Improved Bounds for Fully Dynamic Matching via Ordered Ruzsa-Szemeredi Graphs

Abstract

In a very recent breakthrough, Behnezhad and Ghafari [FOCS'24] developed a novel fully dynamic randomized algorithm for maintaining a $(1-\hat{I}\mu )$-approximation of maximum matching with amortized update time potentially much better than the trivial $O(n)$ update time. The runtime of the BG algorithm is parameterized via the following graph theoretical concept: * For any $n$, define $ORS(n)$ -- standing for Ordered RS Graph -- to be the largest number of edge-disjoint matchings $M_1,\dots ,M_t$ of size $\hat{I}˜(n)$ in an $n$-vertex graph such that for every $i\in [t]$, $M_i$ is an induced matching in the subgraph $M_i\cup M_{i+1}\cup \dots \cup M_t$. Then, for any fixed $\hat{I}\mu >0$, the BG algorithm runs in \[ O\left( \sqrt{n^{1+O(ε)} \cdot ORS(n)} \right) \] amortized update time with high probability, even against an adaptive adversary. $ORS(n)$ is a close variant of a more well-known quantity regarding RS graphs (which require every matching to be induced regardless of the ordering). It is currently only known that $n^{o(1)}⩽ORS(n)⩽n^{1-o(1)}$, and closing this gap appears to be a notoriously challenging problem. In this work, we further strengthen the result of Behnezhad and Ghafari and push it to limit to obtain a randomized algorithm with amortized update time of \[ n^{o(1)} \cdot ORS(n) \] with high probability, even against an adaptive adversary. In the limit, i.e., if current lower bounds for $ORS(n)=n^{o(1)}$ are almost optimal, our algorithm achieves an $n^{o(1)}$ update time for $(1-\hat{I}\mu )$-approximation of maximum matching, almost fully resolving this fundamental question. In its current stage also, this fully reduces the algorithmic problem of designing dynamic matching algorithms to a purely combinatorial problem of upper bounding $ORS(n)$ with no algorithmic considerations.

24 pages, 2 figures. In SODA 2025