction of the number of
variables, and formulate suitable numerical algorithms based on
Lipschitz optimization.