Let G = (V, E) be a graph with n = |V| nodes and m = |E| edges. The t-Pairs Shortest Paths problem, introduced by Cohen [FOCS'93; SICOMP'99], asks to approximate the distances between t prespecified pairs of vertices. Recently, this problem has received renewed attention, particularly in the case where t = Θ(n): the n-Pairs Shortest Paths problem. In this setting, new algorithms and conditional lower bounds have been developed by Dalirrooyfard, Jin, Vassilevska Williams, and Wein [FOCS'22], and Chechik, Hoch, and Lifshitz [SODA'25]. In this paper, we present the first algorithm for the n-Pairs Shortest Paths problem in weighted undirected graphs that achieves a (2 - α)k-approximation, for constant α > 0, that runs in Õ(mn^{1/k} + n^{1 + 2/k}) time. Specifically, we present a 1.622k-approximation, improving upon the (2k - 3)-approximation of Chechik, Hoch, and Lifshitz [SODA'25] for graphs that are not super sparse, which answers in the affirmative the open question posed by them. We also develop improved approximation algorithms with better tradeoffs for unweighted graphs and dense weighted graphs that improve upon the results of Dalirrooyfard et al. and Chechik, Hoch, and Lifshitz. Our main technical contribution is the new heavy-edge technique. Using this technique, we transform an algorithm with an approximation guarantee that depends on W_{uv}, the weight of the heaviest edge on the shortest path between u and v, into an algorithm with purely multiplicative approximation that does not depend on W_{uv}.

We study the problem of approximating the length of a shortest cycle in a given graph, known as the girth of the graph. The state-of-the-art approximation algorithms for unweighted graphs by Kadria et al. [SODA'22] and Roditty and Trabelsi [arXiv'25] achieve the following trade-off: for every integer k ≥ 2, there is an Õ(n^{1+2/k}) time algorithm that achieves a (2k/3)-approximation for the girth in unweighted n-node graphs. The first result of this paper is to achieve the same trade-off for m-edge, n-node graphs with non-negative real edge weights: a 2k/3-approximation algorithm running in Õ(m+n^{1+2/k}) time. The dependence on m is unavoidable in weighted graphs. Our result improves on the work of Kadria et al. [SODA'23] and Ducoffe [ICALP'19 and SIDMA'21], who were only able to achieve such a trade-off for some values of k. We also prove new fine-grained lower bounds for girth approximation and related problems in unweighted graphs.

We provide a fast witness-sensitive algorithm for detecting an induced diamond (a K₄ minus an edge) in an n-vertex graph containing t induced diamonds. Our algorithm runs in time Õ(min(n^2.425/t^0.25 + n², n^ω)) with high probability, improving upon the prior state of the art (witness-oblivious) algorithm that runs in time O(n^ω log n) [Vassilevska Williams, Wang, Williams, Yu, SODA 2014] whenever t ≥ n^{(3-ω)/3}, where ω < 2.372 is the matrix multiplication exponent. Our key insight is that the size of a clique containing one of the triangles of an induced diamond plays a crucial role in detecting such a diamond. We say that a diamond is r-heavy if this size is at least r, and we provide a fast detection algorithm for r-heavy diamonds in Õ(r⋅(n/r)^ω + (n/r)³+ nr) time. When there are no r-heavy diamonds, we provide a different fast detection algorithm in Õ(MM(n,n,n√{r/t})) time, where MM(a,b,c) denotes the time to multiply an a × b matrix by a b × c matrix, which is conditionally optimal for r = Õ(1). Our main technical contribution is in designing a refinement framework for sampling vectors, which allows sampling vertices for detecting diamonds in a manner that is adaptive to the structure of graphs with no r-heavy diamonds. We establish that our technique is of a wide applicability, by showing how it also allows for faster witness-sensitive algorithms for 4-SUM and for a special case of 4-cycles.

We consider the classic 3SUM problem: given sets of integers A, B, C, determine whether there is a tuple (a, b, c) ∈ A × B × C satisfying a + b = c. The 3SUM Hypothesis, central in fine-grained complexity, states that there does not exist a truly subquadratic time 3SUM algorithm. Given this long-standing barrier, recent work over the past decade has explored 3SUM from a data structural perspective. Specifically, in the 3SUM in preprocessed universes regime, we are tasked with preprocessing sets A, B of size n, to create a space-efficient data structure that can quickly answer queries, each of which is a 3SUM problem of the form A', B', C', where A' ⊆ A and B' ⊆ B. A series of results have achieved Õ(n²) preprocessing time, Õ(n²) space, and query time improving progressively from Õ(n^{1.9}) [Timothy M. Chan and Moshe Lewenstein, 2015] to Õ(n^{11/6}) [Timothy M. Chan et al., 2023] to Õ(n^{1.5}) [Kasliwal et al., 2025]. Given these series of works improving query time, a natural open question has emerged: can one achieve both truly subquadratic space and truly subquadratic query time for 3SUM in preprocessed universes? We resolve this question affirmatively, presenting a tradeoff curve between query and space complexity. Specifically, we present a simple randomized algorithm achieving Õ(n^{1.5 + ε}) query time and Õ(n^{2 - 2ε/3}) space complexity. Furthermore, our algorithm has Õ(n²) preprocessing time, matching past work. Notably, quadratic preprocessing is likely necessary for our tradeoff as either the preprocessing or the query time must be at least n^{2-o(1)} under the 3SUM Hypothesis.

Computing the diameter of a graph is a problem of great interest both in general algorithms research and specifically within fine-grained complexity, where it is a cornerstone hard problem. As computing the exact diameter in m-edge graphs requires m^{2-o(1)} time under the Strong Exponential Time Hypothesis, much work has gone into approximating this parameter. Recent work has achieved a full conditional lower bound tradeoff curve for both directed and undirected graphs [Dalirrooyfard, Li and Vassilevska W., FOCS'21]. However, the best known upper bounds do not match the lower bounds. In particular, the best known approximation scheme for undirected graph diameter [Cairo-Grossi-Rizzi, SODA 2016] has not been improved. Moreover, this scheme is randomized and no similar deterministic scheme is known. Another fundamental field of research in shortest paths computation is the construction of approximate distance oracles. Thorup and Zwick [JACM'05] provided the first such distance oracle with constant query time and (conditionally) optimal space, and in the years since many advances have led to a vast toolbox of techniques and data structures. These two areas of research seem natural to combine since they both concern approximating shortest paths. However, the known diameter approximation algorithms only use a small subset of the techniques used in distance oracles research. In this work we show that in fact approximate diameter and distance oracles are intricately connected. We first demonstrate a strong connection between the current best known diameter approximation scheme of Cairo, Grossi and Rizzi ("CGR") and the (2k-1)-approximate distance oracle of Thorup and Zwick. This allows us to derandomize the CGR algorithm and obtain the first deterministic diameter approximation tradeoff. We further derandomize other central techniques in the field of distance oracles and use them to achieve new deterministic diameter approximation algorithms, including a simpler 3/2-approximation with no additive error and a new 5/3-approximation, the first new step in the diameter approximation tradeoff in almost a decade. Finally, we show how these new techniques can be used to derandomize many current best known results in various fields of shortest paths approximations.

Given a graph and two vertices s and t, the Replacement Path Problem (RP) is to compute for every edge e, the distance between s and t when e is removed. There are two natural extensions to RP: - Single Source Replacement Paths (SSRP): Given a graph 𝐆 and a source node s, compute for every vertex v and every edge e the s-v distance in 𝐆⧵e. That is, we do not fix the target anymore. - 2-Fault Replacement Paths (2-FRP): Given a graph 𝐆 and two nodes s and t, compute for every pair of edges e,e' the s-t distance in 𝐆⧵e,e'. That is, there are two failures instead of one. Previously, there was no known formal reduction between SSRP and 2-FRP. It seemed plausible that 2-FRP would be computationally harder because there are no settings where 2-FRP admits a faster algorithm than SSRP. In directed unweighted graphs there is a provable gap in complexity, and in undirected graphs many of the known 2-FRP algorithms in a variety of settings are much slower than those for SSRP in the same setting. The main contribution of this paper is a tight reduction from undirected 2-FRP to undirected SSRP, showing that contrary to prior intuition, 2-FRP is not harder than SSRP. As our reduction is weight-preserving, we get new algorithms for 2-FRP that match the best-known runtimes for SSRP: (a) 𝒪̃(M n^ω) for weights in [1..M] [Grandoni and Vassilevska Williams, FOCS 2012 & TALG 2019], improving upon 𝒪(Mn^{2.87}) [Chechik, Zhang, ICALP 2024]; (b) n³/2^Ω(√{log n}) for weights in [1..poly(n)] [Grandoni and Vassilevska Williams, FOCS 2012 & TALG 2019], improving over the previous n³polylog(n) running time [Vassilevska W., Woldeghebriel and Xu, FOCS 2022]; (c) 𝒪̃(mn^{1/2} + n²) combinatorial time for unweighted graphs [Chechik and Cohen, SODA 2019], and more generally for rational weights in [1,2] [Chechik and Magen, ICALP 2020], improving upon 𝒪̃(n^{3-1/18}) [Chechik, Zhang ICALP 2024]. We complement these upper bounds with tight lower bounds under fine-grained hypotheses.

The All-Pairs Shortest Paths (APSP) is a foundational problem in theoretical computer science. Approximating APSP in undirected unweighted graphs has been studied for many years, beginning with the work of Dor, Halperin and Zwick [SICOMP’01]. Many recent works have attempted to improve these original algorithms using the algebraic tools of fast matrix multiplication. We improve on these results for the following problems.

This work considers the problem of output-sensitive listing of occurrences of $2k$-cycles for fixed constant $k\geq 2$ in an undirected host graph with $m$ edges and $t$ $2k$-cycles. Recent work of Jin and Xu (and independently Abboud, Khoury, Leibowitz, and Safier) [STOC 2023] gives an $O(m^{4/3}+t)$ time algorithm for listing $4$-cycles, and recent work by Jin, Vassilevska Williams and Zhou [SOSA 2024] gives an $\widetilde{O}(n^2+t)$ time algorithm for listing $6$-cycles in $n$ node graphs. We focus on resolving the next natural question: obtaining listing algorithms for $6$-cycles in the sparse setting, i.e., in terms of $m$ rather than $n$. Previously, the best known result here is the better of Jin, Vassilevska Williams and Zhou's $\widetilde{O}(n^2+t)$ algorithm and Alon, Yuster and Zwick's $O(m^{5/3}+t)$ algorithm. We give an algorithm for listing $6$-cycles with running time $\widetilde{O}(m^{1.6}+t)$. Our algorithm is a natural extension of Dahlgaard, Knudsen and Stöckel's [STOC 2017] algorithm for detecting a $2k$-cycle. Our main technical contribution is the analysis of the algorithm which involves a type of ``supersaturation'' lemma relating the number of $2k$-cycles in a bipartite graph to the sizes of the parts in the bipartition and the number of edges. We also give a simplified analysis of Dahlgaard, Knudsen and Stöckel's $2k$-cycle detection algorithm (with a small polylogarithmic increase in the running time), which is helpful in analyzing our listing algorithm.

In this work we study shortest path problems in multimode graphs, a generalization of the min-distance measure introduced by Abboud, Vassilevska W. and Wang in [SODA'16]. A multimode shortest path is the shortest path using one of multiple "modes" of transportation that cannot be combined. This represents real-world scenarios where different modes are not combinable, such as flights operated by different airline alliances. The problem arises naturally in machine learning in the context of learning with multiple embedding. More precisely, a k-multimode graph is a collection of k graphs on the same vertex set and the k-mode distance between two vertices is defined as the minimum among the distances computed in each individual graph. We focus on approximating fundamental graph parameters on these graphs, specifically diameter and radius. In undirected multimode graphs we first show an elegant linear time 3-approximation algorithm for 2-mode diameter. We then extend this idea into a general subroutine that can be used as a part of any α-approximation, and use it to construct a 2 and 2.5 approximation algorithm for 2-mode diameter. For undirected radius, we introduce a general scheme that can compute a 3-approximation of the k-mode radius for any k and runs in near linear time in the case of k = O(1). In the directed case we establish an equivalence between approximating 2-mode diameter on DAGs and approximating the min-diameter, while for general graphs we develop novel techniques and provide a linear time algorithm to determine whether the diameter is finite. We also develop many conditional fine-grained lower bounds for various multimode diameter and radius approximation problems. We are able to show that many of our algorithms are tight under popular fine-grained complexity hypotheses, including our linear time 3-approximation for 3-mode undirected diameter and radius. As part of this effort we propose the first extension to the Hitting Set Hypothesis [SODA'16], which we call the 𝓁-Hitting Set Hypothesis. We use this hypothesis to prove the first parameterized lower bound tradeoff for radius approximation algorithms.

We consider the classical k-Center problem in undirected graphs. The problem is known to have a polynomial-time 2-approximation. There are even (2 + ε )-approximation algorithms for every ε > 0 running in near-linear time. The conventional wisdom is that the problem is closed, as (2 — ε )-approximation is NP-hard when k is part of the input, and for constant k ≥ 2 it requires nk-o(1) time under the Strong Exponential Time Hypothesis (SETH).

We present a new improvement on the laser method for designing fast matrix multiplication algorithms. The new method further develops the recent advances by [Duan, Wu, Zhou FOCS 2023] and [Vassilevska Williams, Xu, Xu, Zhou SODA 2024]. Surprisingly the new improvement is achieved by incorporating more asymmetry in the analysis, circumventing a fundamental tool of prior work that requires two of the three dimensions to be treated identically. The method yields a new bound on the square matrix multiplication exponent ω < 2.371339, improved from the previous bound of ω < 2.371552. We also improve the bounds of the exponents for multiplying rectangular matrices of various shapes.

This work establishes conditional lower bounds for average-case parity -counting versions of the problems k-XOR, k- SUM, and k-OV. The main contribution is a set of self-reductions for the problems, providing the first specific distributions, for which:

Single Source Shortest Paths (SSSP) is among the most well-studied problems in computer science. In the incremental (resp. decremental) setting, the goal is to maintain distances from a fixed source in a graph undergoing edge insertions (resp. deletions). A long line of research culminated in a near-optimal deterministic (1 + ε )-approximate data structure with m1+o(1) total update time over all m updates by Bernstein, Probst Gutenberg and Saranurak [FOCS 2021]. However, there has been remarkably little progress on the exact SSSP problem beyond Even and Shiloach’s algorithm [J. ACM 1981] for unweighted graphs. For weighted graphs,

Let G = (V, E, w ) be a weighted directed graph without negative cycles. For two vertices s,t ∈ V, we let d≤h(s, t ) be the minimum, according to the weight function w, of a path from s to t that uses at most h edges, or hops. We consider algorithms for computing d<h(s,t ) for every 1 ≤ h ≤ n, where n = |V|, in various settings. We consider the singlepair, single-source and all-pairs versions of the problem. We also consider a distance oracle version of the problem in which we are not required to explicitly compute all distances d

STOC ’25, Prague, Czechia

Approximating the graph diameter is a basic task of both theoretical and practical interest. A simple folklore algorithm can output a 2-approximation to the diameter in linear time by running BFS from an arbitrary vertex. It has been open whether a better approximation is possible in near-linear time. A series of articles on fine-grained complexity have led to strong hardness results for diameter in directed graphs, culminating in a recent tradeoff curve independently discovered by [Li, STOC’21] and [Dalirrooyfard and Wein, STOC’21], showing that under the Strong Exponential Time Hypothesis (SETH), for any integer k ≥ 2 and δ > 0, a \(2-\frac{1}{k}-\delta\) approximation for diameter in directed m -edge graphs requires \(m^{1+1/(k-1)-o(1)}\) time. In particular, the simple linear time 2-approximation algorithm is optimal for directed graphs. In this article, we prove that the same tradeoff lower bound curve is possible for undirected graphs as well, extending results of [Roditty and Vassilevska W., STOC’13], [Li’20] and [Bonnet, ICALP’21] who proved the first few cases of the curve, k =2,3, and 4, respectively. Our result shows in particular that the simple linear time 2-approximation algorithm is conditionally optimal for undirected graphs. To obtain our result, we extract the core ideas in known reductions and introduce a unification and generalization that could be useful for proving SETH-based hardness for other problems in undirected graphs related to distance computation.

In the $k$-Disjoint Shortest Paths ($k$-DSP) problem, we are given a weighted graph $G$ on $n$ nodes and $m$ edges with specified source vertices $s_1, \dots, s_k$, and target vertices $t_1, \dots, t_k$, and are tasked with determining if $G$ contains vertex-disjoint $(s_i,t_i)$-shortest paths. For any constant $k$, it is known that $k$-DSP can be solved in polynomial time over undirected graphs and directed acyclic graphs (DAGs). However, the exact time complexity of $k$-DSP remains mysterious, with large gaps between the fastest known algorithms and best conditional lower bounds. In this paper, we obtain faster algorithms for important cases of $k$-DSP, and present better conditional lower bounds for $k$-DSP and its variants. Previous work solved 2-DSP over weighted undirected graphs in $O(n^7)$ time, and weighted DAGs in $O(mn)$ time. For the main result of this paper, we present linear time algorithms for solving 2-DSP on weighted undirected graphs and DAGs. Our algorithms are algebraic however, and so only solve the detection rather than search version of 2-DSP. For lower bounds, prior work implied that $k$-Clique can be reduced to $2k$-DSP in DAGs and undirected graphs with $O((kn)^2)$ nodes. We improve this reduction, by showing how to reduce from $k$-Clique to $k$-DSP in DAGs and undirected graphs with $O((kn)^2)$ nodes. A variant of $k$-DSP is the $k$-Disjoint Paths ($k$-DP) problem, where the solution paths no longer need to be shortest paths. Previous work reduced from $k$-Clique to $p$-DP in DAGs with $O(kn)$ nodes, for $p= k + k(k-1)/2$. We improve this by showing a reduction from $k$-Clique to $p$-DP, for $p=k + \lfloor k^2/4\rfloor$. Under the $k$-Clique Hypothesis from fine-grained complexity, our results establish better conditional lower bounds for $k$-DSP for all $k\ge 4$, and better conditional lower bounds for $p$-DP for all $p\le 4031$.

We construct $n$-node graphs on which any $O(n)$-size spanner has additive error at least $+Ω(n^{3/17})$, improving on the previous best lower bound of $Ω(n^{1/7})$ [Bodwin-Hoppenworth FOCS '22]. Our construction completes the first two steps of a particular three-step research program, introduced in prior work and overviewed here, aimed at producing tight bounds for the problem by aligning aspects of the upper and lower bound constructions. More specifically, we develop techniques that enable the use of inner graphs in the lower bound framework whose technical properties are provably tight with the corresponding assumptions made in the upper bounds. As an additional application of our techniques, we improve the corresponding lower bound for $O(n)$-size additive emulators to $+Ω(n^{1/14})$.

We consider the problem of approximate counting of triangles and longer fixed length cycles in directed graphs. For triangles, Tětek [ICALP'22] gave an algorithm that returns a (1±ε)-approximation in Õ(n^ω/t^{ω-2}) time, where t is the unknown number of triangles in the given n node graph and ω < 2.372 is the matrix multiplication exponent. We obtain an improved algorithm whose running time is, within polylogarithmic factors the same as that for multiplying an n× n/t matrix by an n/t × n matrix. We then extend our framework to obtain the first nontrivial (1± ε)-approximation algorithms for the number of h-cycles in a graph, for any constant h ≥ 3. Our running time is Õ(MM(n,n/t^{1/(h-2)},n)), the time to multiply n × n/(t^{1/(h-2)}) by n/(t^{1/(h-2)) × n matrices. Finally, we show that under popular fine-grained hypotheses, this running time is optimal.

In this paper, we revisit the classic approximate All-Pairs Shortest Paths (APSP) problem in undirected graphs. For unweighted graphs, we provide an algorithm for 2-approximate APSP in Õ(n2.5-r + nω(r)) time, for any r ∈ [0,1]. This is O(n2.032) time, using known bounds for rectangular matrix multiplication nω(r) [Le Gall, Urrutia, SODA 2018]. Our result improves on the Õ(n2·25) bound of [Roditty, STOC 2023], and on the bound of [Baswana, Kavitha, SICOMP 2010] for graphs with m ≥ n1·532 edges.

Roundtrip spanners are the analog of spanners in directed graphs, where the roundtrip metric is used as a notion of distance. Recent works have shown existential results of roundtrip spanners nearly matching the undirected case, but the time complexity for constructing roundtrip spanners is still widely open.

We study the well-known shortcut set problem: how much can one decrease the diameter of a directed graph by adding a small set of shortcuts from the transitive closure of the graph.

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].

We study the problem of finding and listing k-cliques in an m-edge, n-vertex graph, for constant k≥ 3. This is a fundamental problem of both theoretical and practical importance.

Computing the diameter of a graph, i.e. the largest distance, is a fundamental problem that is central in fine-grained complexity. In undirected graphs, the Strong Exponential Time Hypothesis (SETH) yields a lower bound on the time vs. approximation trade-off that is quite close to the upper bounds. In \emph{directed} graphs, however, where only some of the upper bounds apply, much larger gaps remain. Since $d(u,v)$ may not be the same as $d(v,u)$, there are multiple ways to define the problem, the two most natural being the \emph{(one-way) diameter} ($\max_{(u,v)} d(u,v)$) and the \emph{roundtrip diameter} ($\max_{u,v} d(u,v)+d(v,u)$). In this paper we make progress on the outstanding open question for each of them. -- We design the first algorithm for diameter in sparse directed graphs to achieve $n^{1.5-\varepsilon}$ time with an approximation factor better than $2$. The new upper bound trade-off makes the directed case appear more similar to the undirected case. Notably, this is the first algorithm for diameter in sparse graphs that benefits from fast matrix multiplication. -- We design new hardness reductions separating roundtrip diameter from directed and undirected diameter. In particular, a $1.5$-approximation in subquadratic time would refute the All-Nodes $k$-Cycle hypothesis, and any $(2-\varepsilon)$-approximation would imply a breakthrough algorithm for approximate $\ell_{\infty}$-Closest-Pair. Notably, these are the first conditional lower bounds for diameter that are not based on SETH.

The k-Detour problem is a basic path-finding problem: given a graph G on n vertices, with specified nodes s and t, and a positive integer k, the goal is to determine if G has an st-path of length exactly dist(s,t) + k, where dist(s,t) is the length of a shortest path from s to t. The k-Detour problem is NP-hard when k is part of the input, so researchers have sought efficient parameterized algorithms for this task, running in f(k)poly(n) time, for f(⋅) as slow-growing as possible. We present faster algorithms for k-Detour in undirected graphs, running in 1.853^k poly(n) randomized and 4.082^kpoly(n) deterministic time. The previous fastest algorithms for this problem took 2.746^k poly(n) randomized and 6.523^k poly(n) deterministic time [Bezáková-Curticapean-Dell-Fomin, ICALP 2017]. Our algorithms use the fact that detecting a path of a given length in an undirected graph is easier if we are promised that the path belongs to what we call a "bipartitioned" subgraph, where the nodes are split into two parts and the path must satisfy constraints on those parts. Previously, this idea was used to obtain the fastest known algorithm for finding paths of length k in undirected graphs [Björklund-Husfeldt-Kaski-Koivisto, JCSS 2017], intuitively by looking for paths of length k in randomly bipartitioned subgraphs. Our algorithms for k-Detour stem from a new application of this idea, which does not involve choosing the bipartitioned subgraphs randomly. Our work has direct implications for the k-Longest Detour problem, another related path-finding problem. In this problem, we are given the same input as in k-Detour, but are now tasked with determining if G has an st-path of length at least dist(s,t)+k. Our results for k-Detour imply that we can solve k-Longest Detour in 3.432^k poly(n) randomized and 16.661^k poly(n) deterministic time. The previous fastest algorithms for this problem took 7.539^k poly(n) randomized and 42.549^k poly(n) deterministic time [Fomin et al., STACS 2022].

The min-diameter of a directed graph $G$ is a measure of the largest distance between nodes. It is equal to the maximum min-distance $d_{min}(u,v)$ across all pairs $u,v \in V(G)$, where $d_{min}(u,v) = \min(d(u,v), d(v,u))$. Our work provides a $O(m^{1.426}n^{0.288})$-time $3/2$-approximation algorithm for min-diameter in DAGs, and a faster $O(m^{0.713}n)$-time almost-$3/2$-approximation variant. (An almost-$α$-approximation algorithm determines the min-diameter to within a multiplicative factor of $α$ plus constant additive error.) By a conditional lower bound result of [Abboud et al, SODA 2016], a better than $3/2$-approximation can't be achieved in truly subquadratic time under the Strong Exponential Time Hypothesis (SETH), so our result is conditionally tight. We additionally obtain a new conditional lower bound for min-diameter approximation in general directed graphs, showing that under SETH, one cannot achieve an approximation factor below 2 in truly subquadratic time. We also present the first study of approximating bichromatic min-diameter, which is the maximum min-distance between oppositely colored vertices in a 2-colored graph.

We study the classic Text-to-Pattern Hamming Distances problem: given a pattern P of length m and a text T of length n, both over a polynomial-size alphabet, compute the Hamming distance between P and $T[i \ldots i+m-1]$ for every shift i, under the standard Word-RAM model with $\Theta(\log n)$-bit words.•We provide an $O(n \sqrt{m})$ time Las Vegas randomized algorithm for this problem, beating the decades-old $O(n \sqrt{m \log m})$ running time [Abrahamson, SICOMP 1987]. We also obtain a deterministic algorithm, with a slightly higher $O\left(n \sqrt{m}(\log m \log \log m)^{1 / 4}\right)$ running time. Our randomized algorithm extends to the k-bounded setting, with running time $O\left(n+\frac{n k}{\sqrt{m}}\right)$, removing all the extra logarithmic factors from earlier algorithms [Gawrychowski and Uznanski, ICALP 2018; Chan, Golan, Kociumaka, Kopelowitz and Porat, STOC 2020].•For the $(1+\varepsilon)$-approximate version of Text-to-Pattern Hamming Distances, we give an $\widetilde{O}\left(\varepsilon^{-0.93} n\right)$ time Monte Carlo randomized algorithm (where $\widetilde{O}$ hides poly-logarithmic factors), beating the previous $\widetilde{O}\left(\varepsilon^{-1} n\right)$ running time [Kopelowitz and Porat, FOCS 2015; Kopelowitz and Porat, SOSA 2018].Our approximation algorithm exploits a connection with 3SUM, and uses a combination of Fredman’s trick, equality matrix product, and random sampling; in particular, we obtain new results on approximate counting versions of 3 SUM and Exact Triangle, which may be of independent interest. Our exact algorithms use a novel combination of hashing, bit-packed FFT, and recursion; in particular, we obtain a faster algorithm for computing the sumset of two integer sets, in the regime when the universe size is close to quadratic in the number of elements. We also prove a fine-grained equivalence between the exact Text-to-Pattern Hamming Distances problem and a range-restricted, counting version of 3 SUM.

Abstract. Let [Formula: see text] be an [Formula: see text]-node [Formula: see text]-edge weighted undirected graph, where [Formula: see text] is a real length function defined on its edges, and let [Formula: see text] denote the girth of [Formula: see text], i.e., the length of a shortest cycle. We present an algorithm that, for any input, integer [Formula: see text], in [Formula: see text] expected time finds a cycle of length at most [Formula: see text]. This algorithm nearly matches an [Formula: see text]-time algorithm of Kadria et al. [ Algorithmic trade-offs for girth approximation in undirected graphs, in Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), SIAM, 2022, pp. 1471–1492] which applied to unweighted graphs of girth 3. For weighted graphs, this result also improves upon the previous state-of-the-art algorithm that in [Formula: see text] time, where [Formula: see text] is an integral length function, finds a cycle of length at most [Formula: see text] of Kadria et al. [ Algorithmic trade-offs for girth approximation in undirected graphs, in Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), SIAM, 2022, pp. 1471–1492]. For [Formula: see text], this result improves upon the result of Roditty and Tov [ ACM Trans. Algorithms, 9 (2013), pp. 15:1–15:13].

In this paper we carefully combine Fredman’s trick [SICOMP’76] and Matoušek’s approach for dominance product [IPL’91] to obtain powerful results in fine-grained complexity.

A mapping α:V(G)→V(H) from the vertex set of one graph G to another graph H is an isometric embedding if the shortest path distance between any two vertices in G equals the distance between their images in H. Here, we consider isometric embeddings of a weighted graph G into unweighted Hamming graphs, called Hamming embeddings, when G satisfies the property that every edge is a shortest path between its endpoints. Using a Cartesian product decomposition of G called its canonical isometric representation, we show that every Hamming embedding of G may be partitioned into a canonical partition, whose parts provide Hamming embeddings for each factor of the canonical isometric representation of G. This implies that G permits a Hamming embedding if and only if each factor of its canonical isometric representation is Hamming embeddable. This result extends prior work on unweighted graphs that showed that an unweighted graph permits a Hamming embedding if and only if each factor is a complete graph. When a graph G has nontrivial isometric representation, determining whether G has a Hamming embedding can be simplified to checking embeddability of two or more smaller graphs.

We study the known techniques for designing Matrix Multiplication algorithms. The two main approaches are the Laser method of Strassen, and the group theoretic approach of Cohn and Umans. We define a generalization based on zeroing outs which subsumes these two approaches, which we call the Solar method, and an even more general method based on monomial degenerations, which we call the Galactic method. We then design a suite of techniques for proving lower bounds on the value of $\omega$, the exponent of matrix multiplication, which can be achieved by algorithms using many tensors $T$ and the Galactic method. Some of our techniques exploit “local” properties of $T$, like finding a subtensor of $T$ which is so “weak” that $T$ itself couldn't be used to achieve a good bound on $\omega$, while others exploit “global” properties, like $T$ being a monomial degeneration of the structural tensor of a group algebra. Our main result is that there is a universal constant $\ell>2$ such that a large class of tensors generalizing the Coppersmith--Winograd tensor $CW_q$ cannot be used within the Galactic method to show a bound on $\omega$ better than $\ell$ for any $q$. We give evidence that previous lower-bounding techniques were not strong enough to show this. We also prove a number of complementary results along the way, including that for any group $G$, the structural tensor of $\C[G]$ can be used to recover the best bound on $\omega$ which the Coppersmith--Winograd approach gets using $CW_{|G|-2}$ as long as the asymptotic rank of the structural tensor is not too large.

