Spherical Triangle Algorithm: A Fast Oracle for Convex Hull Membership Queries

TL;DR

The paper introduces the Spherical Triangle Algorithm, a fast oracle for convex hull membership queries, improving efficiency and applicability in linear programming, data reduction, and related computational geometry problems.

Contribution

It develops the Spherical-TA, a novel, efficient algorithm for convex hull membership, and demonstrates its effectiveness in various applications including data reduction and LP feasibility.

Findings

Spherical-TA improves complexity to O(1/ε) under certain conditions.

Spherical-TA outperforms existing algorithms in efficiency.

AVTA$^+$ enhances data reduction processes in practical applications.

Abstract

The it Convex Hull Membership(CHM) problem is: Given a point $p$ and a subset $S$ of $n$ points in $\mathbb{R}^m$, is $p \in conv(S)$? CHM is not only a fundamental problem in Linear Programming, Computational Geometry, Machine Learning and Statistics, it also serves as a query problem in many applications e.g. Topic Modeling, LP Feasibility, Data Reduction. The {\it Triangle Algorithm} (TA) \cite{kalantari2015characterization} either computes an approximate solution in the convex hull, or a separating hyperplane. The {\it Spherical}-CHM is a CHM, where $p=0$ and each point in $S$ has unit norm. First, we prove the equivalence of exact and approximate versions of CHM and Spherical-CHM. On the one hand, this makes it possible to state a simple version of the original TA. On the other hand, we prove that under the satisfiability of a simple condition in each iteration, the complexity improves to $O(1/\varepsilon)$. The analysis also suggests a strategy for when the property does not hold at an iterate. This suggests the \textit{Spherical-TA} which first converts a given CHM into a Spherical-CHM before applying the algorithm. Next we introduce a series of applications of Spherical-TA. In particular, Spherical-TA serves as a fast version of vanilla TA to boost its efficiency. As an example, this results in a fast version of \emph{AVTA} \cite{awasthi2018robust}, called \emph{AVTA$^+$} for solving exact or approximate irredundancy problem. Computationally, we have considered CHM, LP and Strict LP Feasibility and the Irredundancy problem. Based on substantial amount of computing, Spherical-TA achieves better efficiency than state of the art algorithms. Leveraging on the efficiency of Spherical-TA, we propose AVTA$^+$ as a pre-processing step for data reduction which arises in such applications as in computing the Minimum Volume Enclosing Ellipsoid \cite{moshtagh2005minimum}.