# Tight Running Time Lower Bounds for Strong Inapproximability of Maximum k-Coverage, Unique Set Cover and Related Problems (via t-Wise Agreement Testing Theorem)

arXiv: https://arxiv.org/abs/1910.11850 · HTML: https://mlsanity.com/paper/1910.11850/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Tight FPT Approximation for Socially Fair Clustering](https://mlsanity.com/paper/2106.06755/) (2021)
- [Johnson Coverage Hypothesis: Inapproximability of k-means and k-median in L_p metrics](https://mlsanity.com/paper/2111.10912/) (2021)
- [Tight FPT Approximation for Constrained k-Center and k-Supplier](https://mlsanity.com/paper/2110.14242/) (2021)
- [Diversity-aware clustering: Computational Complexity and Approximation Algorithms](https://mlsanity.com/paper/2401.05502/) (2024)
- [Fast decision tree learning solves hard coding-theoretic problems](https://mlsanity.com/paper/2409.13096/) (2024)

## Abstract (arXiv)

We show, assuming the (randomized) Gap Exponential Time Hypothesis (Gap-ETH), that the following tasks cannot be done in T(k) · No(k)-time for any function T where N denote the input size: - left(1 - frac1e + εright)-approximation for Max k-Coverage for any ε > 0, - left(1 + frac2e - εright)-approximation for k-Median (in general metrics) for any constant ε > 0. - left(1 + frac8e - εright)-approximation for k-Mean (in general metrics) for any constant ε > 0. - Any constant factor approximation for k-Unique Set Cover, k-Nearest Codeword Problem and k-Closest Vector Problem. - (1 + δ)-approximation for k-Minimum Distance Problem and k-Shortest Vector Problem for some δ > 0. Since these problems can be trivially solved in NO(k) time, our running time lower bounds are essentially tight. In terms of approximation ratios, Max k-Coverage is well-known to admit polynomial-time left(1 - frac1eright)-approximation algorithms, and, recently, it was shown that k-Median and k-Mean are approximable to within factors of left(1 + frac2eright) and left(1 + frac8eright) respectively in FPT time [Cohen-Addad et al. 2019]; hence, our inapproximability ratios are also tight for these three problems. For the remaining problems, no non-trivial FPT approximation algorithms are known. The starting point of all our hardness results mentioned above is the Label Cover problem (with projection constraints). We show that Label Cover cannot be approximated to within any constant factor in T(k) · No(k) time, where N and k denote the size of the input and the number of nodes on the side with the larger alphabet respectively. With this hardness, the above results follow immediately from known reductions...