ACM Transactions on

Algorithms (TALG)

Latest Articles

Editorial: ACM-SIAM Symposium on Discrete Algorithms (SODA) 2016 Special Issue

Subtree Isomorphism Revisited

The Subtree Isomorphism problem asks whether a given tree is contained in another given tree. The problem is of fundamental importance and has been studied since the 1960s. For some variants, e.g., ordered trees, near-linear time algorithms are known, but for the general case truly subquadratic algorithms remain elusive. Our first result is a... (more)

On the Integrality Gap of Degree-4 Sum of Squares for Planted Clique

The problem of finding large cliques in random graphs and its “planted” variant, where one wants to recover a clique of size ω... (more)

CoveringLSH: Locality-Sensitive Hashing without False Negatives

We consider a new construction of locality-sensitive hash functions for Hamming space that is covering in the sense that is it guaranteed to produce a collision for every pair of vectors within a given radius r. The construction is efficient in the sense that the expected number of hash collisions between vectors at distance cr, for a given... (more)

Improved Deterministic Algorithms for Linear Programming in Low Dimensions

Chazelle and Matoušek [J. Algorithms, 1996] presented a derandomization of Clarkson’s sampling-based algorithm [J. ACM, 1995] for... (more)

A Faster Subquadratic Algorithm for Finding Outlier Correlations

We study the problem of detecting outlier pairs of strongly correlated variables among a collection of n variables with otherwise weak pairwise... (more)

Deterministic Algorithms for Submodular Maximization Problems

Randomization is a fundamental tool used in many theoretical and practical areas of computer science. We study here the role of randomization in the... (more)

Near-Optimal Light Spanners

A spanner H of a weighted undirected graph G is a “sparse” subgraph that approximately preserves distances between every pair of vertices in G. We refer to H as a δ-spanner of G for some parameter δ ≥ 1 if the distance in H between every vertex pair is at most a factor δ bigger than in G. In this case, we say... (more)

Fully Polynomial-Time Parameterized Computations for Graphs and Matrices of Low Treewidth

We investigate the complexity of several fundamental polynomial-time solvable problems on graphs and on matrices, when the given instance has low... (more)

Subexponential Parameterized Algorithm for Interval Completion

In the Interval Completion problem we are given an n-vertex graph G and an integer k, and the task is to transform G by making use of at most k edge... (more)


In Memoriam: David S. Johnson

About TALG

The ACM Transactions on Algorithms (TALG) publishes original research of the highest quality dealing with algorithms that are inherently discrete and finite, and having mathematical content in a natural way, either in the objective or in the analysis.

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Approximation Guarantees for the Minimum Linear Arrangement Problem by Higher Eigenvalues

Batched Point Location in SINR Diagrams via Algebraic Tools

The SINR model attempts to predict whether a particular transmitter is heard at a specific location, in a setting consisting of n simultaneous transmitters and background noise. The SINR model gives rise to the SINR diagram, which partitions the space into n regions, one per transmitter, and the remaining space where no transmitter can be heard. Point location in the SINR diagram, i.e., determining which transmitter is heard at a query point (if any), has been investigated in several papers. These planar data structures are constructed in time at least quadratic in n and support logarithmic-time approximate queries. Moreover, the performance of some of them depends also on some geometric parameters that cannot be bounded as a function of n or epsilon. In this paper, we address the question of batched point-location queries, i.e., answering many queries simultaneously. In one dimension, we can answer n queries exactly in amortized polylogarithmic time per query, while in the plane we can do it approximately. These results can handle arbitrary power assignments to the transmitters. Moreover, the amortized query time depends only on n and epsilon. We also show how to speed up the preprocessing in a previously proposed point-location structure for uniform-power sites, by almost a full order of magnitude. For this we obtain results on the sensitivity of the reception regions to slight changes in the reception threshold, which are of independent interest. Finally, these results demonstrate the power of combining algebraic tools with those of computational geometry and other fields.

Conditional Lower Bounds for All-Pairs Max-Flow

We provide evidence that computing the maximum flow value between every pair of nodes in a directed graph on $n$ nodes, $m$ edges, and capacities in the range $[1..n]$, which we call the All-Pairs Max-Flow problem, cannot be solved in time that is faster significantly than $O(n^2 m)$. Since a single maximum $st$-flow in such graphs can be solved in time $\tO(m\sqrt{n})$ [Lee and Sidford, FOCS 2014], we conclude that the all-pairs version might require time equivalent to $\tilde\Omega(n^{3/2})$ computations of maximum $st$-flow, which strongly separates the directed case from the undirected one. Moreover, if maximum $st$-flow can be solved in time $\tO(m)$, then the runtime of $\tilde\Omega(n^2)$ computations is needed. This is in contrast to a conjecture of Lacki, Nussbaum, Sankowski, and Wulf-Nilsen [FOCS 2012] that All-Pairs Max-Flow in general graphs can be solved faster than the time of $O(n^2)$ computations of maximum $st$-flow. Specifically, we show that in sparse graphs $G=(V,E,w)$, if one can compute All-Pairs Max-Flow in time $O((n^2 m)^{1-\varepsilon})$, for some constant $\varepsilon>0$, then MAX-CNF-SAT with $n'$ variables and $m'$ clauses can be solved in time ${m'}^{O(1)}2^{(1-\delta)n'}$ for a constant $\delta(\varepsilon)>0$, a problem for which not even $2^{n'}/\poly(n')$ algorithms are known. Such runtime for MAX-CNF-SAT would in particular refute the Strong Exponential Time Hypothesis (SETH). Hence, we improve the lower bound of Abboud, Vassilevska-Williams, and Yu [STOC 2015], who showed that for every fixed $\varepsilon>0$ and $\card{S}=\card{T}=O(\sqrt{n})$, if the above problem can be solved in time $O(n^{3/2-\varepsilon})$, then some incomparable (and intuitively weaker) conjecture is false.

Subquadratic Algorithms for the Diameter and the Sum of Pairwise Distances in Planar Graphs

We show how to compute for n-vertex planar graphs in O(n^{11/6} polylog(n)) expected time the diameter and the sum of the pairwise distances. The algorithms work for directed graphs with real weights and no negative cycles. In O(n^{15/8} polylog(n)) expected time we can also compute the number of pairs of vertices at distance smaller than a given threshold, These are the first algorithms for these problems using time O(n^c) for some constant c < 2, even when restricted to undirected, unweighted planar graphs.

Dynamic Time Warping and Geometric Edit Distance: Breaking the Quadratic Barrier

Dynamic Time Warping (DTW) and Geometric Edit Distance (GED) are basic similarity measures between curves or general temporal sequences (e.g., time series) that are represented as sequences of points in some metric space $(X, dist)$. The DTW and GED measures are massively used in various fields of computer science and computational biology. Consequently, the tasks of computing these measures are among the core problems in P. Despite extensive efforts to find more efficient algorithms, the best-known algorithms for computing the DTW or GED between two sequences of points in $X = \mathbb{R}^d$ are long-standing dynamic programming algorithms that require quadratic runtime, even for the one-dimensional case $d = 1$, which is perhaps one of the most used in practice. In this paper, we break the nearly 50 years old quadratic time bound for computing DTW or GED between two sequences of $n$ points in $\mathbb{R}$, by presenting deterministic algorithms that run in $O\left( n^2 \log\log\log n / \log\log n \right)$ time. Our algorithms can be extended to work also for higher dimensional spaces $\mathbb{R}^d$, for any constant $d$, when the underlying distance-metric $dist$ is polyhedral (e.g., $L_1, L_\infty$).

An Efficient Representation for Filtrations of Simplicial Complexes

A filtration over a simplicial complex K is an ordering of the simplices of K such that all prefixes in the ordering are subcomplexes of K. Filtrations are at the core of Persistent Homology, a major tool in Topological Data Analysis. In order to represent the filtration of a simplicial complex, the entire filtration can be appended to any data structure that explicitly stores all the simplices of the complex such as the Hasse diagram or the recently introduced Simplex Tree [Algorithmica '14]. However, with the popularity of various computational methods that need to handle simplicial complexes, and with the rapidly increasing size of the complexes, the task of finding a compact data structure that can still support efficient queries is of great interest. In this paper, we propose a new data structure called the Critical Simplex Diagram (CSD) which is a variant of the Simplex Array List (SAL) [SoCG '15]. Our data structure allows to store in a compact way the filtration of a simplicial complex, and allows for the efficient implementation of a large range of basic operations. Moreover, we prove that our data structure is essentially optimal with respect to the requisite storage space. Finally, we show that the CSD representation admits fast construction algorithms for Flag complexes and relaxed Delaunay complexes.

Completeness for First-Order Properties on Sparse Structures with Algorithmic Applications

Properties definable in first-order logic are algorithmically interesting for both theoretical and pragmatic reasons. Many of the most studied algorithmic problems, such as Hitting Set and Orthogonal Vectors, are first-order, and the first-order properties naturally arise as relational database queries. A relatively straightforward algorithm for evaluating a property with k+1 quantifiers takes time $O(m^k)$ and, assuming the Strong Exponential Time Hypothesis (SETH), some such properties require $O(m^{k-\epsilon})$ time for any $\epsilon > 0$. (Here, m represents the size of the input structure, i.e. the number of tuples in all relations.) We give algorithms for every first-order property that improves this upper bound to $m^k/2^{\Theta(\sqrt{\log n})}$, i.e., an improvement by a factor more than any poly-log, but less than the polynomial required to refute SETH. Moreover, we show that further improvement is equivalent to improving algorithms for sparse instances of the well-studied Orthogonal Vectors problem. Surprisingly, both results are obtained by showing completeness of the Sparse Orthogonal Vectors problem for the class of first-order properties under fine-grained reductions. To obtain improved algorithms, we apply the fast Orthogonal Vectors algorithm of [AWY15,CW16]. While fine-grained reductions (reductions that closely preserve the conjectured complexities of problems) have been used to relate the hardness of disparate specific problems both within P and beyond, this is the first such completeness result for a standard complexity class.

Network Sparsification for Steiner Problems on Planar and Bounded-Genus Graphs

We propose polynomial-time algorithms that sparsify planar and bounded-genus graphs while preserving optimal or near-optimal solutions to Steiner problems. Our main contribution is a polynomial-time algorithm that, given an unweighted graph $G$ embedded on a surface of genus $g$ and a designated face $f$ bounded by a simple cycle of length $k$, uncovers a set $F \subseteq E(G)$ of size polynomial in $g$ and $k$ that contains an optimal Steiner tree for any set of terminals that is a subset of the vertices of $f$. We apply this general theorem to prove that: * given an unweighted graph $G$ embedded on a surface of genus $g$ and a terminal set $\terms \subseteq V(G)$, one can in polynomial time find a set $F \subseteq E(G)$ that contains an optimal Steiner tree $T$ for $\terms$ and that has size polynomial in $g$ and $|E(T)|$; * an analogous result holds for an optimal Steiner forest for a set $T$ of terminal pairs; * given an unweighted planar graph $G$ and a terminal set $\terms \subseteq V(G)$, one can in polynomial time find a set $F \subseteq E(G)$ that contains an optimal (edge) multiway cut $C$ separating $\terms$ (i.e., a cutset that intersects any path with endpoints in different terminals from $\terms$) and that has size polynomial in $|C|$.

Windrose Planarity: Embedding Graphs with Direction-Constrained Edges

Given a planar graph $G$ and a partition of the neighbors of each vertex $v$ in four sets $\overset{\nearrow}{v}$, $\overset{\nwarrow}{v}$, $\overset{\swarrow}{v}$, and $\overset{\searrow}{v}$, the problem {\sc Windrose Planarity} asks to decide whether $G$ admits a \emph{windrose-planar drawing}, that is, a planar drawing in which \begin{inparaenum}[(i)] \item each neighbor $u \in \overset{\nearrow}{v}$ is above and to the right of $v$, \item each neighbor $u \in \overset{\nwarrow}{v}$ is above and to the left of $v$, \item each neighbor $u \in \overset{\swarrow}{v}$ is below and to the left of $v$, \item each neighbor $u \in \overset{\searrow}{v}$ is below and to the right of $v$, and \item edges are represented by curves that are monotone with respect to each axis. \end{inparaenum} By exploiting both the horizontal and the vertical relationship among vertices, windrose-planar drawings allow to simultaneously visualize two partial orders defined by means of the edges of the graph. Although the problem is NP-complete in the general case, we give a polynomial-time algorithm for testing whether there exists a windrose-planar drawing that respects a given combinatorial embedding. This algorithm is based on a characterization of the plane triangulations admitting a windrose-planar drawing. Furthermore, for any embedded graph with $n$ vertices that has a windrose-planar drawing, we can construct one with at most one bend per edge and with at most $2n-5$ bends in total, which lies on the $3n \times 3n$ grid. The latter result contrasts with the fact that straight-line windrose-planar drawings may require exponential area.

Packing Groups of Items into Multiple Knapsacks

We consider a natural generalization of the classical multiple knapsack problem in which instead of packing single items we are packing groups of items. In this problem, we have multiple knapsacks and a set of items which are partitioned into groups. Each item has an individual weight, while the profit is associated with groups rather than items. The profit of a group can be attained if and only if every item of this group is packed. Such a general model finds applications in various practical problems, e.g., delivering bundles of goods. The tractability of this problem relies heavily on how large a group could be. Deciding if a group of items of total weight $2$ could be packed into two knapsacks of unit capacity is already $NP$-hard and it thus rules out a constant-approximation algorithm for this problem in general. We then focus on the parameterized version where the total weight of items in each group is bounded by a factor $\delta$ of the total capacity of all knapsacks. Both approximation and inapproximability results with respect to $\delta$ are derived. We also show that, depending on whether the number of knapsacks is a constant or part of the input, the approximation ratio for the problem, as a function on $\delta$, changes substantially, which has a clear difference from the classical multiple knapsack problem.

An Efficient Algorithm for Computing High-Quality Paths amid Polygonal Obstacles

We study a path-planning problem amid a set O of obstacles in R2, in which we wish to compute a short path between two points while also maintaining a high clearance from O; the clearance of a point is its distance from a nearest obstacle in O. Specifically, the problem asks for a path minimizing the reciprocal of the clearance integrated over the length of the path. We present the first polynomial-time approximation scheme for this problem. Let n be the total number of obstacle vertices and let µ  (0,1]. Our algorithm computes in time O((n2 / µ2) log(n / µ)) a path of total cost at most (1+µ) times the cost of the optimal path.

Adaptive Computation of the Swap-Insert Correction Distance

The Swap-Insert Correction distance from a string S of length n to another string L of length m g n on the alphabet [1..d] is the minimum number of insertions, and swaps of pairs of adjacent symbols, converting S into L. Contrarily to other correction distances, computing it is NP-Hard in the size d of the alphabet. We describe an algorithm computing this distance in time within O(d2 nm gd-1), where for each a[1..d] there are na occurrences of a in S, ma occurrences of a in L, and where g=maxa[1..d] min {na,ma-na} measures the difficulty of the instance. The difficulty g is bounded by above by various terms, such as the length n of the shortest string S, and by the maximum number of occurrences of a single character in S. Those results illustrate how, in many cases, the correction distance between two strings can be easier to compute than in the worst case scenario.

