Treedepth Inapproximability and Exponential ETH Lower Bound

TL;DR

This paper proves that approximating treedepth within a tiny factor is NP-hard and that exact computation requires exponential time, establishing strong complexity lower bounds under ETH.

Contribution

It introduces new hardness results for treedepth approximation and exact computation, including NP-hardness of 1.0003-approximation and exponential ETH lower bounds.

Findings

1.0003-approximation is NP-hard

Exact treedepth computation requires 2^{Ω(n)} time

Any (1+δ)-approximation needs 2^{Ω(n / log^c n)} time

Abstract

Treedepth is a central parameter to algorithmic graph theory. The current state-of-the-art in computing and approximating treedepth consists of a $2^{O(k^2)} n$-time exact algorithm and a polynomial-time $O(\text{OPT} \log^{3/2} \text{OPT})$-approximation algorithm, where the former algorithm returns an elimination forest of height $k$ (witnessing that treedepth is at most $k$) for the $n$-vertex input graph $G$, or correctly reports that $G$ has treedepth larger than $k$, and $\text{OPT}$ is the actual value of the treedepth. On the complexity side, exactly computing treedepth is NP-complete, but the known reductions do not rule out a polynomial-time approximation scheme (PTAS), and under the Exponential Time Hypothesis (ETH) only exclude a running time of $2^{o(\sqrt n)}$ for exact algorithms. We show that 1.0003-approximating treedepth is NP-hard, and that exactly computing the treedepth of an $n$-vertex graph requires time $2^{\Omega(n)}$, unless the ETH fails. We further derive that there exist absolute constants $\delta, c > 0$ such that any $(1+\delta)$-approximation algorithm requires time $2^{\Omega(n / \log^c n)}$. We do so via a simple direct reduction from Satisfiability to Treedepth, inspired by a reduction recently designed for Treewidth [STOC '25].