Search arXiv⌕ Search

arXiv · 2611.05982

Single-Exponential FPT Algorithms for Minimum Bisection and Matching Cut via a New Unbreakable Decomposition

Abstract

Minimum Bisection asks whether the vertices of a graph can be partitioned into two parts whose sizes differ by at most one and with at most k edges between the two parts. Cygan, Lokshtanov, Marcin Pilipczuk, Michał Pilipczuk, and Saurabh (SICOMP 2019) gave the first fixed-parameter algorithm, which runs in O*(2^(O(k^3))) time, by introducing an unbreakable decomposition. Cygan, Komosa, Lokshtanov, Marcin Pilipczuk, Michał Pilipczuk, Saurabh, and Wahlström (TALG 2021) strengthened the decomposition and improved the running time to O*(2^(O(k log k))). However, whether the running time could be improved to single-exponential remained open. In this paper, we give a deterministic O*(2^(O(k)))-time algorithm for Minimum Bisection. Our algorithm also counts all feasible bisections. To obtain an O*(2^(O(k)))-time algorithm, we introduce two new techniques. First, we give a new algorithm that computes a (2k,k)-edge-unbreakable decomposition with adhesion at most 2k in O*(2^(O(k))) time by relaxing the unbreakability and adhesion guarantees of the decomposition in (TALG 2021). Second, we improve the running time of the dynamic program over the decomposition from O*(2^(O(k log k))) to O*(2^(O(k))) using important cuts, inclusion-exclusion, and formal logarithms and exponentials. Together with color coding, the same techniques give a deterministic O*(2^(O(k)))-time algorithm for Matching Cut, which asks for a cut whose crossing edges form a matching. This improves the O*(2^(O(k log k)))-time algorithm of Aravind and Saxena (IWOCA 2021).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Soh Kumabe. 2026-11-04. Single-Exponential FPT Algorithms for Minimum Bisection and Matching Cut via a New Unbreakable Decomposition. https://arxiv.org/abs/2611.05982

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Computing Ultrabubbles in Linear Time by Reduction to Weak Superbubbles in the Double Covering Graph

In recent years, in attempts to capture variation structures in genomic and pangenomic graphs, the zoo of bubbles has been filled with more and more different variants. The first more complex variant was the superbubble which was later (informally) generalised to bidirected graphs as the ultrabubble. Ultrabubbles are more applicable in bioinformatics research, because genome and pangenome graphs usually are bidirected. In this paper, we study the relationship between superbubbles and ultrabubbles, and show that ultrabubbles in a bidirected graph are equivalent to the weak superbubbles in the corresponding double covering graph. Using this, we introduce a new linear-time algorithm to enumerate all ultrabubbles that consists of a very simple reduction to employ an existing linear-time algorithm to enumerate all weak superbubbles. Treating the superbubble algorithm as a black box, our algorithm is vastly simpler than the SPQR-tree-based linear-time ultrabubble enumeration algorithm of Sena et al. [2026]. Further, since our algorithm works on all bidirected graphs, it is strictly more powerful than the simple graph-orientation-based algorithm by Harviainen et al. [2026], which only works on a restricted class of bidirected graphs. In order to prove our equivalence, we additionally introduce simplified definitions of both ultrabubbles and weak superbubbles, through which the equivalence becomes more intuitive. Finally, our reduction motivates that some algorithmic problems in bidirected graphs can be reduced to well-known algorithmic problems in directed graphs.

cs.DS↗

Subgaussian Vector Balancing by an Ising Model

We show that the guaranties of the Gram-Schmidt Walk Algorithm of Bansal, Dadush, Garg, and Lovett can be achieved by an Ising model. Given vectors $v_1, \ldots, v_n$ of norm less than 1, we give a closed form for a centrally symmetric distribution on a vector $σ\in \{\pm 1 \}^n$ so that the signed sum $\sum_j σ(j) v_j$ is $1$-subgaussian. We show that the Glauber dynamics for this model mix rapidly provided that the norms of the input vectors are bounded below 1 by an inverse-polynomial. The proofs employ the theory of Lorentzian polynomials, and we use these techniques to show fast mixing bounds for other spin systems.

cs.DS↗

No Escape from the Quadratic Barrier: Adaptive Attacks on Linear $\ell_2$ Sketches

Linear sketches estimate properties of high-dimensional inputs from a fixed low-dimensional projection. At constant accuracy, randomized Euclidean-norm sketches, notably the Johnson-Lindenstrauss transform, support exponentially many nonadaptive queries in sketch dimension $k$. For adaptive black-box queries, known privacy-based defenses guarantee $\widetildeΩ(k^2)$ and it remained open whether stronger guarantees were possible. Prior attacks used more queries, restricted the responder, or provided weaker failure guarantees. We show that the quadratic guarantee is optimal up to logarithmic factors, even with a computationally unbounded responder that knows the matrix, attack algorithm, history, and query distributions and may change its randomized rule after every query. For every fixed hidden $A\in\mathbb{R}^{k\times n}$ with $n>40k$ and target accuracy $α\in (0,1]$, our black-box attack uses $O(k+α^2k^2\log(2+α^2k))$ queries in $\widetilde{O}(1+α^2k)$ adaptive rounds to force, with constant probability, a round in which a constant fraction of answers have $Ω(α)$ relative error. This fraction can be taken arbitrarily close to one half. Our results clarify the limits of defending a fixed compressive linear representation by adapting the prediction rule.

cs.DS↗