In the Contention Resolution problem n parties each wish to have exclusive use of a shared resource for one unit of time. A canonical example is n devices that each must broadcast a packet of information on a shared channel, but the same principles apply to other distributed systems. The problem has been studied since the early 1970s, under a variety of assumptions on feedback (collision detection, etc.) given to the parties, how the parties wake up (synchronized, adversarial, random), knowledge of n, and so on. The most consistent assumption is that parties do not have access to a global clock, only their local time since wake-up. This is surprising because the assumption of a global clock is both technologically realistic and algorithmically interesting. It enriches the problem, and opens the door to entirely new techniques.

In the snippets problem, the goal is to preprocess a text T so that given two pattern queries, P₁ and P₂, one can quickly locate the occurrences of the two patterns in T that are closest to each other, or report the distance between these occurrences. Kopelowitz and Krauthgamer [CPM2016] showed upper bound tradeoffs and conditional lower bounds tradeoffs for the snippets problem, by utilizing connections between the snippets problem and the problem of constructing a color distance oracle (CDO), which is a data structure that preprocess a set of points with associated colors so that given two colors c and c' one can quickly find the (distance between the) closest pair of points where one has color c and the other has color c'. However, the existing upper bound and lower bound curves are not tight. Inspired by recent advances by Kopelowitz and Vassilevska-Williams [ICALP2020] regarding tradeoff curves for Set-disjointness data structures, in this paper we introduce new conditionally optimal algorithms for a (1+ε) approximation version of the snippets problem and a (1+ε) approximation version of the CDO problem, by applying fast matrix multiplication. For example, for CDO on n points in an array, if the preprocessing time is Õ(n^a) and the query time is Õ(n^b) then, assuming that ω = 2 (where ω is the exponent of n in the runtime of the fastest matrix multiplication algorithm on two squared matrices of size n× n), we show that approximate CDO can be solved with the following tradeoff a + 2b = 2 (if 0 ≤ b ≤ 1/3) 2a + b = 3 (if 1/3 ≤ b ≤ 1). Moreover, we prove that for exact CDO on points in an array, the algorithm of Kopelowitz and Krauthgamer [CPM2016], which obtains a tradeoff of a+b = 2, is essentially optimal assuming that the strong all-pairs shortest paths hypothesis holds for randomized algorithms. Thus, we demonstrate that the exact version of CDO is strictly harder than the approximate version. Moreover, this separation carries over to the snippets problem.

The main contribution of this paper is a new improved variant of the laser method for designing matrix multiplication algorithms. Building upon the recent techniques of [Duan, Wu, Zhou, FOCS 2023], the new method introduces several new ingredients that not only yield an improved bound on the matrix multiplication exponent $ω$, but also improve the known bounds on rectangular matrix multiplication by [Le Gall and Urrutia, SODA 2018]. In particular, the new bound on $ω$ is $ω\le 2.371552$ (improved from $ω\le 2.371866$). For the dual matrix multiplication exponent $α$ defined as the largest $α$ for which $ω(1,α,1)=2$, we obtain the improvement $α\ge 0.321334$ (improved from $α\ge 0.31389$). Similar improvements are obtained for various other exponents for multiplying rectangular matrices.

For an undirected unweighted graph G = (V,E) with n vertices and m edges, let d(u,v) denote the distance from u ∈ V to v ∈ V in G. An (α,β)-stretch approximate distance oracle (ADO) for G is a data structure that given u,v ∈ V returns in constant (or near constant) time a value dˆ(u,v) such that d(u,v) ≤ dˆ(u,v) ≤ α⋅ d(u,v) + β, for some reals α > 1, β. Thorup and Zwick [Mikkel Thorup and Uri Zwick, 2005] showed that one cannot beat stretch 3 with subquadratic space (in terms of n) for general graphs. Pǎtraşcu and Roditty [Mihai Pǎtraşcu and Liam Roditty, 2010] showed that one can obtain stretch 2 using O(m^{1/3}n^{4/3}) space, and so if m is subquadratic in n then the space usage is also subquadratic. Moreover, Pǎtraşcu and Roditty [Mihai Pǎtraşcu and Liam Roditty, 2010] showed that one cannot beat stretch 2 with subquadratic space even for graphs where m = Õ(n), based on the set-intersection hypothesis. In this paper we explore the conditions for which an ADO can beat stretch 2 while using subquadratic space. In particular, we show that if the maximum degree in G is Δ_G ≤ O(n^{1/k-ε}) for some 0 < ε ≤ 1/k, then there exists an ADO for G that uses Õ(n^{2-(kε)/3) space and has a (2,1-k)-stretch. For k = 2 this result implies a subquadratic sub-2 stretch ADO for graphs with Δ_G ≤ O(n^{1/2-ε}). Moreover, we prove a conditional lower bound, based on the set intersection hypothesis, which states that for any positive integer k ≤ log n, obtaining a sub-(k+2)/k stretch for graphs with Δ_G = Θ(n^{1/k}) requires Ω̃(n²) space. Thus, for graphs with maximum degree Θ(n^{1/2}), obtaining a sub-2 stretch requires Ω̃(n²) space.