Graph Reconstruction and Verification

How efficiently can we find an unknown graph using distance or shortest path queries between its vertices? We assume that the unknown graph G is connected, unweighted, and has bounded degree. In the reconstruction problem, the goal is to find the graph G. In the verification problem, we are given a hypothetical graph $\hat G$ and want to check whether G is equal to $\hat G$. We provide a randomized algorithm for reconstruction using $\tilde O(n^{3/2})$ distance queries, based on Voronoi cell decomposition. Next, we analyze natural greedy algorithms for reconstruction using a shortest path oracle and also for verification using either oracle, and show that their query complexity is $n^{1+o(1)}$. We further improve the query complexity when the graph is chordal or outerplanar. Finally, we show some lower bounds, and consider an approximate version of the reconstruction problem.

Stream Sampling Framework and Application for Frequency Cap Statistics

Unaggregated data, in a streamed or distributed form, is prevalent and comes from diverse sources such as interactions of users with web services and IP traffic. Data elements have {\em keys} (cookies, users, queries) and elements with different keys interleave. Analytics on such data typically utilizes statistics expressed as a sum over keys in a specified segment of a function $f$ applied to the frequency (the total number of occurrences) of the key. In particular, {\em Distinct} is the number of active keys in the segment, {\em Sum} is the sum of their frequencies, and both are special cases of {\em frequency cap} statistics, which cap the frequency by a parameter T. The number of distinct active keys in the data can be very large, making exact computation of queries costly. Instead, we can estimate these statistics from a sample. An optimal sample for a given function $f$ would include a key with frequency $w$ with probability roughly proportional to $f(w)$. But while such a "gold-standard" sample can be easily computed over the aggregated data (the set of key-frequency pairs), exact aggregation itself is costly, requiring state proportional to the number of active keys. Ideally, we would like to compute a sample without exact aggregation. We present a sampling framework for unaggregated data that uses a single pass (for streams) or two passes (for distributed data) and state equal to the desired sample size and applies to all cap statistics.

Deterministic parallel algorithms for fooling polylogarithmic juntas and the Lovász Local Lemma

Many randomized algorithms can be derandomized efficiently using either the method of conditional expectations or probability spaces with low (almost-) independence. A series of papers, beginning with Luby (1988) and continuing with Berger \& Rompel (1991) and Chari et al. (1994), showed that these techniques can be combined to give deterministic parallel algorithms for combinatorial optimization problems involving sums of $w$-juntas. We improve these algorithms through derandomized variable partitioning and a new code construction for fooling Fourier characters over $GF(2)$. This reduces the processor complexity to essentially independent of $w$ while the running time is reduced from exponential in $w$ to linear in $w$. As a key subroutine, we give a new algorithm to generate a probability space which can fool a given set of neighborhoods, each of size at most $w$. Schulman (1992) gave an NC algorithm to do so for $w \leq O(\log n)$. Our new algorithm is NC1, with essentially optimal time and processor complexity, when $w = O(\log n)$; it remains NC up to $w = \text{polylog}(n)$. This answers an open problem of Schulman. One major application of these algorithms is an NC algorithm for the Lov\'{a}sz Local Lemma. Previous NC algorithms, including the seminal algorithm of Moser \& Tardos (2010) and the work of Chandrasekaran et. al (2013), required that (essentially) the bad-events could span only $O(\log n)$ variables; we relax this to $\text{polylog}(n)$ variables. We use this to give algorithms for defective vertex coloring and domatic graph partition in graphs of maximum degree $\text{polylog}(n)$.

Beating Approximation Factor Two for Weighted Tree Augmentation with Bounded Costs

The Weighted Tree Augmentation Problem (WTAP) is a fundamental well-studied problem in the field of network design. Given an undirected tree $G=(V,E)$, an additional set of edges $L \subseteq V\times V$ disjoint from $E$ called \textit{links} and a cost vector $c\in \mathbb{R}_{\geq 0}^L$, WTAP asks to find a minimum-cost set $F\subseteq L$ with the property that $(V,E\cup F)$ is $2$-edge connected. The special case where $c_\ell = 1$ for all $\ell\in L$ is called the Tree Augmentation Problem (TAP). For the class of bounded cost vectors, we present a first improved approximation algorithm for WTAP since more than three decades. Concretely, for any $M\in \mathbb{R}_{\geq 1}$ and $\epsilon > 0$, we present an LP based $(\delta+\epsilon)$-approximation for WTAP restricted to cost vectors $c$ in $[1,M]^L$ for $\delta \approx 1.96417$. More generally, our result is a $(\delta+\epsilon)$-approximation algorithm with running time $n^{r^{O(1)}}$, where $r = c_{\max}/c_{\min}$ is the ratio between the largest and the smallest cost of any link. For the special case of TAP we improve this factor to $\frac{5}{3}+\epsilon$. Our results rely on several new ideas, including a new LP relaxation of WTAP and a two-phase rounding algorithm.

