Spherical Convex Hull Membership: Complexity and Performance of Geometric and Accelerated Methods

Abstract

We study the problem of testing membership of the origin in the convex hull of $n$ unit vectors in $\mathbb{R}^m$, seeking a convex combination of norm at most $\varepsilon$ or a strict separating hyperplane. The Spherical Triangle Algorithm (STA) uses nearest-point updates on pivot segments. We first establish a conditional $O(1/\varepsilon)$ iteration bound from accumulated geometric progress, allowing deeper pivots to compensate for shallow ones. A three-point example shows that ordinary STA can require $\Theta(\varepsilon^{-2})$ iterations. We show that the same conditional bound holds for STA with Wolfe minimum-norm correction (STA--WMC), provided the averaged geometric progress condition holds for the STA steps taken within this corrected method. We then combine STA, WMC, and the Nemirovski weighted-pivot method (NWP) in a hybrid with unconditional bounds of $O(\sqrt{\log n}/\varepsilon)$ matrix--vector products with the input matrix or its transpose and $O(mn\sqrt{\log n}/\varepsilon)$ arithmetic operations, including corrections. We also give a self-contained derivation of the $O(\sqrt{\log n}/\varepsilon)$ membership iteration bound for entropic Nesterov acceleration. Computationally, we compare five methods---ordinary STA, STA--WMC, the hybrid, standalone NWP, and entropic Nesterov acceleration---on convex-hull queries derived from 14 public benchmark datasets and on 204 controlled synthetic inputs. STA--WMC completes all cases in both comparisons, and the hybrid completes all public-data cases. STA performs well on favorable interior and separation instances; Nesterov acceleration is faster than the STA-based methods on the highest-dimensional random membership tests. The experiments show complementary strengths and distinguish practical performance from worst-case guarantees.