arXiv subjects

Mathias Bæk Tejs Knudsen

Publications and source records attributed to Mathias Bæk Tejs Knudsen.

18 recordsLinked to original sources

arXiv2018

Maximal Unbordered Factors of Random Strings

A border of a string is a non-empty prefix of the string that is also a suffix of the string, and a string is unbordered if it has no border other than itself. Loptev, Kucherov, and Starikovskaya [CPM 2015] conjectured the following: If we pick a string of length $n$ from a fixed non-unary alphabet uniformly at random, then the expected maximum length of its unbordered factors is $n - O(1)$. We confirm this conjecture by proving that the expected value is, in fact, ${n - Θ(σ^{-1})}$, where $σ$ is the size of the alphabet. This immediately implies that we can find such a maximal unbordered factor in linear time on average. However, we go further and show that the optimum average-case running time is in $Ω(\sqrt{n}) \cap O (\sqrt{n \log_σn})$ due to analogous bounds by Czumaj and Gąsieniec [CPM 2000] for the problem of computing the shortest period of a uniformly random string.

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arXiv2018

Power of $d$ Choices with Simple Tabulation

Suppose that we are to place $m$ balls into $n$ bins sequentially using the $d$-choice paradigm: For each ball we are given a choice of $d$ bins, according to $d$ hash functions $h_1,\dots,h_d$ and we place the ball in the least loaded of these bins breaking ties arbitrarily. Our interest is in the number of balls in the fullest bin after all $m$ balls have been placed. Azar et al. [STOC'94] proved that when $m=O(n)$ and when the hash functions are fully random the maximum load is at most $\frac{\lg \lg n }{\lg d}+O(1)$ whp (i.e. with probability $1-O(n^{-γ})$ for any choice of $γ$). In this paper we suppose that the $h_1,\dots,h_d$ are simple tabulation hash functions. Generalising a result by Dahlgaard et al [SODA'16] we show that for an arbitrary constant $d\geq 2$ the maximum load is $O(\lg \lg n)$ whp, and that expected maximum load is at most $\frac{\lg \lg n}{\lg d}+O(1)$. We further show that by using a simple tie-breaking algorithm introduced by Vöcking [J.ACM'03] the expected maximum load drops to $\frac{\lg \lg n}{d\lg φ_d}+O(1)$ where $φ_d$ is the rate of growth of the $d$-ary Fibonacci numbers. Both of these expected bounds match those of the fully random setting. The analysis by Dahlgaard et al. relies on a proof by Pătraşcu and Thorup [J.ACM'11] concerning the use of simple tabulation for cuckoo hashing. We need here a generalisation to $d>2$ hash functions, but the original proof is an 8-page tour de force of ad-hoc arguments that do not appear to generalise. Our main technical contribution is a shorter, simpler and more accessible proof of the result by Pătraşcu and Thorup, where the relevant parts generalise nicely to the analysis of $d$ choices.

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arXiv2017

Practical Hash Functions for Similarity Estimation and Dimensionality Reduction

Hashing is a basic tool for dimensionality reduction employed in several aspects of machine learning. However, the perfomance analysis is often carried out under the abstract assumption that a truly random unit cost hash function is used, without concern for which concrete hash function is employed. The concrete hash function may work fine on sufficiently random input. The question is if it can be trusted in the real world when faced with more structured input. In this paper we focus on two prominent applications of hashing, namely similarity estimation with the one permutation hashing (OPH) scheme of Li et al. [NIPS'12] and feature hashing (FH) of Weinberger et al. [ICML'09], both of which have found numerous applications, i.e. in approximate near-neighbour search with LSH and large-scale classification with SVM. We consider mixed tabulation hashing of Dahlgaard et al.[FOCS'15] which was proved to perform like a truly random hash function in many applications, including OPH. Here we first show improved concentration bounds for FH with truly random hashing and then argue that mixed tabulation performs similar for sparse input. Our main contribution, however, is an experimental comparison of different hashing schemes when used inside FH, OPH, and LSH. We find that mixed tabulation hashing is almost as fast as the multiply-mod-prime scheme ax+b mod p. Mutiply-mod-prime is guaranteed to work well on sufficiently random data, but we demonstrate that in the above applications, it can lead to bias and poor concentration on both real-world and synthetic data. We also compare with the popular MurmurHash3, which has no proven guarantees. Mixed tabulation and MurmurHash3 both perform similar to truly random hashing in our experiments. However, mixed tabulation is 40% faster than MurmurHash3, and it has the proven guarantee of good performance on all possible input.

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arXiv2017

Linear Hashing is Awesome

We consider the hash function $h(x) = ((ax+b) \bmod p) \bmod n$ where $a,b$ are chosen uniformly at random from $\{0,1,\ldots,p-1\}$. We prove that when we use $h(x)$ in hashing with chaining to insert $n$ elements into a table of size $n$ the expected length of the longest chain is $\tilde{O}\!\left(n^{1/3}\right)$. The proof also generalises to give the same bound when we use the multiply-shift hash function by Dietzfelbinger et al. [Journal of Algorithms 1997].

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arXiv2017

Additive Spanners and Distance Oracles in Quadratic Time

Let $G$ be an unweighted, undirected graph. An additive $k$-spanner of $G$ is a subgraph $H$ that approximates all distances between pairs of nodes up to an additive error of $+k$, that is, it satisfies $d_H(u,v) \le d_G(u,v)+k$ for all nodes $u,v$, where $d$ is the shortest path distance. We give a deterministic algorithm that constructs an additive $O\!\left(1\right)$-spanner with $O\!\left(n^{4/3}\right)$ edges in $O\!\left(n^2\right)$ time. This should be compared with the randomized Monte Carlo algorithm by Woodruff [ICALP 2010] giving an additive $6$-spanner with $O\!\left(n^{4/3}\log^3 n\right)$ edges in expected time $O\!\left(n^2\log^2 n\right)$. An $(α,β)$-approximate distance oracle for $G$ is a data structure that supports the following distance queries between pairs of nodes in $G$. Given two nodes $u$, $v$ it can in constant time compute a distance estimate $\tilde{d}$ that satisfies $d \le \tilde{d} \le αd + β$ where $d$ is the distance between $u$ and $v$ in $G$. Sommer [ICALP 2016] gave a randomized Monte Carlo $(2,1)$-distance oracle of size $O\!\left(n^{5/3}\text{poly} \log n\right)$ in expected time $O\!\left(n^2\text{poly} \log n\right)$. As an application of the additive $O(1)$-spanner we improve the construction by Sommer [ICALP 2016] and give a Las Vegas $(2,1)$-distance oracle of size $O\!\left(n^{5/3}\right)$ in time $O\!\left(n^2\right)$. This also implies an algorithm that in $O\!\left(n^2\right)$ gives approximate distance for all pairs of nodes in $G$ improving on the $O\!\left(n^2 \log n\right)$ algorithm by Baswana and Kavitha [SICOMP 2010].

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arXiv2017

The Entropy of Backwards Analysis

Backwards analysis, first popularized by Seidel, is often the simplest most elegant way of analyzing a randomized algorithm. It applies to incremental algorithms where elements are added incrementally, following some random permutation, e.g., incremental Delauney triangulation of a pointset, where points are added one by one, and where we always maintain the Delauney triangulation of the points added thus far. For backwards analysis, we think of the permutation as generated backwards, implying that the $i$th point in the permutation is picked uniformly at random from the $i$ points not picked yet in the backwards direction. Backwards analysis has also been applied elegantly by Chan to the randomized linear time minimum spanning tree algorithm of Karger, Klein, and Tarjan. The question considered in this paper is how much randomness we need in order to trust the expected bounds obtained using backwards analysis, exactly and approximately. For the exact case, it turns out that a random permutation works if and only if it is minwise, that is, for any given subset, each element has the same chance of being first. Minwise permutations are known to have $Θ(n)$ entropy, and this is then also what we need for exact backwards analysis. However, when it comes to approximation, the two concepts diverge dramatically. To get backwards analysis to hold within a factor $α$, the random permutation needs entropy $Ω(n/α)$. This contrasts with minwise permutations, where it is known that a $1+\varepsilon$ approximation only needs $Θ(\log (n/\varepsilon))$ entropy. Our negative result for backwards analysis essentially shows that it is as abstract as any analysis based on full randomness.

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arXiv2017

New Subquadratic Approximation Algorithms for the Girth

We consider the problem of approximating the girth, $g$, of an unweighted and undirected graph $G=(V,E)$ with $n$ nodes and $m$ edges. A seminal result of Itai and Rodeh [SICOMP'78] gave an additive $1$-approximation in $O(n^2)$ time, and the main open question is thus how well we can do in subquadratic time. In this paper we present two main results. The first is a $(1+\varepsilon,O(1))$-approximation in truly subquadratic time. Specifically, for any $k\ge 2$ our algorithm returns a cycle of length $2\lceil g/2\rceil+2\left\lceil\frac{g}{2(k-1)}\right\rceil$ in $\tilde{O}(n^{2-1/k})$ time. This generalizes the results of Lingas and Lundell [IPL'09] who showed it for the special case of $k=2$ and Roditty and Vassilevska Williams [SODA'12] who showed it for $k=3$. Our second result is to present an $O(1)$-approximation running in $O(n^{1+\varepsilon})$ time for any $\varepsilon > 0$. Prior to this work the fastest constant-factor approximation was the $\tilde{O}(n^{3/2})$ time $8/3$-approximation of Lingas and Lundell [IPL'09] using the algorithm corresponding to the special case $k=2$ of our first result.

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arXiv2017

Finding Even Cycles Faster via Capped k-Walks

In this paper, we consider the problem of finding a cycle of length $2k$ (a $C_{2k}$) in an undirected graph $G$ with $n$ nodes and $m$ edges for constant $k\ge2$. A classic result by Bondy and Simonovits [J.Comb.Th.'74] implies that if $m \ge100k n^{1+1/k}$, then $G$ contains a $C_{2k}$, further implying that one needs to consider only graphs with $m = O(n^{1+1/k})$. Previously the best known algorithms were an $O(n^2)$ algorithm due to Yuster and Zwick [J.Disc.Math'97] as well as a $O(m^{2-(1+\lceil k/2\rceil^{-1})/(k+1)})$ algorithm by Alon et al. [Algorithmica'97]. We present an algorithm that uses $O(m^{2k/(k+1)})$ time and finds a $C_{2k}$ if one exists. This bound is $O(n^2)$ exactly when $m=Θ(n^{1+1/k})$. For $4$-cycles our new bound coincides with Alon et al., while for every $k>2$ our bound yields a polynomial improvement in $m$. Yuster and Zwick noted that it is "plausible to conjecture that $O(n^2)$ is the best possible bound in terms of $n$". We show "conditional optimality": if this hypothesis holds then our $O(m^{2k/(k+1)})$ algorithm is tight as well. Furthermore, a folklore reduction implies that no combinatorial algorithm can determine if a graph contains a $6$-cycle in time $O(m^{3/2-ε})$ for any $ε>0$ under the widely believed combinatorial BMM conjecture. Coupled with our main result, this gives tight bounds for finding $6$-cycles combinatorially and also separates the complexity of finding $4$- and $6$-cycles giving evidence that the exponent of $m$ in the running time should indeed increase with $k$. The key ingredient in our algorithm is a new notion of capped $k$-walks, which are walks of length $k$ that visit only nodes according to a fixed ordering. Our main technical contribution is an involved analysis proving several properties of such walks which may be of independent interest.

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arXiv2016

Sublinear Distance Labeling

A distance labeling scheme labels the $n$ nodes of a graph with binary strings such that, given the labels of any two nodes, one can determine the distance in the graph between the two nodes by looking only at the labels. A $D$-preserving distance labeling scheme only returns precise distances between pairs of nodes that are at distance at least $D$ from each other. In this paper we consider distance labeling schemes for the classical case of unweighted graphs with both directed and undirected edges. We present a $O(\frac{n}{D}\log^2 D)$ bit $D$-preserving distance labeling scheme, improving the previous bound by Bollobás et. al. [SIAM J. Discrete Math. 2005]. We also give an almost matching lower bound of $Ω(\frac{n}{D})$. With our $D$-preserving distance labeling scheme as a building block, we additionally achieve the following results: 1. We present the first distance labeling scheme of size $o(n)$ for sparse graphs (and hence bounded degree graphs). This addresses an open problem by Gavoille et. al. [J. Algo. 2004], hereby separating the complexity from distance labeling in general graphs which require $Ω(n)$ bits, Moon [Proc. of Glasgow Math. Association 1965]. 2. For approximate $r$-additive labeling schemes, that return distances within an additive error of $r$ we show a scheme of size $O\left ( \frac{n}{r} \cdot\frac{\operatorname{polylog} (r\log n)}{\log n} \right )$ for $r \ge 2$. This improves on the current best bound of $O\left(\frac{n}{r}\right)$ by Alstrup et. al. [SODA 2016] for sub-polynomial $r$, and is a generalization of a result by Gawrychowski et al. [arXiv preprint 2015] who showed this for $r=2$.

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arXiv2016

Near-Optimal Induced Universal Graphs for Bounded Degree Graphs

A graph $U$ is an induced universal graph for a family $F$ of graphs if every graph in $F$ is a vertex-induced subgraph of $U$. For the family of all undirected graphs on $n$ vertices Alstrup, Kaplan, Thorup, and Zwick [STOC 2015] give an induced universal graph with $O\!\left(2^{n/2}\right)$ vertices, matching a lower bound by Moon [Proc. Glasgow Math. Assoc. 1965]. Let $k= \lceil D/2 \rceil$. Improving asymptotically on previous results by Butler [Graphs and Combinatorics 2009] and Esperet, Arnaud and Ochem [IPL 2008], we give an induced universal graph with $O\!\left(\frac{k2^k}{k!}n^k \right)$ vertices for the family of graphs with $n$ vertices of maximum degree $D$. For constant $D$, Butler gives a lower bound of $Ω\!\left(n^{D/2}\right)$. For an odd constant $D\geq 3$, Esperet et al. and Alon and Capalbo [SODA 2008] give a graph with $O\!\left(n^{k-\frac{1}{D}}\right)$ vertices. Using their techniques for any (including constant) even values of $D$ gives asymptotically worse bounds than we present. For large $D$, i.e. when $D = Ω\left(\log^3 n\right)$, the previous best upper bound was ${n\choose\lceil D/2\rceil} n^{O(1)}$ due to Adjiashvili and Rotbart [ICALP 2014]. We give upper and lower bounds showing that the size is ${\lfloor n/2\rfloor\choose\lfloor D/2 \rfloor}2^{\pm\tilde{O}\left(\sqrt{D}\right)}$. Hence the optimal size is $2^{\tilde{O}(D)}$ and our construction is within a factor of $2^{\tilde{O}\left(\sqrt{D}\right)}$ from this. The previous results were larger by at least a factor of $2^{Ω(D)}$. As a part of the above, proving a conjecture by Esperet et al., we construct an induced universal graph with $2n-1$ vertices for the family of graphs with max degree $2$. In addition, we give results for acyclic graphs with max degree $2$ and cycle graphs. Our results imply the first labeling schemes that for any $D$ are at most $o(n)$ bits from optimal.

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arXiv2016

Hashing for statistics over k-partitions

In this paper we analyze a hash function for $k$-partitioning a set into bins, obtaining strong concentration bounds for standard algorithms combining statistics from each bin. This generic method was originally introduced by Flajolet and Martin~[FOCS'83] in order to save a factor $Ω(k)$ of time per element over $k$ independent samples when estimating the number of distinct elements in a data stream. It was also used in the widely used HyperLogLog algorithm of Flajolet et al.~[AOFA'97] and in large-scale machine learning by Li et al.~[NIPS'12] for minwise estimation of set similarity. The main issue of $k$-partition, is that the contents of different bins may be highly correlated when using popular hash functions. This means that methods of analyzing the marginal distribution for a single bin do not apply. Here we show that a tabulation based hash function, mixed tabulation, does yield strong concentration bounds on the most popular applications of $k$-partitioning similar to those we would get using a truly random hash function. The analysis is very involved and implies several new results of independent interest for both simple and double tabulation, e.g. a simple and efficient construction for invertible bloom filters and uniform hashing on a given set.

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arXiv2016

Optimal induced universal graphs and adjacency labeling for trees

We show that there exists a graph $G$ with $O(n)$ nodes, where any forest of $n$ nodes is a node-induced subgraph of $G$. Furthermore, for constant arboricity $k$, the result implies the existence of a graph with $O(n^k)$ nodes that contains all $n$-node graphs as node-induced subgraphs, matching a $Ω(n^k)$ 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 $\log_2 n +O(1)$ 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).

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arXiv2016

The Power of Two Choices with Simple Tabulation

The power of two choices is a classic paradigm for load balancing when assigning $m$ balls to $n$ bins. When placing a ball, we pick two bins according to two hash functions $h_0$ and $h_1$, and place the ball in the least loaded bin. Assuming fully random hash functions, when $m=O(n)$, Azar et al.~[STOC'94] proved that the maximum load is $\lg \lg n + O(1)$ with high probability. In this paper, we investigate the power of two choices when the hash functions $h_0$ and $h_1$ are implemented with simple tabulation, which is a very efficient hash function evaluated in constant time. Following their analysis of Cuckoo hashing [J.ACM'12], Pǎtraşcu and Thorup claimed that the expected maximum load with simple tabulation is $O(\lg\lg n)$. This did not include any high probability guarantee, so the load balancing was not yet to be trusted. Here, we show that with simple tabulation, the maximum load is $O(\lg\lg n)$ with high probability, giving the first constant time hash function with this guarantee. We also give a concrete example where, unlike with fully random hashing, the maximum load is not bounded by $\lg \lg n + O(1)$, or even $(1+o(1))\lg \lg n$ with high probability. Finally, we show that the expected maximum load is $\lg \lg n + O(1)$, just like with fully random hashing.

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arXiv2015

A simple and optimal ancestry labeling scheme for trees

We present a $\lg n + 2 \lg \lg n+3$ ancestry labeling scheme for trees. The problem was first presented by Kannan et al. [STOC 88'] along with a simple $2 \lg n$ solution. Motivated by applications to XML files, the label size was improved incrementally over the course of more than 20 years by a series of papers. The last, due to Fraigniaud and Korman [STOC 10'], presented an asymptotically optimal $\lg n + 4 \lg \lg n+O(1)$ labeling scheme using non-trivial tree-decomposition techniques. By providing a framework generalizing interval based labeling schemes, we obtain a simple, yet asymptotically optimal solution to the problem. Furthermore, our labeling scheme is attained by a small modification of the original $2 \lg n$ solution.

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arXiv2015

Longest Common Extensions in Sublinear Space

The longest common extension problem (LCE problem) is to construct a data structure for an input string $T$ of length $n$ that supports LCE$(i,j)$ queries. Such a query returns the length of the longest common prefix of the suffixes starting at positions $i$ and $j$ in $T$. This classic problem has a well-known solution that uses $O(n)$ space and $O(1)$ query time. In this paper we show that for any trade-off parameter $1 \leq τ\leq n$, the problem can be solved in $O(\frac{n}τ)$ space and $O(τ)$ query time. This significantly improves the previously best known time-space trade-offs, and almost matches the best known time-space product lower bound.

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arXiv2015

Quicksort, Largest Bucket, and Min-Wise Hashing with Limited Independence

Randomized algorithms and data structures are often analyzed under the assumption of access to a perfect source of randomness. The most fundamental metric used to measure how "random" a hash function or a random number generator is, is its independence: a sequence of random variables is said to be $k$-independent if every variable is uniform and every size $k$ subset is independent. In this paper we consider three classic algorithms under limited independence. We provide new bounds for randomized quicksort, min-wise hashing and largest bucket size under limited independence. Our results can be summarized as follows. -Randomized quicksort. When pivot elements are computed using a $5$-independent hash function, Karloff and Raghavan, J.ACM'93 showed $O ( n \log n)$ expected worst-case running time for a special version of quicksort. We improve upon this, showing that the same running time is achieved with only $4$-independence. -Min-wise hashing. For a set $A$, consider the probability of a particular element being mapped to the smallest hash value. It is known that $5$-independence implies the optimal probability $O (1 /n)$. Broder et al., STOC'98 showed that $2$-independence implies it is $O(1 / \sqrt{|A|})$. We show a matching lower bound as well as new tight bounds for $3$- and $4$-independent hash functions. -Largest bucket. We consider the case where $n$ balls are distributed to $n$ buckets using a $k$-independent hash function and analyze the largest bucket size. Alon et. al, STOC'97 showed that there exists a $2$-independent hash function implying a bucket of size $Ω( n^{1/2})$. We generalize the bound, providing a $k$-independent family of functions that imply size $Ω( n^{1/k})$.

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arXiv2014

Additive Spanners: A Simple Construction

We consider additive spanners of unweighted undirected graphs. Let $G$ be a graph and $H$ a subgraph of $G$. The most naïve way to construct an additive $k$-spanner of $G$ is the following: As long as $H$ is not an additive $k$-spanner repeat: Find a pair $(u,v) \in H$ that violates the spanner-condition and a shortest path from $u$ to $v$ in $G$. Add the edges of this path to $H$. We show that, with a very simple initial graph $H$, this naïve method gives additive $6$- and $2$-spanners of sizes matching the best known upper bounds. For additive $2$-spanners we start with $H=\emptyset$ and end with $O(n^{3/2})$ edges in the spanner. For additive $6$-spanners we start with $H$ containing $\lfloor n^{1/3} \rfloor$ arbitrary edges incident to each node and end with a spanner of size $O(n^{4/3})$.

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arXiv2014

Dynamic and Multi-functional Labeling Schemes

We investigate labeling schemes supporting adjacency, ancestry, sibling, and connectivity queries in forests. In the course of more than 20 years, the existence of $\log n + O(\log \log)$ labeling schemes supporting each of these functions was proven, with the most recent being ancestry [Fraigniaud and Korman, STOC '10]. Several multi-functional labeling schemes also enjoy lower or upper bounds of $\log n + Ω(\log \log n)$ or $\log n + O(\log \log n)$ respectively. Notably an upper bound of $\log n + 5\log \log n$ for adjacency+siblings and a lower bound of $\log n + \log \log n$ for each of the functions siblings, ancestry, and connectivity [Alstrup et al., SODA '03]. We improve the constants hidden in the $O$-notation. In particular we show a $\log n + 2\log \log n$ lower bound for connectivity+ancestry and connectivity+siblings, as well as an upper bound of $\log n + 3\log \log n + O(\log \log \log n)$ for connectivity+adjacency+siblings by altering existing methods. In the context of dynamic labeling schemes it is known that ancestry requires $Ω(n)$ bits [Cohen, et al. PODS '02]. In contrast, we show upper and lower bounds on the label size for adjacency, siblings, and connectivity of $2\log n$ bits, and $3 \log n$ to support all three functions. There exist efficient adjacency labeling schemes for planar, bounded treewidth, bounded arboricity and interval graphs. In a dynamic setting, we show a lower bound of $Ω(n)$ for each of those families.

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