# Metric random matchings with applications

## TL;DR

This paper studies metric random matchings and introduces algorithms for approximating various distance-based problems in metric spaces, providing probabilistic guarantees and efficient runtimes.

## Contribution

It presents new randomized algorithms for approximating minimum sum of distances, average distances, and related problems in metric spaces with provable guarantees.

## Key findings

- Efficient algorithms for approximate solutions to minimum sum distance problem.
- Probabilistic approximation of average distances within specified factors.
- Fast approximation of average distances in graph metrics with high probability.

## Abstract

Let $(\{1,2,\ldots,n\},d)$ be a metric space. We analyze the expected value and the variance of $\sum_{i=1}^{\lfloor n/2\rfloor}\,d({\boldsymbol{\pi}}(2i-1),{\boldsymbol{\pi}}(2i))$ for a uniformly random permutation ${\boldsymbol{\pi}}$ of $\{1,2,\ldots,n\}$, leading to the following results: (I) Consider the problem of finding a point in $\{1,2,\ldots,n\}$ with the minimum sum of distances to all points. We show that this problem has a randomized algorithm that (1) always outputs a $(2+\epsilon)$-approximate solution in expected $O(n/\epsilon^2)$ time and that (2) inherits Indyk's~\cite{Ind99, Ind00} algorithm to output a $(1+\epsilon)$-approximate solution in $O(n/\epsilon^2)$ time with probability $\Omega(1)$, where $\epsilon\in(0,1)$. (II) The average distance in $(\{1,2,\ldots,n\},d)$ can be approximated in $O(n/\epsilon)$ time to within a multiplicative factor in $[\,1/2-\epsilon,1\,]$ with probability $1/2+\Omega(1)$, where $\epsilon>0$. (III) Assume $d$ to be a graph metric. Then the average distance in $(\{1,2,\ldots,n\},d)$ can be approximated in $O(n)$ time to within a multiplicative factor in $[\,1-\epsilon,1+\epsilon\,]$ with probability $1/2+\Omega(1)$, where $\epsilon=\omega(1/n^{1/4})$.