A discrete optimization method based on a parameterization of a Grassmannian in multidimensional dichotomous data structuring

An approach to reducing large computational time in the problem of multidimensional dichotomous data structuring based on algebraic properties of finite geometries is proposed. A vector parameterization of the Grassmannian Gr₂(k, n) reducing memory expenditures and the number of operations required to solve this problem is introduced. A parallelization algorithm based on this parameterization and Gray coding which further reduces computational time is constructed.