SearcharxivSearch

arXiv · 1604.06058

Succinct Choice Dictionaries

Abstract

The choice dictionary is introduced as a data structure that can be initialized with a parameter $n\in\mathbb{N}=\{1,2,\ldots\}$ and subsequently maintains an initially empty subset $S$ of $\{1,\ldots,n\}$ under insertion, deletion, membership queries and an operation choice that returns an arbitrary element of $S$. The choice dictionary appears to be fundamental in space-efficient computing. We show that there is a choice dictionary that can be initialized with $n$ and an additional parameter $t\in\mathbb{N}$ and subsequently occupies $n+O(n(t/w)^t+\log n)$ bits of memory and executes each of the four operations insert, delete, contains (i.e., a membership query) and choice in $O(t)$ time on a word RAM with a word length of $w=\Omega(\log n)$ bits. In particular, with $w=\Theta(\log n)$, we can support insert, delete, contains and choice in constant time using $n+O(n/(\log n)^t)$ bits for arbitrary fixed $t$. We extend our results to maintaining several pairwise disjoint subsets of $\{1,\ldots,n\}$. We study additional space-efficient data structures for subsets $S$ of $\{1,\ldots,n\}$, including one that supports only insertion and an operation extract-choice that returns and deletes an arbitrary element of $S$. All our main data structures can be initialized in constant time and support efficient iteration over the set $S$, and we can allow changes to $S$ while an iteration over $S$ is in progress. We use these abilities crucially in designing the most space-efficient algorithms known for solving a number of graph and other combinatorial problems in linear time. In particular, given an undirected graph $G$ with $n$ vertices and $m$ edges, we can output a spanning forest of $G$ in $O(n+m)$ time with at most $(1+\epsilon)n$ bits of working memory for arbitrary fixed $\epsilon>0$.

Explore related subjects

Keep this discovery

BibTeXRIS

Torben Hagerup, Frank Kammer. 2016-04-20. Succinct Choice Dictionaries. https://arxiv.org/abs/1604.06058

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