MinidxksiS
MinidxksiS is a theoretical framework and algorithmic approach used in discrete optimization to identify a minimal subset of indices that satisfies a family of constraints defined over indexed elements. The central task is to find the smallest index set I such that a specified predicate P is satisfied when restricted to the elements whose positions lie in I. The concept is described as a generalization of index-based covering and independence problems, with a focus on minimality of the index set rather than of the data subset.
Formalization typically begins with a finite universe U = {1,...,n}, a collection of constraints C, and a
Applications span feature selection in machine learning, sensor placement, and data summarization, where one seeks to
Related concepts include the minimum set cover, hitting set, and minimum dominating set problems, as well as