A Dyck sequence is a sequence of opening and closing parentheses (of various types) that is balanced. The Dyck edit distance of a given sequence of parentheses S is the smallest number of edit operations (insertions, deletions, and substitutions) needed to transform S into a Dyck sequence. We consider the threshold Dyck edit distance problem, where the input is a sequence of parentheses S and a positive integer k , and the goal is to compute the Dyck edit distance of S only if the distance is at most k , and otherwise report that the distance is larger than k . Backurs and Onak [PODS’16] showed that the threshold Dyck edit distance problem can be solved in O ( n + k 16 ) time. In this work, we design new algorithms for the threshold Dyck edit distance problem which costs O ( n + k 4.544184 ) time with high probability or O ( n + k 4.853059 ) deterministically. Our algorithms combine several new structural properties of the Dyck edit distance problem, a refined algorithm for fast (min, +) matrix product, and a careful modification of ideas used in Valiant’s parsing algorithm.

A Dyck sequence is a sequence of opening and closing parentheses (of various types) that is balanced. The Dyck edit distance of a given sequence of parentheses S is the smallest number of edit operations (insertions, deletions, and substitutions) needed to transform S into a Dyck sequence. We consider the threshold Dyck edit distance problem, where the input is a sequence of parentheses S and a positive integer k, and the goal is to compute the Dyck edit distance of S only if the distance is at most k, and otherwise report that the distance is larger than k. Backurs and Onak [PODS'16] showed that the threshold Dyck edit distance problem can be solved in O(n + k16) time. In this work, we design new algorithms for the threshold Dyck edit distance problem which costs O(n + k4.782036) time with high probability or O(n + k4.853059) deterministically. Our algorithms combine several new structural properties of the Dyck edit distance problem, a refined algorithm for fast (min, +) matrix product, and a careful modification of ideas used in Valiant's parsing algorithm.

No abstract available.

First introduced in 1954, linear probing is one of the oldest data structures in computer science, and due to its unrivaled data locality, it continues to be one of the fastest hash tables in practice. It is widely believed and taught, however, that linear probing should never be used at high load factors; this is because primary-clustering effects cause insertions at load factor $1 - 1 /x$ to take expected time $Θ(x^2)$ (rather than the ideal $Θ(x)$). The dangers of primary clustering, first discovered by Knuth in 1963, have been taught to generations of computer scientists, and have influenced the design of some of many widely used hash tables. We show that primary clustering is not a foregone conclusion. We demonstrate that small design decisions in how deletions are implemented have dramatic effects on the asymptotic performance of insertions, so that, even if a hash table operates continuously at a load factor $1 - Θ(1/x)$, the expected amortized cost per operation is $\tilde{O}(x)$. This is because tombstones created by deletions actually cause an anti-clustering effect that combats primary clustering. We also present a new variant of linear probing (which we call graveyard hashing) that completely eliminates primary clustering on \emph{any} sequence of operations: if, when an operation is performed, the current load factor is $1 - 1/x$ for some $x$, then the expected cost of the operation is $O(x)$. One corollary is that, in the external-memory model with a data blocks of size $B$, graveyard hashing offers the following remarkable guarantee: at any load factor $1 - 1/x$ satisfying $x = o(B)$, graveyard hashing achieves $1 + o(1)$ expected block transfers per operation. Past external-memory hash tables have only been able to offer a $1 + o(1)$ guarantee when the block size $B$ is at least $Ω(x^2)$.

The shift distance $\mathsf{sh}(S_1,S_2)$ between two strings $S_1$ and $S_2$ of the same length is defined as the minimum Hamming distance between $S_1$ and any rotation (cyclic shift) of $S_2$. We study the problem of sketching the shift distance, which is the following communication complexity problem: Strings $S_1$ and $S_2$ of length $n$ are given to two identical players (encoders), who independently compute sketches (summaries) $\mathtt{sk}(S_1)$ and $\mathtt{sk}(S_2)$, respectively, so that upon receiving the two sketches, a third player (decoder) is able to compute (or approximate) $\mathsf{sh}(S_1,S_2)$ with high probability. This paper primarily focuses on the more general $k$-mismatch version of the problem, where the decoder is allowed to declare a failure if $\mathsf{sh}(S_1,S_2)>k$, where $k$ is a parameter known to all parties. Andoni et al. (STOC'13) introduced exact circular $k$-mismatch sketches of size $\widetilde{O}(k+D(n))$, where $D(n)$ is the number of divisors of $n$. Andoni et al. also showed that their sketch size is optimal in the class of linear homomorphic sketches. We circumvent this lower bound by designing a (non-linear) exact circular $k$-mismatch sketch of size $\widetilde{O}(k)$; this size matches communication-complexity lower bounds. We also design $(1\pm \varepsilon)$-approximate circular $k$-mismatch sketch of size $\widetilde{O}(\min(\varepsilon^{-2}\sqrt{k}, \varepsilon^{-1.5}\sqrt{n}))$, which improves upon an $\widetilde{O}(\varepsilon^{-2}\sqrt{n})$-size sketch of Crouch and McGregor (APPROX'11).

We revisit the k-mismatch problem in the streaming model on a pattern of length m and a streaming text of length n, both over a size-σ alphabet. The current state-of-the-art algorithm for the streaming k-mismatch problem, by Clifford et al. [SODA 2019], uses Õ(k) space and Õ(√k) worst-case time per character. The space complexity is known to be (unconditionally) optimal, and the worst-case time per character matches a conditional lower bound. However, there is a gap between the total time cost of the algorithm, which is Õ(n√k), and the fastest known offline algorithm, which costs Õ(n + min(nk/√m, σn)) time. Moreover, it is not known whether improvements over the Õ(n√k) total time are possible when using more than O(k) space. We address these gaps by designing a randomized streaming algorithm for the k-mismatch problem that, given an integer parameter k≤s≤m, uses Õ(s) space and costs Õ(n+min(nk²/m, nk/√s, σnm/s)) total time. For s=m, the total runtime becomes Õ(n + min(nk/√m, σn)), which matches the time cost of the fastest offline algorithm. Moreover, the worst-case time cost per character is still Õ(√k).

