Search arXiv⌕ Search

arXiv · 2610.29184

Optimal Labeling Schemes for $Γ$-Free Matrices

Abstract

We study labeling schemes for $p \times q$ $Γ$-free binary matrices, which do not contain a submatrix of the form $Γ= \begin{pmatrix} 1 & 1 \\ 1 & 0 \end{pmatrix}$. We give a labeling scheme that uses $O(\log^2 N)$-bit labels for rows and columns, where $N=p+q$, such that any matrix entry can be determined from the corresponding two labels in constant time in the word RAM model. The worst-case label size matches the counting lower bound up to constant factors. We also show that the entrywise least $Γ$-free supermatrix of an arbitrary $p\times q$ binary matrix, together with its labels, can be constructed in $O(pq)$ time. Our construction represents a $Γ$-free matrix by a sequence of rooted forests, where each column corresponds to a node and the $1$-entries of each row form a root path in its corresponding forest. For $n$-vertex strongly chordal and chordal bipartite graphs, our result gives asymptotically optimal $O(\log^2 n)$-bit adjacency labeling schemes with constant-time queries. These results answer both representation questions posed by Spinrad in 1995.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sankardeep Chakraborty, Seungbum Jo. 2026-10-21. Optimal Labeling Schemes for $Γ$-Free Matrices. https://arxiv.org/abs/2610.29184

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

KEEP EXPLORING

Related papers

Compressed Set Representations based on Set Difference

We introduce a compressed representation of sets of sets that exploits how similar they are to each other. Our representation supports access, membership, predecessor and successor queries on the sets within logarithmic time. It also computes the support of any element (i.e., the sets containing it) in optimal time. In addition, we give a new MST-based construction algorithm for the representation that outperforms standard ones.

cs.DS↗

Fast Approximation Algorithms for Balanced Edge and Vertex Separators

We present a family of fast pseudo-approximation algorithms for the minimum balanced element cut problem in a graph, which includes vertex and edge separators. Given a graph $G=(V,E)$ with polynomially-bounded finite integer element weights with $n$ vertices and $m$ edges, and a (constant) balance parameter $c\in(0,1/2)$, where $G$ has some (unknown) $c$-balanced element cut of size $OPT_c$, we give a (Monte-Carlo randomized) algorithm running in $O(n^{\varepsilon}m^{1+o(1)})$ time that produces a $Θ(1)$-balanced element cut of size $O(OPT_c\cdot\sqrt{(\log n)/\varepsilon})$ for any value $\varepsilon\in[Θ(1/\log(n)),Θ(1)]$. Our algorithms are based on a semidefinite programming (SDP) relaxation of the problem. A standard approach to solving an SDP relaxation for this kind of problem would use the primal-dual Matrix Multiplicative Weights Update framework of Arora and Kale, but this framework does not seem to apply, since we cannot express all of our SDPs primal variables explicitly. Instead, we present a new MMWU framework for our problem that allows us to represent some of our variables implicitly. We also extend our framework to a general "Partial-variable MMWU framework", which we expect will be useful for future work. Our oracle for the balanced element cut problem within our framework uses $O(n^{\varepsilon}\text{polylog}(n))$ almost-linear time maximum-flow computations, and would be sped up if the time complexity of maximum-flow improves. For graphs with padding bound $β$, another oracle gives an $O(\sqrtβ/\varepsilon)$ approximation in $m^{1+o(1)}n^{\varepsilon}\operatorname{polylog}(n)$ time. It is known that $β\le O(\log h)$ for $K_h$-minor-free graphs (and in particular $β\le O(\log n)$).

cs.DS↗

Optimal-Time Move Structure Construction

The move structure represents a permutation $π$ of $[0,n)$ by partitioning the domain into $O(r)$ disjoint, contiguously permuted intervals, with $r$ being the minimum number of such intervals. This data structure occupies $O(r)$ words of space and enables $O(1)$-time computation of $π(i)$ given the interval that contains $i$. For permutations where $r \ll n$, this provides an efficient, compressed representation for navigation. While the existing best $O(r)$-space construction approaches require $O(r\log r)$-time, we present an optimal $O(r)$-time and space construction algorithm. This is achieved by replacing balanced search trees with pointer-based lists through introducing a bidirectional strategy that synchronizes construction of the structures for $π$ and its inverse $π^{-1}$ in a single, unified pass. By applying this algorithm to an existing result, we achieve the first optimal $O(n)$-time construction of the longest common prefix (LCP) array from a run-length-encoded Burrows-Wheeler transform (RLBWT) of $r$ runs in $O(r)$ working space. Empirical evaluation on pangenome-scale data confirms that our move structure construction algorithm is consistently faster than the previous best, achieving speedups of up to $\sim 2\times$ while using less memory.

cs.DS↗