# Approximate Polytope Membership Queries

arXiv: https://arxiv.org/abs/1604.01183 · HTML: https://mlsanity.com/paper/1604.01183/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2016.

## Built on by (most cited first, 2 of 2)

- [Improved Search of Relevant Points for Nearest-Neighbor Classification](https://mlsanity.com/paper/2203.03567/) (2022)
- [Truncated Log-concave Sampling with Reflective Hamiltonian Monte Carlo](https://mlsanity.com/paper/2102.13068/) (2021)

## Abstract (arXiv)

In the polytope membership problem, a convex polytope K in ℝd is given, and the objective is to preprocess K into a data structure so that, given any query point q ∈ ℝd, it is possible to determine efficiently whether q ∈ K. We consider this problem in an approximate setting. Given an approximation parameter varepsilon, the query can be answered either way if the distance from q to K's boundary is at most varepsilon times K's diameter. We assume that the dimension d is fixed, and K is presented as the intersection of n halfspaces. Previous solutions to approximate polytope membership were based on straightforward applications of classic polytope approximation techniques by Dudley (1974) and Bentley et al. (1982). The former is optimal in the worst-case with respect to space, and the latter is optimal with respect to query time. We present four main results. First, we show how to combine the two above techniques to obtain a simple space-time trade-off. Second, we present an algorithm that dramatically improves this trade-off. In particular, for any constant α ≥ 4, this data structure achieves query time O(1/varepsilon(d-1)/α) and space roughly O(1/varepsilon(d-1)(1 - O(log α)/α)). We do not know whether this space bound is tight, but our third result shows that there is a convex body such that our algorithm achieves a space of at least Omega( 1/varepsilon^(d-1)(1-O(√α)/α ). Our fourth result shows that it is possible to reduce approximate Euclidean nearest neighbor searching to approximate polytope membership queries. Combined with the above results, this provides significant improvements to the best known space-time trade-offs for approximate nearest neighbor searching in ℝd.