The Dependent Doors Problem: An Investigation into Sequential Decisions without Feedback

We introduce the dependent doors problem as an abstraction for situations in which one must perform a sequence of dependent decisions, without receiving feedback information on the effectiveness of previously made actions. Informally, the problem considers a set of d doors that are initially closed. To open a door, the algorithm knocks on it and it might open or not according to some probability distribution. This distribution may depend on which other doors are currently open, as well as on which other doors were open during each of the previous knocks on that door. The algorithm aims to minimize the expected time until all doors open without knowing whether or which other doors have already opened. Here, we focus on scenarios where dependencies are positively correlated and acyclic. The fundamental distribution of a door describes the probability it opens in the best of conditions. We show that if in two configurations corresponding doors share the same fundamental distribution, then these configurations have the same optimal running time up to a universal constant, no matter what are the dependencies between doors and what are the distributions. We also identify algorithms that are optimal up to a universal constant factor. We then turn our attention to investigate precise bounds. Even for the case of two doors, identifying the optimal sequence is an intriguing combinatorial question. Here, we study the case of two cascading memoryless doors and solve it almost completely.

Streaming Algorithms for Estimating the Matching Size in Planar Graphs and Beyond

We consider the problem of estimating the size of a maximum matching when the edges are revealed in a streaming fashion. When the input graph is planar, we present a simple and elegant streaming algorithm that with high probability estimates the size of a maximum matching within a constant factor using O(n^{2/3}) space, where n is the number of vertices. The approach generalizes to the family of graphs that have bounded arboricity, which include graphs with an excluded constant-size minor. To the best of our knowledge, this is the first result for estimating the size of a maximum matching in the adversarial-order streaming model in o(n) space. We circumvent the barriers inherent in the adversarial-order model by exploiting several structural properties of planar graphs, and more generally, graphs with bounded arboricity. We further reduce the required memory size to O(\sqrt{n}) for three restricted settings: (i) when the input graph is a forest and (ii) when we have 2-passes and the input graph has bounded arboricity. Finally, we design a reduction from the Boolean Hidden Matching Problem to show that there is no randomized streaming algorithm that estimates the size of the maximum matching to within a factor better than 3/2 and uses only o(n^{1/2}) bits of space. Using the same reduction, we show that there is no deterministic algorithm that computes this kind of estimate in $o(n)$ bits of space. The lower bounds hold even for graphs that are collections of paths of constant length.

Even Delta-Matroids and the Complexity of Planar Boolean CSPs

The main result of this paper is a generalization of the classical blossom algorithm for finding perfect matchings. Our algorithm can efficiently solve Boolean CSPs where each variable appears in exactly two constraints (we call it edge CSP) and all constraints are even ”-matroid relations (represented by lists of tuples). As a consequence of this, we settle the complexity classification of planar Boolean CSPs started by DvoYák and Kupec. Knowing that edge CSP is tractable for even ”-matroid constraints allows us to extend the tractability result to a larger class of ”-matroids that includes many classes that were known to be tractable before, namely co-independent, compact, local and binary.


