SearcharxivSearch

arXiv · 1401.2538

Linear-Time Compression of Bounded-Genus Graphs into Information-Theoretically Optimal Number of Bits

Abstract

A $\textit{compression scheme}$ $A$ for a class $\mathbb{G}$ of graphs consists of an encoding algorithm $\textit{Encode}_A$ that computes a binary string $\textit{Code}_A(G)$ for any given graph $G$ in $\mathbb{G}$ and a decoding algorithm $\textit{Decode}_A$ that recovers $G$ from $\textit{Code}_A(G)$. A compression scheme $A$ for $\mathbb{G}$ is $\textit{optimal}$ if both $\textit{Encode}_A$ and $\textit{Decode}_A$ run in linear time and the number of bits of $\textit{Code}_A(G)$ for any $n$-node graph $G$ in $\mathbb{G}$ is information-theoretically optimal to within lower-order terms. Trees and plane triangulations were the only known nontrivial graph classes that admit optimal compression schemes. Based upon Goodrich's separator decomposition for planar graphs and Djidjev and Venkatesan's planarizers for bounded-genus graphs, we give an optimal compression scheme for any hereditary (i.e., closed under taking subgraphs) class $\mathbb{G}$ under the premise that any $n$-node graph of $\mathbb{G}$ to be encoded comes with a genus-$o(\frac{n}{\log^2 n})$ embedding. By Mohar's linear-time algorithm that embeds a bounded-genus graph on a genus-$O(1)$ surface, our result implies that any hereditary class of genus-$O(1)$ graphs admits an optimal compression scheme. For instance, our result yields the first-known optimal compression schemes for planar graphs, plane graphs, graphs embedded on genus-$1$ surfaces, graphs with genus $2$ or less, $3$-colorable directed plane graphs, $4$-outerplanar graphs, and forests with degree at most $5$. For non-hereditary graph classes, we also give a methodology for obtaining optimal compression schemes. From this methodology, we give the first known optimal compression schemes for triangulations of genus-$O(1)$ surfaces and floorplans.

Explore related subjects

Keep this discovery

BibTeXRIS

Hsueh-I Lu. 2014-01-11. Linear-Time Compression of Bounded-Genus Graphs into Information-Theoretically Optimal Number of Bits. https://doi.org/10.1137/120879142

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

KEEP EXPLORING

Related papers

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS