METRIC PROBLEMS AND THEIR APPLICATIONS IN MULTIDIMENSIONAL EUCLIDEAN SPACE

This article investigates classical and modern metric problems within the framework of multidimensional Euclidean space . It explores key mathematical tools such as Euclidean distance, inner products, orthogonal projections, and optimization techniques. Theoretical constructs are connected to practical applications in data science, robotics, and computer vision, where metric computations are essential for decision-making and geometric analysis. The paper highlights both the analytical foundations and computational aspects of metric problems and discusses the impact of dimensionality on geometric intuition and algorithmic efficiency.