Hardness of Dynamic Tree Edit Distance and Friends

TL;DR

This work establishes conditional lower bounds showing that dynamic updates do not yield speedups for several tree- and string-structured edit problems, including unweighted and weighted TED, RNA Folding, and Dyck ED. Using clique-gadget reductions from 3k- and 4k-Clique problems, the authors demonstrate that efficient dynamic TED (both unweighted and weighted) would imply breakthroughs in fine-grained hardness (e.g., rapid solutions to k-Clique variants), thus ruling out substantial dynamic improvements under standard conjectures. The results also draw connections to online OMv-type bounds, clarifying when dynamic problems can or cannot match known online or monotone-min-plus product techniques. Overall, the paper provides the first clear dynamic-separation results between TED and String ED, and between RNA/Dyck variants and their combinatorial counterparts, shaping our understanding of what dynamic updates can realistically achieve in these domains.

Abstract

String Edit Distance is a more-than-classical problem whose behavior in the dynamic setting, where the strings are updated over time, is well studied. A single-character substitution, insertion, or deletion can be processed in time $\tilde{\mathcal{O}}(n w)$ when operation costs are positive integers bounded by $w$ [Charalampopoulos, Kociumaka, Mozes, CPM 2020][Gorbachev, Kociumaka, STOC 2025]. If the weights are further uniform (insertions and deletions have equal cost), also an $\tilde{\mathcal{O}}(n \sqrt{n})$-update time algorithm exists [Charalampopoulos, Kociumaka, Mozes, CPM 2020]. This is a substantial improvement over the static $\mathcal{O}(n^2)$ algorithm when $w \ll n$ or when we are dealing with uniform weights. In contrast, for inherently related problems such as Tree Edit Distance, Dyck Edit Distance, and RNA Folding, it has remained unknown whether it is possible to devise dynamic algorithms with an advantage over the static algorithm. In this paper, we resolve this question by showing that (weighted) Tree Edit Distance, Dyck Edit Distance, and RNA Folding admit no dynamic speedup: under well-known fine-grained assumptions we show that the best possible algorithm recomputes the solution from scratch after each update. Furthermore, we prove a quadratic per-update lower bound for unweighted Tree Edit Distance under the $k$-Clique Conjecture. This provides the first separation between dynamic unweighted String Edit Distance and unweighted Tree Edit Distance, problems whose relative difficulty in the static setting is still open.

Figures (2)

• Figure 1: The trees $\mathbf{T}_\mathcal{X}(Z)$ and $\mathbf{T}_\mathcal{Y}'(Z)$ depicted on the left and right, respectively.
• Figure 2: The figure illustrates how to extend the TED instance from BGMW20 to obtain a dynamic bound. Nodes newly introduced in \ref{['fig:weighted:b']} are shown in full color, while those from \ref{['fig:weighted:a']} are rendered translucent.

Theorems & Definitions (28)

• lemma 1
• definition 1
• lemma 1: ABVW15
• lemma 2: C15
• lemma 3: Claim 4 in ABVW15
• lemma 4
• proof
• definition 2
• theorem 1
• proof