On the Complexity of the Geometric Median Problem with Outliers

TL;DR

This paper establishes the computational hardness of the Geometric Median problem with outliers in high dimensions, proving it is NP-hard, W[1]-hard, and does not admit efficient approximation schemes, even in different metric spaces.

Contribution

It proves the NP-hardness and related complexity results for the Geometric Median with outliers in general dimensions and different metric spaces, resolving an open question.

Findings

The problem is strongly NP-hard in high dimensions.

No FPTAS exists unless P=NP for the problem.

The problem is W[1]-hard with respect to the number of outliers.

Abstract

In the Geometric Median problem with outliers, we are given a finite set of points in d-dimensional real space and an integer m, the goal is to locate a new point in space (center) and choose m of the input points to minimize the sum of the Euclidean distances from the center to the chosen points. This problem can be solved "almost exactly" in polynomial time if d is fixed and admits an approximation scheme PTAS in high dimensions. However, the complexity of the problem was an open question. We prove that, if the dimension of space is not fixed, Geometric Median with outliers is strongly NP-hard, does not admit approximation schemes FPTAS unless P=NP, and is W[1]-hard with respect to the parameter m. The proof is done by a reduction from the Independent Set problem. Based on a similar reduction, we also get the NP-hardness of closely related geometric 2-clustering problems in which it is required to partition a given set of points into two balanced clusters minimizing the cost of median clustering. Finally, we study Geometric Median with outliers in $\ell_\infty$ space and prove the same complexity results as for the Euclidean problem.