Publication Years 2005-2018
Publication Count 622
Citation Count 3896
Available for Download 622
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First Name Last Name Award
Pankaj Agarwal ACM Fellows (2002)
Noga Alon ACM Fellows (2016)
Lars Arge ACM Fellows (2012)
ACM Distinguished Member (2009)
Guy Blelloch ACM Fellows (2011)
Allan Borodin ACM Fellows (2014)
Moses Charikar ACM Paris Kanellakis Theory and Practice Award (2012)
Danny Z Chen ACM Distinguished Member (2014)
ACM Senior Member (2011)
Siu-Wing Cheng ACM Distinguished Member (2017)
Mahdi Cheraghchi ACM Senior Member (2016)
Kenneth Clarkson ACM Fellows (2008)
Edith Cohen ACM Fellows (2017)
Richard J Cole ACM Fellows (1998)
Anne Condon ACM Fellows (2010)
ACM Doctoral Dissertation Award
Series Winner (1988)
Graham R. Cormode ACM Distinguished Member (2013)
Constantinos Daskalakis ACM Doctoral Dissertation Award (2008)
Erik Demaine ACM Fellows (2016)
Xiaotie Deng ACM Fellows (2008)
ACM Senior Member (2006)
Martin Dietzfelbinger ACM Distinguished Member (2011)
David Eppstein ACM Fellows (2011)
Joan Feigenbaum ACM Fellows (2001)
Pedro F Felzenszwalb ACM Grace Murray Hopper Award (2013)
Harold N Gabow ACM Fellows (2002)
Zvi Galil ACM Fellows (1995)
Emden R Gansner ACM Distinguished Member (2016)
Mohsen Ghaffari ACM Doctoral Dissertation Award (2017)
Andrew V Goldberg ACM Fellows (2009)
Michael T Goodrich ACM Fellows (2009)
ACM Distinguished Member (2006)
Ronald L. Graham ACM Fellows (1999)
Martin Grohe ACM Fellows (2017)
Rachid Guerraoui ACM Fellows (2012)
Leonidas Guibas ACM AAAI Allen Newell Award (2007)
ACM Fellows (1999)
Rajesh Gupta ACM Fellows (2016)
Venkatesan Guruswami ACM Fellows (2017)
Venkatesan Guruswami ACM Doctoral Dissertation Award (2002)
Monika Henzinger ACM Fellows (2016)
John Hershberger ACM Fellows (2012)
Piotr Indyk ACM Fellows (2015)
ACM Paris Kanellakis Theory and Practice Award (2012)
David S Johnson ACM Fellows (1995)
Erich L Kaltofen ACM Fellows (2009)
Howard J Karloff ACM Fellows (2011)
Valerie King ACM Fellows (2014)
Philip N Klein ACM Fellows (2010)
Richard E. Ladner ACM Fellows (1995)
Charles E Leiserson ACM-IEEE CS Ken Kennedy Award (2014)
ACM Paris Kanellakis Theory and Practice Award (2013)
ACM Fellows (2006)
ACM Doctoral Dissertation Award (1982)
Carsten Lund ACM Doctoral Dissertation Award
Series Winner (1991) ACM Doctoral Dissertation Award
Series Winner (1991)
Dahlia Malkhi ACM Fellows (2011)
Yishay Mansour ACM Fellows (2014)
Madhav Marathe ACM Fellows (2013)
Kurt Mehlhorn ACM Paris Kanellakis Theory and Practice Award (2010)
ACM Fellows (1999)
Joseph Mitchell ACM Fellows (2011)
Mukesh Mohania ACM Distinguished Member (2011)
Rajeev Motwani ACM Fellows (2007)
Ian Munro ACM Fellows (2008)
S. Muthukrishnan ACM Fellows (2010)
Moni Naor ACM Paris Kanellakis Theory and Practice Award (2016)
Noam Nissan ACM Doctoral Dissertation Award
Series Winner (1990) ACM Doctoral Dissertation Award
Series Winner (1990)
David Peleg ACM Fellows (2016)
Satish Rao ACM Fellows (2013)
Edward M Reingold ACM Fellows (1996)
Omer Reingold ACM Fellows (2014)
ACM Grace Murray Hopper Award (2005)
Micha Sharir ACM Fellows (1997)
David Shmoys ACM Fellows (2001)
Sandeep K Shukla ACM Distinguished Member (2012)
ACM Senior Member (2007)
Aravind Srinivasan ACM Fellows (2014)
Clifford Stein ACM Fellows (2012)
David Steurer ACM Doctoral Dissertation Award
Honorable Mention (2011) ACM Doctoral Dissertation Award
Honorable Mention (2011)
Madhu Sudan ACM Fellows (2008)
ACM Doctoral Dissertation Award (1993)
Subhash Suri ACM Fellows (2010)
ACM Distinguished Member (2007)
Eva Tardos ACM Fellows (1998)
Robert E Tarjan ACM Paris Kanellakis Theory and Practice Award (1999)
ACM Fellows (1994)
ACM A. M. Turing Award (1986)
Mikkel Thorup ACM Fellows (2005)
Eli Upfal ACM Fellows (2005)
Salil P Vadhan ACM Doctoral Dissertation Award (2000)
Jeffrey S Vetter ACM Distinguished Member (2012)
ACM Gordon Bell Prize
Performance (2010)
Jennifer L Welch ACM Distinguished Member (2012)
Emmerich Welzl ACM Fellows (1998)
Peter Widmayer ACM Fellows (1997)
Rebecca N. Wright ACM Distinguished Member (2017)

