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Stephen Alstrup

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

18 papers
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18

TCS Journal 2022 Journal Article

Constructing light spanners deterministically in near-linear time

  • Stephen Alstrup
  • Søren Dahlgaard
  • Arnold Filtser
  • Morten Stöckel
  • Christian Wulff-Nilsen

Graph spanners are well-studied and widely used both in theory and practice. In a recent breakthrough, Chechik and Wulff-Nilsen [10] improved the state-of-the-art for light spanners by constructing a ( 2 k − 1 ) ( 1 + ε ) -spanner with O ( n 1 + 1 k ) edges and O ε ( n 1 k ) lightness. Soon after, Filtser and Solomon [18] showed that the classic greedy spanner construction achieves the same bounds. The major drawback of the greedy spanner is its running time of O ( m n 1 + 1 k ) (which is faster than [10]). This makes the construction impractical even for graphs of moderate size. Much faster spanner constructions do exist but they only achieve lightness Ω ε ( k n 1 k ), even when randomization is used. The contribution of this paper is deterministic spanner constructions that are fast, and achieve similar bounds as the state-of-the-art slower constructions. Our first result is an O ε ( n 2 + 1 k + ε ′ ) time spanner construction which achieves the state-of-the-art bounds. Our second result is an O ε ( m + n log ⁡ n ) time construction of a spanner with ( 2 k − 1 ) ( 1 + ε ) stretch, O ( log ⁡ k ⋅ n 1 + 1 k ) edges and O ε ( log ⁡ k ⋅ n 1 k ) lightness. This is an exponential improvement in the dependence on k compared to the previous result with such running time. Finally, for the important special case where k = log ⁡ n, for every constant ε > 0, we provide an O ( m + n 1 + ε ) time construction that produces an O ( log ⁡ n ) -spanner with O ( n ) edges and O ( 1 ) lightness which is asymptotically optimal. This is the first known sub-quadratic construction of such a spanner for any k = ω ( 1 ). To achieve our constructions, we show a novel deterministic incremental approximate distance oracle. Our new oracle is crucial in our construction, as known randomized dynamic oracles require the assumption of a non-adaptive adversary. This is a strong assumption, which has seen recent attention in prolific venues. Our new oracle allows the order of the edge insertions to not be fixed in advance, which is critical as our spanner algorithm chooses which edges to insert based on the answers to distance queries. We believe our new oracle is of independent interest.

SODA Conference 2018 Conference Paper

A Hamiltonian Cycle in the Square of a 2-connected Graph in Linear Time

  • Stephen Alstrup
  • Agelos Georgakopoulos
  • Eva Rotenberg
  • Carsten Thomassen

Fleischner's theorem says that the square of every 2-connected graph contains a Hamiltonian cycle. We present a proof resulting in an O (| E |) algorithm for producing a Hamiltonian cycle in the square G 2 of a 2-connected graph G = ( V, E ). The previous best was O (| V | 2 ) by Lau in 1980. More generally, we get an O (| E |) algorithm for producing a Hamiltonian path between any two prescribed vertices, and we get an O (| V | 2 ) algorithm for producing cycles C 3, C 4, …, C | V | in G 2 of lengths 3, 4, …, | V |, respectively.

SODA Conference 2016 Conference Paper

Simpler, faster and shorter labels for distances in graphs

  • Stephen Alstrup
  • Cyril Gavoille
  • Esben Bistrup Halvorsen
  • Holger Petersen 0001