In the online set-disjointness problem the goal is to preprocess a family of sets ℱ, so that given two sets S,S' ∈ ℱ, one can quickly establish whether the two sets are disjoint or not. If N = ∑_{S ∈ ℱ} |S|, then let N^p be the preprocessing time and let N^q be the query time. The most efficient known combinatorial algorithm is a generalization of an algorithm by Cohen and Porat [TCS'10] which has a tradeoff curve of p+q = 2. Kopelowitz, Pettie, and Porat [SODA'16] showed that, based on the 3SUM hypothesis, there is a conditional lower bound curve of p+2q ≥ 2. Thus, the current state-of-the-art exhibits a large gap. The online set-intersection problem is the reporting version of the online set-disjointness problem, and given a query, the goal is to report all of the elements in the intersection. When considering algorithms with N^p preprocessing time and N^q +O(op) query time, where op is the size of the output, the combinatorial algorithm for online set-disjointess can be extended to solve online set-intersection with a tradeoff curve of p+q = 2. Kopelowitz, Pettie, and Porat [SODA'16] showed that, assuming the 3SUM hypothesis, for 0 ≤ q ≤ 2/3 this curve is tight. However, for 2/3 ≤ q < 1 there is no known lower bound. In this paper we close both gaps by showing the following: - For online set-disjointness we design an algorithm whose runtime, assuming ω = 2 (where ω is the exponent in the fastest matrix multiplication algorithm), matches the lower bound curve of Kopelowitz et al., for q ≤ 1/3. We then complement the new algorithm by a matching conditional lower bound for q > 1/3 which is based on a natural hypothesis on the time required to detect a triangle in an unbalanced tripartite graph. Remarkably, even if ω > 2, the algorithm matches the lower bound curve of Kopelowitz et al. for p≥ 1.73688 and q ≤ 0.13156. - For set-intersection, we prove a conditional lower bound that matches the combinatorial upper bound curve for q≥ 1/2 which is based on a hypothesis on the time required to enumerate all triangles in an unbalanced tripartite graph. - Finally, we design algorithms for detecting and enumerating triangles in unbalanced tripartite graphs which match the lower bounds of the corresponding hypotheses, assuming ω = 2.

In population protocols, the underlying distributed network consists of n nodes (or agents), denoted by V, and a scheduler that continuously selects uniformly random pairs of nodes to interact. When two nodes interact, their states are updated by applying a state transition function that depends only on the states of the two nodes prior to the interaction. The efficiency of a population protocol is measured in terms of both time (which is the number of interactions until the nodes collectively have a valid output) and the number of possible states of nodes used by the protocol. By convention, we consider the parallel time cost, which is the time divided by n.

This paper focuses on the contention resolution problem on a shared communication channel that does not support collision detection. A shared communication channel is a multiple access channel, which consists of a sequence of synchronized time slots. Players on the channel may attempt to broadcast a packet (message) in any time slot. A player's broadcast succeeds if no other player broadcasts during that slot. If two or more players broadcast in the same time slot, then the broadcasts collide and both broadcasts fail. The lack of collision detection means that a player monitoring the channel cannot differentiate between the case of two or more players broadcasting in the same slot (a collision) and zero players broadcasting. In the contention-resolution problem, players arrive on the channel over time, and each player has one packet to transmit. The goal is to coordinate the players so that each player is able to successfully transmit its packet within reasonable time. However, the players can only communicate via the shared channel by choosing to either broadcast or not. A contention-resolution protocol is measured in terms of its throughput (channel utilization). Previous work on contention resolution that achieved constant throughput assumed that either players could detect collisions, or the players' arrival pattern is generated by a memoryless (non-adversarial) process.

We revisit a fundamental problem in string matching: given a pattern of length m and a text of length n, both over an alphabet of size σ, compute the Hamming distance (i.e., the number of mismatches) between the pattern and the text at every location. Several randomized (1+ε)-approximation algorithms have been proposed in the literature (e.g., by Karloff (Inf. Proc. Lett., 1993), Indyk (FOCS 1998), and Kopelowitz and Porat (SOSA 2018)), with running time of the form O(ε−O(1) nlognlogm), all using fast Fourier transform (FFT). We describe a simple randomized (1+ε)-approximation algorithm that is faster and does not need FFT. Combining our approach with additional ideas leads to numerous new results (all Monte-Carlo randomized) in different settings:

Over the past 30 years numerous algorithms have been designed for symmetry breaking problems in the LOCAL model, such as maximal matching, MIS, vertex coloring, and edge coloring. For most problems the best randomized algorithm is at least exponentially faster than the best deterministic algorithm. In this paper we prove that these exponential gaps are necessary and establish numerous connections between the deterministic and randomized complexities in the LOCAL model. Each of our results has a very compelling take-away message: Fast $\Delta$-coloring of trees requires random bits. Building on a recent randomized lower bound of Brandt et al. [ A lower bound for the distributed Lovász local lemma, in Proceedings of the 48th ACM Symposium on Theory of Computing (STOC), ACM, New York, 2016, pp. 479--488], we prove that the randomized complexity of $\Delta$-coloring a tree with maximum degree $\Delta$ is $O(\log_\Delta \log n + \log^\ast n)$ for any $\Delta \ge 55$, whereas its deterministic complexity is $\Omega(\log_\Delta n)$ for any $\Delta\ge 3$. This also establishes a large separation between the deterministic complexity of $\Delta$-coloring and $(\Delta+1)$-coloring trees. There is a gap in the deterministic complexity hierarchy. We show that any deterministic algorithm for a natural class of problems that runs in $O(1) + o(\log_\Delta n)$ rounds can be transformed to run in $O(\log^* n - \log^*\Delta + 1)$ rounds. If the transformed algorithm violates a lower bound (even allowing randomization), then one can conclude that the problem requires $\Omega(\log_\Delta n)$ time deterministically. This gives an alternate proof that deterministically $\Delta$-coloring a tree with small $\Delta$ takes $\Omega(\log_\Delta n)$ rounds. Graph shattering is necessary. We prove that the randomized complexity of any natural problem on instances of size $n$ is at least its deterministic complexity on instances of size $\sqrt{\log n}$. This shows that any randomized $O(1) + o(\log_\Delta \log n)$-round algorithm can be derandomized to run in deterministically $O(1) + o(\log_\Delta n)$ rounds and hence can be transformed to run in $O(\log^* n - \log^*\Delta + 1)$ rounds. This also shows that a deterministic $\Omega(\log_\Delta n)$ lower bound for any problem ($\Delta$-coloring a tree, for example) implies a randomized $\Omega(\log_\Delta \log n)$ lower bound. It illustrates that the graph shattering technique employed in recent randomized symmetry breaking algorithms is absolutely essential to the LOCAL model. For example, it is provably impossible to improve the $2^{O(\sqrt{\log\log n})}$ terms in the complexities of the best MIS and $(\Delta+1)$-coloring algorithms without also improving the $2^{O(\sqrt{\log n})}$-round Panconesi--Srinivasan algorithms.

Energy is often the most constrained resource for battery-powered wireless devices, and most of the energy is often spent on transceiver usage (i.e., transmitting and receiving packets) rather than computation. In this article, we study the energy complexity of fundamental problems in several models of wireless radio networks. It turns out that energy complexity is very sensitive to whether the devices can generate random bits and their ability to detect collisions . We consider four collision detection models: Strong-CD (in which transmitters and listeners detect collisions), Sender-CD (in which only transmitters detect collisions), Receiver-CD (in which only listeners detect collisions), and No-CD (in which no one detects collisions). The take-away message of our results is quite surprising. For randomized algorithms, there is an exponential gap between the energy complexity of Sender-CD and Receiver-CD: Randomized: No-CD = Sender-CD > Receiver-CD = Strong-CD and for deterministic algorithms, there is another exponential gap in energy complexity, but in the reverse direction : Deterministic: No-CD = Receiver-CD > Sender-CD = Strong-CD Precisely, the randomized energy complexity of Leader Election is Θ(log * n ) in Sender-CD but Θ(log(log * n )) in Receiver-CD, where n is the number of devices, which is unknown to the devices at the beginning; the deterministic complexity of Leader Election is Θ(log N ) in Receiver-CD but Θ(log log N ) in Sender-CD, where N is the size of the ID space. There is a tradeoff between time and energy. We provide a new upper bound on the time-energy tradeoff curve for randomized algorithms. A critical component of this algorithm is a new deterministic Leader Election algorithm for dense instances, when n = Θ( N ), with inverse Ackermann energy complexity.

Recently, there has been a growing focus in solving approximate pattern matching problems in the streaming model. Of particular interest are the pattern matching with k-mismatches (KMM) problem and the pattern matching with w-wildcards (PMWC) problem. Motivated by reductions from these problems in the streaming model to the dictionary matching problem, this paper focuses on designing algorithms for the dictionary matching problem in the multi-stream model where there are several independent streams of data (as opposed to just one in the streaming model), and the memory complexity of an algorithm is expressed using two quantities: (1) a read-only shared memory storage area which is shared among all the streams, and (2) local stream memory that each stream stores separately. In the dictionary matching problem in the multi-stream model the goal is to preprocess a dictionary D={P_1,P_2,...,P_d} of d=|D| patterns (strings with maximum length m over alphabet Sigma) into a data structure stored in shared memory, so that given multiple independent streaming texts (where characters arrive one at a time) the algorithm reports occurrences of patterns from D in each one of the texts as soon as they appear. We design two efficient algorithms for the dictionary matching problem in the multi-stream model. The first algorithm works when all the patterns in D have the same length m and costs O(d log m) words in shared memory, O(log m log d) words in stream memory, and O(log m) time per character. The second algorithm works for general D, but the time cost per character becomes O(log m+log d log log d). We also demonstrate the usefulness of our first algorithm in solving both the KMM problem and PMWC problem in the streaming model. In particular, we obtain the first almost optimal (up to poly-log factors) algorithm for the PMWC problem in the streaming model. We also design a new algorithm for the KMM problem in the streaming model that, up to poly-log factors, has the same bounds as the most recent results that use different techniques. Moreover, for most inputs, our algorithm for KMM is significantly faster on average.

This paper gives a new deterministic algorithm for the dynamic Minimum Spanning Forest (MSF) problem in the EREW PRAM model, where the goal is to maintain a MSF of a weighted graph with n vertices and m edges while supporting edge insertions and deletions. We show that one can solve the dynamic MSF problem using $O(\sqrt n)$ processors and $O(łog n)$ worst-case update time, for a total of $O(\sqrt n łog n)$ work. This improves on the work of Ferragina [IPPS 1995] which costs $O(łog n)$ worst-case update time and $O(n^2/3 łog\fracm n )$ work.

For decades, randomized exponential backoff has provided a critical algorithmic building block in situations where multiple devices seek access to a shared resource. Despite this history, the performance of standard exponential backoff is poor under worst-case scheduling of demands on the resource: (i) subconstant throughput can occur under plausible scenarios, and (ii) each of $N$ devices requires $\Omega(\log N)$ access attempts before obtaining the resource. In this paper, we address these shortcomings by offering a new backoff protocol for a shared communication channel that guarantees expected constant throughput with only $O(\log(\log^* N))$ channel accesses in expectation, even when packet arrivals are scheduled by an adversary. Central to this result are new algorithms for approximate counting and leader election with the same performance guarantees.

We introduce and study the online house numbering problem, where houses are added arbitrarily along a road and must be assigned labels to maintain their ordering along the road. The online house numbering problem is related to classic online list labeling problems, except that the optimization goal here is to minimize the maximum number of times that any house is relabeled. We provide several algorithms that achieve interesting tradeoffs between upper bounds on the number of maximum relabels per element and the number of bits used by labels.

This paper investigates the task of load balancing where the objective function is to minimize the p-norm of loads, for p\geq 1, in both static and incremental settings. We consider two closely related load balancing problems. In the bipartite matching problem we are given a bipartite graph G=(C\cup S, E) and the goal is to assign each client c\in C to a server s\in S so that the p-norm of assignment loads on S is minimized. In the graph orientation problem the goal is to orient (direct) the edges of a given undirected graph while minimizing the p-norm of the out-degrees. The graph orientation problem is a special case of the bipartite matching problem, but less complex, which leads to simpler algorithms. For the graph orientation problem we show that the celebrated Chiba-Nishizeki peeling algorithm provides a simple linear time load balancing scheme whose output is an orientation that is 2-competitive, in a p-norm sense, for all p\geq 1. For the bipartite matching problem we first provide an offline algorithm that computes an optimal assignment. We then extend this solution to the online bipartite matching problem with reassignments, where vertices from C arrive in an online fashion together with their corresponding edges, and we are allowed to reassign an amortized O(1) vertices from C each time a new vertex arrives. In this online scenario we show how to maintain a single assignment that is 8-competitive, in a p-norm sense, for all p\geq 1.

This paper gives a new deamortized solution to the sequential-file-maintenance problem. The data structure uses several new tools for solving this historically complicated problem. These tools include an unbalanced ternary-tree layout embedded in the sparse table, one-way rebalancing, and extra structural properties to keep interaction among rebalances to a minimum.

Computing the strongly connected Components (SCCs) in a graph $G=(V,E)$ is known to take only $O(m + n)$ time using an algorithm by Tarjan [SIAM J. Comput., 1 (1972), pp. 146--160] where $m = |E|$, $n=|V|$. For fully dynamic graphs, conditional lower bounds provide evidence that the update time cannot be improved by polynomial factors over recomputing the SCCs from scratch after every update. Nevertheless, substantial progress has been made to find algorithms with fast update time for decremental graphs, i.e., graphs that undergo edge deletions. In this paper, we present the first algorithm for general decremental graphs that maintains the SCCs in total update time $\tilde{O}(m)$, thus only a polylogarithmic factor from the optimal running time. (We use $\tilde{O}(f(n))$ notation to suppress logarithmic factors, i.e., $g(n) = \tilde{O}(f(n))$ if $g(n) = O(f(n) {polylog}(n)).$) Our result also yields the fastest algorithm for the decremental single-source reachability (SSR) problem which can be reduced to decrementally maintaining SCCs. Using a well-known reduction, we use our decremental result to achieve new update/query-time trade-offs in the fully dynamic setting. We can maintain the reachability of pairs $S \times V$, $S \subseteq V$ in fully dynamic graphs with update time $\tilde{O}(\frac{|S|m}{t})$ and query time $O(t)$ for all $t \in [1,|S|]$; this matches to polylogarithmic factors the best all-pairs reachability algorithm for $S = V$.

Energy is often the most constrained resource for battery-powered wireless devices and the lion's share of energy is often spent on transceiver usage (sending/receiving packets), not on computation. In this paper we study the energy complexity of Leader Election and Approximate Counting in several models of wireless radio networks. It turns out that energy complexity is very sensitive to whether the devices can generate random bits and their ability to detect collisions. We consider four collision-detection models: Strong-CD (in which transmitters and listeners detect collisions), Sender-CD and Receiver-CD (in which only transmitters or only listeners detect collisions), and No-CD (in which no one detects collisions.)

In the snippets problem we are interested in preprocessing a text T so that given two pattern queries P_1 and P_2, one can quickly locate the occurrences of the patterns in T that are the closest to each other. A closely related problem is that of constructing a color-distance oracle, where the goal is to preprocess a set of points from some metric space, in which every point is associated with a set of colors, so that given two colors one can quickly locate two points associated with those colors, that are as close as possible to each other. We introduce efficient data structures for both color-distance oracles and the snippets problem. Moreover, we prove conditional lower bounds for these problems from both the 3SUM conjecture and the Combinatorial Boolean Matrix Multiplication conjecture.

In the online dictionary matching problem the goal is to preprocess a set of patterns D={P_1,...,P_d} over alphabet Sigma, so that given an online text (one character at a time) we report all of the occurrences of patterns that are a suffix of the current text before the following character arrives. We introduce a succinct Aho-Corasick like data structure for the online dictionary matching problem. Our solution uses a new succinct representation for multi-labeled trees, in which each node has a set of labels from a universe of size lambda. We consider lowest labeled ancestor (LLA) queries on multi-labeled trees, where given a node and a label we return the lowest proper ancestor of the node that has the queried label. In this paper we introduce a succinct representation of multi-labeled trees for lambda=omega(1) that support LLA queries in O(log(log(lambda))) time. Using this representation of multi-labeled trees, we introduce a succinct data structure for the online dictionary matching problem when sigma=omega(1). In this solution the worst case cost per character is O(log(log(sigma)) + occ) time, where occ is the size of the current output. Moreover, the amortized cost per character is O(1+occ) time.

In the pattern matching with d wildcards problem we are given a text T of length n and a pattern P of length m that contains d wildcard characters, each denoted by a special symbol '?'. A wildcard character matches any other character. The goal is to establish for each m-length substring of T whether it matches P. In the streaming model variant of the pattern matching with d wildcards problem the text T arrives one character at a time and the goal is to report, before the next character arrives, if the last m characters match P while using only o(m) words of space. In this paper we introduce two new algorithms for the d wildcard pattern matching problem in the streaming model. The first is a randomized Monte Carlo algorithm that is parameterized by a constant 0<=delta<=1. This algorithm uses ~O(d^{1-delta}) amortized time per character and ~O(d^{1+delta}) words of space. The second algorithm, which is used as a black box in the first algorithm, is a randomized Monte Carlo algorithm which uses O(d+log m) worst-case time per character and O(d log m) words of space.

In recent years much effort was put into developing polynomial-time conditional lower bounds for algorithms and data structures in both static and dynamic settings. Along these lines we suggest a framework for proving conditional lower bounds based on the well-known 3SUM conjecture. Our framework creates a \emph{compact representation} of an instance of the 3SUM problem using hashing and domain specific encoding. This compact representation admits false solutions to the original 3SUM problem instance which we reveal and eliminate until we find a true solution. In other words, from all \emph{witnesses} (candidate solutions) we figure out if an \emph{honest} one (a true solution) exists. This enumeration of witnesses is used to prove conditional lower bound on \emph{reporting} problems that generate all witnesses. In turn, these reporting problems are reduced to various decision problems. These help to enumerate the witnesses by constructing appropriate search data structures. Hence, 3SUM-hardness of the decision problems is deduced. We utilize this framework to show conditional lower bounds for several variants of convolutions, matrix multiplication and string problems. Our framework uses a strong connection between all of these problems and the ability to find \emph{witnesses}. While these specific applications are used to demonstrate the techniques of our framework, we believe that this novel framework is useful for many other problems as well.

We present a deterministic dynamic connectivity data structure for undirected graphs with worst case update time O(sqrt{(n(log(log(n)))^2)/log(n)}) and constant query time. This improves on the previous best deterministic worst case algorithm of Frederickson (SIAM J. Comput., 1985) and Eppstein Galil, Italiano, and Nissenzweig (J. ACM, 1997), which had update time O(sqrt{n}). All other algorithms for dynamic connectivity are either randomized (Monte Carlo) or have only amortized performance guarantees.

Over the past 30 years numerous algorithms have been designed for symmetry breaking problems in the LOCAL model, such as maximal matching, MIS, vertex coloring, and edge coloring. For most problems the best randomized algorithm is at least exponentially faster than the best deterministic algorithm. We prove that these exponential gaps are necessary and establish numerous connections between the deterministic and randomized complexities in the LOCAL model. Each of our results has a very compelling take-away message: 1) Building on the recent randomized lower bounds of Brandt et al. [1], we prove that the randomized complexity of Δ-coloring a tree with maximum degree Δ is O(log Δ log n + log*n), for any Δ > = 55, whereas its deterministic complexity is Ω(log Δ n) for any Δ > = 3. This also establishes a large separation between the deterministic complexity of Δ-coloring and (Δ+1)-coloring trees. 2) We prove that any deterministic algorithm for a natural class of problems that runs in O(1) + o(log Δ n) rounds can be transformed to run in O(log*n - log*Δ + 1) rounds. If the transformed algorithm violates a lower bound (even allowing randomization), then one can conclude that the problem requires Ω(log Δ n) time deterministically. This gives an alternate proof that deterministically Δ-coloring a tree with small Δ takes Ω(log Δ n) rounds. 3) We prove that the randomized complexity of any natural problem on instances of size n is at least its deterministic complexity on instances of size √log n. This shows that a deterministic Ω(log Δ n) lower bound for any problem (Δ-coloring a tree, for example) implies a randomized Ω(log Δ log n) lower bound. It also illustrates that the graph shattering technique employed in recent randomized symmetry breaking algorithms is absolutely essential to the LOCAL model. For example, it is provably impossible to improve the 2O(√log log n) term in the complexities of the best MIS and (Δ+1)-coloring algorithms without also improving the 2O(√log n)-round Panconesi-Srinivasan algorithm.