First Name Last Name Paper Counts
Saket Saurabh 14
Daniel Lokshtanov 12
Mohammadtaghi Hajiaghayi 12
Dániel Marx 12
Guy Kortsarz 11
Robert Tarjan 10
Fedor Fomin 9
Uri Zwick 9
Pankaj Agarwal 8
Erik Demaine 8
Mikkel Thorup 8
Zeev Nutov 8
Haim Kaplan 8
Ke Yi 7
Gonzalo Navarro 7
David Peleg 7
Magnús Halldórsson 7
Samir Khuller 7
Maxim Sviridenko 7
Anupam Gupta 7
Rohit Khandekar 6
Andrzej Pelc 6
Moshe Lewenstein 6
Micha Sharir 6
Venkatesh Raman 6
Viswanath Nagarajan 6
Noga Alon 6
Adi Rosén 5
Chandra Chekuri 5
David Eppstein 5
Kirk Pruhs 5
Harold Gabow 5
Hadas Shachnai 5
Timothy Chan 5
Raphael Yuster 5
Joseph Naor 5
Inge Gørtz 5
Graham Cormode 5
Philip Klein 5
Yossi Azar 5
Shay Solomon 5
Michael Elkin 5
S Muthukrishnan 5
Liam Roditty 5
Nikhil Bansal 5
Sariel Har-Peled 5
Seth Pettie 5
Thore Husfeldt 4
Telikepalli Kavitha 4
Glencora Borradaile 4
Loukas Georgiadis 4
Dimitrios Thilikos 4
Meng He 4
Srinivasa Satti 4
Dana Ron 4
Ashish Goel 4
Ola Svensson 4
Boris Aronov 4
Bernhard Haeupler 4
Marek Cygan 4
Oren Weimann 4
T Chan 4
Baruch Schieber 4
M Ramanujan 4
Sudipto Guha 4
Chaitanya Swamy 4
Guy Even 4
Fabrizio Grandoni 4
Susanne Albers 4
Martín Farach-Colton 4
Amin Saberi 4
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Akiko Suzuki 1
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T Jayram 1
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Rajsekar Manokaran 1
Martin Wahlén 1
Zvi Galil* 1
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C Subramanian 1
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Affiliation Paper Counts
Stevens Institute of Technology 1
Florida International University 1
University of Durham 1
University College Cork 1
Boston University 1
Aegean University 1
University of G. d'Annunzio Chieti and Pescara 1
Universite Pierre et Marie Curie 1
Netanya Academic College 1
National Taiwan Ocean University 1
University of Eastern Piedmont Amedeo Avogadro, Alessandria 1
Laboratoire d'Analyse et Modelisation de Systemes pour l'Aide a la Decision 1
University of Tubingen 1
Federal University of Parana 1
Wroclaw University of Science and Technology 1
University of Missouri-Kansas City 1
Birkbeck University of London 1
Hong Kong Polytechnic University 1
National Technical University of Athens 1
Vrije Universiteit Amsterdam 1
Hong Kong Baptist University 1
University of Wisconsin Madison 1
St. Petersburg Department of V.A. Steklov Institute of Mathematics of the Russian Academy of Sciences 1
University of Electro-Communications 1
Linkoping University 1
SRI International 1
University of Glasgow 1
University of Melbourne 1
Iowa State University 1
Cisco Systems 1
Kwansei Gakuin University 1
University of Leoben 1
Dalian University of Technology 1
National Chiao Tung University Taiwan 1
Laboratoire d'Informatique, de Robotique et de Microelectronique de Montpellier LIRMM 1
Sobolev Institute of Mathematics of Siberian Branch of the RAS 1
University of Western Macedonia 1
Holon Institute of Technology 1
Hewlett-Packard Inc. 1
Sant'Anna School of Advanced Studies 1
Microsoft Corporation 1
NASA Ames Research Center 1
University of California, Santa Cruz 1
University of Miami 1
The University of North Carolina at Charlotte 1
International Institute of Information Technology Bangalore 1
University of Colorado at Denver 1
The University of Georgia 1
Duquesne University 1
Ludwig Maximilian University of Munich 1
St Petersburg National Research Academic University of the Russian Academy of Sciences 1
National Institutes of Health, Bethesda 1
NEC Deutschland GmbH 1
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Microsoft Research Cambridge 1
Sandia National Laboratories, New Mexico 1
University of Sao Paulo 1
Illinois Wesleyan University 1
University of Stellenbosch 1
University of Quebec in Montreal 1
Meiji University 1
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Korea Advanced Institute of Science & Technology 1
Sun Yat-Sen University 1
State University of New York College at Oneonta 1
Kyushu University 1
University of Bristol 1
Emory University 1
University of Milan 1
Oracle Corporation 1
National Research University Higher School of Economics, Moscow 1
University of California, Merced 1
Center for Communications Research 1
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Renmin University of China 1
Institute for Advanced Studies 1
ORT Braude - College of Engineering 1
Siemens AG 1
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Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 1
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Laboratoire d'Informatique de l'Ecole Polytechnique 1
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VMware, Inc 1
Leonard N. Stern School of Business 1
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Commonwealth Scientific and Industrial Research Organization 1
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Pavol Jozef safarik University in Kosice 1
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University of Tokyo 1, Inc. 1
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BRICS Basic Research in Computer Science 1
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CNRS Centre National de la Recherche Scientifique 1
CSIRO Data61 1
University of Illinois at Chicago 1
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Google Switzerland GmbH 1
Dalle Molle Institute for Artificial Intelligence 1
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Lawrence Livermore National Laboratory 1
University of Cambridge 2
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Instituto Superior Tecnico 2
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Athens University of Economics and Business 2
Georgetown University 2
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IBM Haifa Labs 2
Mentor Graphics Corporation 2
Microsoft Research Asia 2
National Taiwan University 2
University of Primorska 2
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Tata Institute of Fundamental Research 2
University of L'Aquila 2