We consider how to assign labels to any undirected graph with n nodes such that, given the labels of two nodes and no other information regarding the graph, it is possible to determine the distance between the two nodes. The challenge in such a distance labeling scheme is primarily to minimize the maximum label length and secondarily to minimize the time needed to answer distance queries (decoding). Previous schemes have offered different tradeoffs between label lengths and query time. This paper presents a simple algorithm with shorter labels and shorter query time than any previous solution, thereby improving the state-of-the-art with respect to both label length and query time in one single algorithm. Our solution addresses several open problems concerning label length and decoding time and is the first improvement of label length for more than three decades. More specifically, we present a distance labeling scheme with labels of length bits 1 and constant decoding time. This outperforms all existing results with respect to both size and decoding time, including Winkler's (Combinatorica 1983) decade-old result, which uses labels of size (log 3) n and O ( n /log n ) decoding time, and Gavoille et al. (SODA'01), which uses labels of size 11 n + o ( n ) and O (log log n ) decoding time. In addition, our algorithm is simpler than the previous ones. In the case of integral edge weights of size at most W, we present almost matching upper and lower bounds for the label size. Furthermore, for r -additive approximation labeling schemes, where distances can be off by up to an additive constant r, we present both upper and lower bounds. In particular, we present an upper bound for 1-additive approximation schemes which, in the unweighted case, has the same size (ignoring second order terms) as an adjacency labeling scheme, namely n /2. We also give results for bipartite graphs as well as for exact and 1-additive distance oracles.

STOC Conference 2015 Conference Paper

Adjacency Labeling Schemes and Induced-Universal Graphs

  • Stephen Alstrup
  • Haim Kaplan
  • Mikkel Thorup
  • Uri Zwick

We describe a way of assigning labels to the vertices of any undirected graph on up to n vertices, each composed of n/2+O(1) bits, such that given the labels of two vertices, and no other information regarding the graph, it is possible to decide whether or not the vertices are adjacent in the graph. This is optimal, up to an additive constant, and constitutes the first improvement in almost 50 years of an n/2+O(log n) bound of Moon. As a consequence, we obtain an induced-universal graph for n-vertex graphs containing only O(2 n/2 ) vertices, which is optimal up to a multiplicative constant, solving an open problem of Vizing from 1968. We obtain similar tight results for directed graphs, tournaments and bipartite graphs.

FOCS Conference 2015 Conference Paper

Optimal Induced Universal Graphs and Adjacency Labeling for Trees

  • Stephen Alstrup
  • Søren Dahlgaard
  • Mathias Bæk Tejs Knudsen

We show that there exists a graph G with O(n) nodes, where any forest of n nodes is a node-induced sub graph of G. Furthermore, for constant arboricity k, the result implies the existence of a graph with O(nk) nodes that contains all n-node graphs as node-induced sub graphs, matching a Ω(nk) lower bound. The lower bound and previously best upper bounds were presented in Alstrup and Rauhe (FOCS'02). Our upper bounds are obtained through a log2(n) + O(1) labeling scheme for adjacency queries in forests. We hereby solve an open problem being raised repeatedly over decades, e. g. In Kannan, Naor, Rudich (STOC 1988), Chung (J. Of Graph Theory 1990), Fraigniaud and Korman (SODA 2010).

SODA Conference 2014 Conference Paper

Near-optimal labeling schemes for nearest common ancestors

  • Stephen Alstrup
  • Esben Bistrup Halvorsen
  • Kasper Green Larsen

We consider NCA labeling schemes: given a rooted tree T, label the nodes of T with binary strings such that, given the labels of any two nodes, one can determine, by looking only at the labels, the label of their nearest common ancestor. For trees with n nodes we present upper and lower bounds establishing that labels of size (2 ± ∊)log n, ∊ < 1 are both sufficient and necessary. Alstrup, Bille, and Rauhe (SIDMA'05) showed that ancestor and NCA labeling schemes have labels of size log n + Ω(log log n ). Our lower bound increases this to log n +Ω(log n ) for NCA labeling schemes. Since Fraigniaud and Korman (STOC'10) established that labels in ancestor labeling schemes have size log n +Θ(loglog n ), our new lower bound separates ancestor and NCA labeling schemes. Our upper bound improves the 10log n upper bound by Alstrup, Gavoille, Kaplan and Rauhe (TOCS'04), and our theoretical result even outperforms some recent experimental studies by Fischer (ESA'09) where variants of the same NCA labeling scheme are shown to all have labels of size approximately 8 log n.