We examine the complexity of the online Dictionary Matching with One Gap Problem (DMOG) which is the following. Preprocess a dictionary D of d patterns, where each pattern contains a special gap symbol that can match any string, so that given a text that arrives online, a character at a time, we can report all of the patterns from D that are suffixes of the text that has arrived so far, before the next character arrives. In more general versions the gap symbols are associated with bounds determining the possible lengths of matching strings. Online DMOG captures the difficulty in a bottleneck procedure for cyber-security, as many digital signatures of viruses manifest themselves as patterns with a single gap. In this paper, we demonstrate that the difficulty in obtaining efficient solutions for the DMOG problem, even in the offline setting, can be traced back to the infamous 3SUM conjecture. We show a conditional lower bound of Omega(delta(G_D)+op) time per text character, where G_D is a bipartite graph that captures the structure of D, delta(G_D) is the degeneracy of this graph, and op is the output size. Moreover, we show a conditional lower bound in terms of the magnitude of gaps for the bounded case, thereby showing that some known offline upper bounds are essentially optimal. We also provide matching upper-bounds (up to sub-polynomial factors), in terms of the degeneracy, for the online DMOG problem. In particular, we introduce algorithms whose time cost depends linearly on delta(G_D). Our algorithms make use of graph orientations, together with some additional techniques. These algorithms are of practical interest since although delta(G_D) can be as large as sqrt(d), and even larger if G_D is a multi-graph, it is typically a very small constant in practice. Finally, when delta(G_D) is large we are able to obtain even more efficient solutions.

Over the past 30 years numerous algorithms have been designed for symmetry breaking problems in the LOCAL model, such as maximal matching, MIS, vertex coloring, and edge-coloring. For most problems the best randomized algorithm is at least exponentially faster than the best deterministic algorithm. In this paper we prove that these exponential gaps are necessary and establish numerous connections between the deterministic and randomized complexities in the LOCAL model. Each of our results has a very compelling take-away message:

The 3SUM conjecture has proven to be a valuable tool for proving conditional lower bounds on dynamic data structures and graph problems. This line of work was initiated by Pâtraşcu (STOC 2010) who reduced 3SUM to an offline SetDisjointness problem. However, the reduction introduced by Pâtraşcu suffers from several inefficiencies, making it difficult to obtain tight conditional lower bounds from the 3SUM conjecture. In this paper we address many of the deficiencies of Pâtraşcu's framework. We give new and efficient reductions from 3SUM to offline SetDisjointness and offline SetIntersection (the reporting version of SetDisjointness) which leads to polynomially higher lower bounds on several problems. Using our reductions, we are able to show the essential optimality of several algorithms, assuming the 3SUM conjecture. Chiba and Nishizeki's O(mα)-time algorithm (SICOMP 1985) for enumerating all triangles in a graph with arboricity/degeneracy α is essentially optimal, for any α. Bjørklund, Pagh, Williams, and Zwick's algorithm (ICALP 2014) for listing t triangles is essentially optimal (assuming the matrix multiplication exponent is ω = 2). Any static data structure for SetDisjointness that answers queries in constant time must spend Ω(N2–o(1)) time in preprocessing, where N is the size of the set system. These statements were unattainable via Pâtraşcu's reductions. We also introduce several new reductions from 3SUM to pattern matching problems and dynamic graph problems. Of particular interest are new conditional lower bounds for dynamic versions of Maximum Cardinality Matching, which introduce a new technique for obtaining amortized lower bounds.

We introduce the Holiday Gathering Problem which models the difficulty in scheduling non-interfering transmissions in (wireless) networks. Our goal is to schedule transmission rounds so that the antennas that transmit in a given round will not interfere with each other, i.e. all of the other antennas that can interfere will not transmit in that round, while minimizing the number of consecutive rounds in which antennas do not transmit.

For decades, randomized exponential backoff has provided a critical algorithmic building block in situations where multiple devices seek access to a shared resource. Surprisingly, despite this history, the performance of standard backoff is poor under worst-case scheduling of demands on the resource: (i) subconstant throughput can occur under plausible scenarios, and (ii) each of N devices requires Omega(log N) access attempts before obtaining the resource.

In this work, we present efficient algorithms for constructing sparse suffix trees, sparse suffix arrays, and sparse position heaps for b arbitrary positions of a text T of length n while using only O ( b ) words of space during the construction. Attempts at breaking the naïve bound of Ω( nb ) time for constructing sparse suffix trees in O ( b ) space can be traced back to the origins of string indexing in 1968. First results were not obtained until 1996, but only for the case in which the b suffixes were evenly spaced in T . In this article, there is no constraint on the locations of the suffixes. Our main contribution is to show that the sparse suffix tree (and array) can be constructed in O ( n log 2 b ) time. To achieve this, we develop a technique that allows one to efficiently answer b longest common prefix queries on suffixes of T , using only O ( b ) space. We expect that this technique will prove useful in many other applications in which space usage is a concern. Our first solution is Monte Carlo, and outputs the correct tree with high probability. We then give a Las Vegas algorithm, which also uses O ( b ) space and runs in the same time bounds with high probability when b = O (√ n). Additional trade-offs between space usage and construction time for the Monte Carlo algorithm are given. Finally, we show that, at the expense of slower pattern queries, it is possible to construct sparse position heaps in O ( n + b log b ) time and O ( b ) space.

The algorithmic tasks of computing the Hamming distance between a given pattern of length m and each location in a text of length n is one of the most fundamental algorithmic tasks in string algorithms. Unfortunately, there is evidence that for a text T of sizen and a pattern P of size m, one cannot compute the exact Hamming distance for all locations in T in time which is less than O(n√m). However, Karloff [30] showed that if one is willing to suffer a 1 ± € approximation, then it is possible to solve the problem with high probability, in O(2/n) time. Due to related lower bounds for computing the Hamming distance of two strings in the one-way communication complexity model, it is strongly believed that obtaining an algorithm for solving the approximation version cannot be done much faster as a function of 1/ε. We show here that this belief is false by introducing a new O(n/ε ) time algorithm that succeeds with high probability. The main idea behind our algorithm, which is common in sparse recovery problems, is to reduce the variance of a specific randomized experiment by (approximately) separating heavy hitters from non-heavy hitters. However, while known sparse recovery techniques work very well on vectors, they do not seem to apply here, where we are dealing with mismatches between pairs of characters. We introduce two main algorithmic ingredients. The first is a new sparse recovery method that applies for pair inputs (such as in our setting). The second is a new construction of hash/projection functions, for which have which allows us to count the number of projections that induce mismatches between two characters exponentially faster than brute force. We expect that these algorithmic techniques will be of independent interest.