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University of Arizona 2
King's College London 2
Universite d'Orleans 2
University of Iowa 2
Free University of Berlin 2
University of Puerto Rico 2
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Technische Universitat Braunschweig 2
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Temple University 2
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West Virginia University 2
Center for Mathematics and Computer Science - Amsterdam 2
Kasetsart University 2
Graz University of Technology 2
University of Kaiserslautern 2
Universite de Bordeaux 3
Goethe University Frankfurt 3
University of Chicago 3
Royal Institute of Technology 3
Oregon State University 3
University of Texas-Pan American 3
Dalhousie University 3
Aix Marseille Universite 3
The Interdisciplinary Center Herzliya 3
Tsinghua University 3
University of Texas at Dallas 3
IBM Research 3
INRIA Institut National de Rechereche en Informatique et en Automatique 3
Universite Paris 13 3
Academy of Sciences of the Czech Republic (Avcr.Cz) 3
Shandong University 3
University of Iceland 3
University of Connecticut 3
Ecole Normale Superieure 3
University of Utah 3
Seoul National University 3
Ohio State University 3
Brooklyn College 3
University of Ljubljana 3
New Jersey Institute of Technology 3
Pennsylvania State University 3
University of Freiburg 3
Uppsala University 3
University of Texas at Austin 3
York University Canada 3
King Abdullah University of Science and Technology 3
National University of Singapore 3
University of Helsinki 3
Toyota Technological Institute at Chicago 3
Harvard University 3
TU Dortmund University 4
Budapest University of Technology and Economics 4
Chinese University of Hong Kong 4
Universitat Politecnica de Catalunya 4
INRIA Lorraine 4
National Tsing Hua University 4
The University of Sydney 4
Karlsruhe Institute of Technology, Campus South 4
Johns Hopkins University 4
Roma Tre University 4
University at Buffalo, State University of New York 4
Arizona State University 4
University of Ioannina 4
University of Twente 4
City University of New York 4
University of Southern Denmark 4
University of Southern California 4
University of Michigan 4
Helsinki Institute for Information Technology 4
Research Organization of Information and Systems National Institute of Informatics 4
Nanyang Technological University 4
Virginia Tech 4
Indian Institute of Science, Bangalore 4
Northwestern University 4
Texas A and M University 4
Georgia Institute of Technology 4
IBM India Research Laboratory 4
University of New Mexico 4
London School of Economics and Political Science 4
Intertrust Technologies Corporation 4
University of Victoria 4
University of Salerno 4
University of Aarhus 4
Nokia Bell Labs 5
Illinois Institute of Technology 5
University of Massachusetts Amherst 5
Computer and Automation Research Institute Hungarian Academy of Sciences 5
University of Colorado at Boulder 5
University of Kiel 5
Yale University 5
McMaster University 5
Indian Institute of Technology, Kanpur 5
Ecole Polytechnique 5
University of California, Riverside 5
University of Washington, Seattle 5
Hungarian Academy of Sciences 5
AT&T Inc. 5
Academia Sinica Taiwan 5
Reykjavik University 5
Purdue University 5
New York University 6
Karlsruhe Institute of Technology 6
The University of British Columbia 6
University of California, San Diego 6
University of Vienna 6
Dartmouth College 6
Columbia University 6
Simon Fraser University 6
University of Montpellier 6
IT University of Copenhagen 6
Kyoto University 6
Charles University 6
Humboldt University of Berlin 6
Universite Libre de Bruxelles 6
Aalto University 6
MIT Computer Science and Artificial Intelligence Laboratory 6
Technical University of Ilmenau 6
University of Pittsburgh 6
IBM Almaden Research Center 7
University of Leicester 7
Utrecht University 7
University of Roma Tor Vergata 7
Yahoo Research Labs 7
University of Athens 7
University of California, Santa Barbara 7
University of Patras 7
Cornell University 7
Technical University of Denmark 8
McGill University 8
University of Quebec in Outaouais 8
Carleton University 8
University of California, Los Angeles 8
Sharif University of Technology 8
Stony Brook University 8
University of California, Irvine 8
NYU Tandon School of Engineering 8
Hebrew University of Jerusalem 9
Rutgers, The State University of New Jersey 9
University of Liverpool 9
University of Wroclaw 9
University of Bonn 9
University of Paderborn 9
University of Pennsylvania 9
Open University of Israel 9
University of Copenhagen 9
The University of Warwick 9
Vienna University of Technology 9
Lund University 9
Friedrich Schiller University Jena 9
University of Illinois 9
University of Illinois at Urbana-Champaign 10
Universidad de Chile 10
University of Toronto 10
University Michigan Ann Arbor 10
Universite Paris 7- Denis Diderot 10
RWTH Aachen University 10
University of Pisa 10
Brown University 11
Swiss Federal Institute of Technology, Zurich 11
University of California, Berkeley 12
Princeton University 13
Duke University 13
University of Alberta 14
Technical University of Berlin 14
Hong Kong University of Science and Technology 15
Eindhoven University of Technology 15
University of Roma La Sapienza 15
Rutgers University-Camden campus 15
Swiss Federal Institute of Technology, Lausanne 16
University of Warsaw 16
University of Haifa 18
IBM Thomas J. Watson Research Center 18
AT&T Laboratories Florham Park 18
Microsoft Research 19
Google Inc. 19
The University of Hong Kong 20
Institute of Mathematical Sciences India 21
Weizmann Institute of Science Israel 23
Stanford University 24
Max Planck Institute for Informatics 26
Ben-Gurion University of the Negev 26
Massachusetts Institute of Technology 27
Bar-Ilan University 27
University of Maryland 28
Carnegie Mellon University 29
University of Bergen 31
Technion - Israel Institute of Technology 33
University of Waterloo 36
Tel Aviv University 78
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