A Polynomial-Time Algorithm for Computing the Exact Convex Hull in High-Dimensional Spaces
This study presents a novel algorithm for identifying the set of extreme points that constitute the exact convex hull of a point set in high-dimensional Euclidean space. The proposed method iteratively solves a sequence of dynamically updated quadratic programming (QP) problems for each point and exploits their solutions to provide theoretical guarantees for exact convex hull identification. For a dataset of \( n \) points in an \( m \)-dimensional space, the algorithm achieves a dimension-independent worst-case time complexity of \( O(n^{p+2} \log(1/ε)) \), where \( p \) depends on the choice of QP solver (e.g., \( p = 4 \) corresponds to the worst-case bound when using an interior-point method), and \( ε\) denotes the target numerical precision (i.e., the optimality tolerance of the QP solver). The proposed method is applicable to spaces of arbitrary dimensionality and exhibits particular efficiency in high-dimensional settings, owing to its polynomial-time complexity, whereas existing exponential-time algorithms become computationally impractical.