This paper presents a general technique for optimally transforming any dynamic data structure that operates on atomic and indivisible keys by constant-time comparisons, into a data structure that handles unbounded-length keys whose comparison cost is not a constant. Examples of these keys are strings, multidimensional points, multiple-precision numbers, multikey data (e.g., records), XML paths, URL addresses, etc. The technique is more general than what has been done in previous work as no particular exploitation of the underlying structure is required. The only requirement is that the insertion of a key must identify its predecessor or its successor. Using the proposed technique, online suffix tree construction can be done in worst case time $O(\log n)$ per input symbol (as opposed to amortized $O(\log n)$ time per symbol, achieved by previously known algorithms). To our knowledge, our algorithm is the first that achieves $O(\log n)$ worst case time per input symbol. Searching for a pattern of length $m$ in the resulting suffix tree takes $O(\min(m \log |\Sigma|, m + \log n) + tocc)$ time, where $tocc$ is the number of occurrences of the pattern. The paper also describes more applications and shows how to obtain alternative methods for dealing with suffix sorting, dynamic lowest common ancestors, and order maintenance. The technical features of the proposed technique for a given data structure $\mathscr{D}$ are the following ones. The new data structure $\mathscr{D}'$ is obtained from $\mathscr{D}$ by augmenting the latter with an oracle for strings, extending the functionalities of the Dietz--Sleator list for order maintenance [P. F. Dietz and D. D. Sleator, Proceedings of the Nineteenth Annual ACM Symposium on Theory of Computing, ACM, New York, 1987, pp. 365--372; A. Tsakalidis, Acta Inform., 21 (1984), pp. 101--112]. The space complexity of $\mathscr{D}'$ is $\mathscr{S}(n) + O(n)$ memory cells for storing $n$ keys, where $\mathscr{S}(n)$ denotes the space complexity of $\mathscr{D}$. Then, each operation involving $O(1)$ keys taken from $\mathscr{D}'$ requires $O(\mathscr{T}(n))$ time, where $\mathscr{T}(n)$ denotes the time complexity of the corresponding operation originally supported in $\mathscr{D}$. Each operation involving a key $y$ not stored in $\mathscr{D}'$ takes $O(\mathscr{T}(n) + |y|)$ time, where $|y|$ denotes the length of $y$. For the special case where the oracle handles suffixes of a string, the achieved insertion time is $O(\mathscr{T}(n))$.

The problem of Text Indexing is a fundamental algorithmic problem in which one wishes to preprocess a text in order to quickly locate pattern queries within the text. In the ever evolving world of dynamic and on-line data, there is also a need for developing solutions to index texts which arrive online, i.e. a character at a time, and still be able to quickly locate said patterns. In this paper, a new solution for on-line indexing is presented by providing an on-line suffix tree construction in O(log log n + log log |Σ|) worst-case expected time per character, where n is the size of the string, and Σ is the alphabet. This improves upon all previously known on-line suffix tree constructions for general alphabets, at the cost of having the run time in expectation. The main idea is to reduce the problem of constructing a suffix tree on-line to an interesting variant of the order maintenance problem, which may be of independent interest. In the famous order maintenance problem, one wishes to maintain a dynamic list L of size n under insertions, deletions, and order queries. In an order query, one is given two nodes from L and must determine which node precedes the other in L. In an extension to this problem, named the Predecessor search on Dynamic Subsets of an Ordered Dynamic List problem (POLP for short), it is also necessary to maintain dynamic subsets S1, · · · , Sk⊆ L, such that given some u ∈ L it will be possible to quickly locate the predecessor of u in Si, for any integer 1 ≤ i ≤ k. This paper provides an efficient data structure capable of locating the predecessor of u in Si in O(log log n) worst-case time and answering order queries on L in O(1) worst-case time, while allowing updates to L in O(1) worst-case expected time and updates to the subsets in O(log log n) worst-case expected time. This improves over a previous data structure which may be implicitly obtained from Dietz [8], in which the updates to the sets and L are done in O(log log n) amortized expected time. In addition, the bounds shown here match the currently best known bounds for predecessor search in the RAM model. Furthermore, this paper improves or simplifies bounds for several additional applications, including fully-persistent arrays, the monotonic list labeling problem, and the Order-Maintenance Problem.

We present an approximate distance oracle for a point set S with n points and doubling dimension Λ. For every ε > 0, the oracle supports (1 + ε)-approximate distance queries in (universal) constant time, occupies space [ε−O(Λ) + 2O(Λ log Λ)]n, and can be constructed in [2O(Λ) log3 n + ε−O(Λ) + 2O(Λ log Λ)]n expected time. This improves upon the best previously known constructions, presented by Har-Peled and Mendel [13]. Furthermore, the oracle can be made fully dynamic with expected O(1) query time and only 2O(Λ) log n + ε−O(Λ) + 2O(Λ log Λ) update time. This is the first fully dynamic (1 + ε)-distance oracle.