I&C Journal 2004 Journal Article

Dynamic nested brackets

  • Stephen Alstrup
  • Thore Husfeldt
  • Theis Rauhe

We consider the problem of maintaining a string of n brackets `('or `)' under the operation reverse(i) that changes the ith bracket from `(' to `)' or vice versa, and returns `yes' if and only if the resulting string is properly balanced. We show that this problem can be solved on the RAM in time O(logn/loglogn) per operation using linear space and preprocessing. Moreover, we show that this is optimal in the sense that every data structure supporting reverse (no matter its space and preprocessing complexity) needs time Ω(logn/loglogn) per operation in the cell probe model.

FOCS Conference 2002 Conference Paper

Small Induced-Universal Graphs and Compact Implicit Graph Representations

  • Stephen Alstrup
  • Theis Rauhe

We show that there exists a graph G with n /spl middot/ 2/sup O(log* n)/ nodes, where any forest with n nodes is a node-induced subgraph of G. Furthermore, the result implies the existence of a graph with n/sup k/2/sup O(log* n)/ nodes that contains all n-node graphs of fixed arboricity k as node-induced subgraphs. We provide a lower bound of /spl Omega/(n/sup k/) for the size of such a graph. The upper bound is obtained through a simple labeling scheme for parent queries in rooted trees.

STOC Conference 2001 Conference Paper

Optimal static range reporting in one dimension

  • Stephen Alstrup
  • Gerth Stølting Brodal
  • Theis Rauhe

We consider static one dimensional range searching problems. These problems are to build static data structures for an integer set S \subseteq U , where U = \{0,1,\dots,2^ w -1\}, which support various queries for integer intervals of U . For the query of reporting all integers in S contained within a query interval, we present an optimal data structure with linear space cost and with query time linear in the number of integers reported. This result holds in the unit cost RAM model with word size w and a standard instruction set. We also present a linear space data structure for approximate range counting. A range counting query for an interval returns the number of integers in S contained within the interval. For any constant ε>0, our range counting data structure returns in constant time an approximate answer which is within a factor of at most 1+ε of the correct answer.

FOCS Conference 2000 Conference Paper

New Data Structures for Orthogonal Range Searching

  • Stephen Alstrup
  • Gerth Stølting Brodal
  • Theis Rauhe

We present new general techniques for static orthogonal range searching problems in two and higher dimensions. For the general range reporting problem in R/sup 3/, we achieve query time O(log n+k) using space O(n log/sup 1+/spl epsiv// n), where n denotes the number of stored points and k the number of points to be reported. For the range reporting problem on an n/spl times/n grid, we achieve query time O(log log n+k) using space O(n log/sup /spl epsiv// n). For the two-dimensional semi-group range sum problem we achieve query time O(log n) using space O(n log n).

FOCS Conference 1998 Conference Paper

Marked Ancestor Problems

  • Stephen Alstrup
  • Thore Husfeldt
  • Theis Rauhe

Consider a rooted tree whose nodes can be in two states: marked or unmarked. The marked ancestor problem is to maintain a data structure with the following operations: mark(v) marks node v: unmark(v) removes any marks from node v; firstmarked(v) returns the first marked node on the path from v to the root. We show tight upper and lower bounds for the marked ancestor problem. The lower bounds are proved in the cell probe model, the algorithms run on a unit-cost RAM. As easy corollaries we prove (often optimal) lower bounds on a number of problems. These include planar range searching, including the existential or emptiness problem, priority search trees static tree union-find, and several problems from dynamic computational geometry, including segment intersection, interval maintenance, and ray shooting in the plane. Our upper bounds improve algorithms from various fields, including coloured ancestor problems and maintenance of balanced parentheses.

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