Measuring the importance of a node in a network is a major goal in the analysis of social networks, biological systems, transportation networks, and so forth. Different centrality measures have been proposed to capture the notion of node importance. For example, the center of a graph is a node that minimizes the maximum distance to any other node (the latter distance is the radius of the graph). The median of a graph is a node that minimizes the sum of the distances to all other nodes. Informally, the betweenness centrality of a node w measures the fraction of shortest paths that have w as an intermediate node. Finally, the reach centrality of a node w is the smallest distance r such that any s - t shortest path passing through w has either s or t in the ball of radius r around w . The fastest known algorithms to compute the center and the median of a graph and to compute the betweenness or reach centrality even of a single node take roughly cubic time in the number n of nodes in the input graph. It is open whether these problems admit truly subcubic algorithms, i.e., algorithms with running time Õ(n 3-δ ) for some constant δ > 0. 1 We relate the complexity of the mentioned centrality problems to two classical problems for which no truly subcubic algorithm is known, namely All Pairs Shortest Paths (APSP) and Diameter. We show that Radius, Median, and Betweenness Centrality are equivalent under subcubic reductions to APSP, i.e., that a truly subcubic algorithm for any of these problems implies a truly subcubic algorithm for all of them. We then show that Reach Centrality is equivalent to Diameter under subcubic reductions. The same holds for the problem of approximating Betweenness Centrality within any finite factor. Thus, the latter two centrality problems could potentially be solved in truly subcubic time, even if APSP required essentially cubic time. On the positive side, our reductions for Reach Centrality imply an improved Õ(Mn ω )-time algorithm for this problem in case of non-negative integer weights upper bounded by M , where ω is a fast matrix multiplication exponent.

Given a graph where every vertex has exactly one labeled token, how can we most quickly execute a given permutation on the tokens? In (sequential) token swapping, the goal is to use the shortest possible sequence of swaps, each of which exchanges the tokens at the two endpoints of an edge of the graph. In parallel token swapping, the goal is to use the fewest rounds, each of which consists of one or more swaps on the edges of a matching. We prove that both of these problems remain NP-hard when the graph is restricted to be a tree. These token swapping problems have been studied by disparate groups of researchers in discrete mathematics, theoretical computer science, robot motion planning, game theory, and engineering. Previous work establishes NP-completeness on general graphs (for both problems), constant-factor approximation algorithms, and some poly-time exact algorithms for simple graph classes such as cliques, stars, paths, and cycles. Sequential and parallel token swapping on trees were first studied over thirty years ago (as "sorting with a transposition tree") and over twenty-five years ago (as "routing permutations via matchings"), yet their complexities were previously unknown. We also show limitations on approximation of sequential token swapping on trees: we identify a broad class of algorithms that encompass all three known polynomial-time algorithms that achieve the best known approximation factor (which is 2) and show that no such algorithm can achieve an approximation factor less than 2.

We study the approximability of two related problems on graphs with n nodes and m edges: n-Pairs Shortest Paths (n-PSP), where the goal is to find a shortest path between O(n) prespecified pairs, and All Node Shortest Cycles (ANSC), where the goal is to find the shortest cycle passing through each node. Approximate n-PSP has been previously studied, mostly in the context of distance oracles. We ask the question of whether approximate n-PSP can be solved faster than by using distance oracles or All Pair Shortest Paths (APSP). ANSC has also been studied previously, but only in terms of exact algorithms, rather than approximation.We provide a thorough study of the approximability of n PSP and ANSC, providing a wide array of algorithms and conditional lower bounds that trade off between running time and approximation ratio.A highlight of our conditional lower bounds results is that for any integer k$\geq$1, under the combinatorial 4k-clique hypothesis, there is no combinatorial algorithm for unweighted undirected n-PSP with approximation ratio better than $1+1/k$ that runs in $O(m^{2-2/(k+1)}n^{1/(k+1)-\varepsilon})$ time. This nearly matches an upper bound implied by the result of Agarwal (2014).Our algorithms use a surprisingly wide range of techniques, including techniques from the girth problem, distance oracles, approximate APSP, spanners, fault-tolerant spanners, and link-cut trees.A highlight of our algorithmic results is that one can solve both n-PSP and ANSC in $O(m+n^{3/2+\in})$ time1with approximation factor $2+\varepsilon$ (and additive error that is function of $\varepsilon$), for any constant $\varepsilon\lt 0$. For n-PSP, our conditional lower bounds imply that this approximation ratio is nearly optimal for any subquadratic-time combinatorial algorithm. We further extend these algorithms for n-PSP and ANSC to obtain a time/accuracy trade-off that includes near-linear time algorithms.1$\tilde{O}$ hides sub-polynomial factors.Additionally, for ANSC, for all integers $k\geq 1$, we extend the very recent almost k-approximation algorithm for the girth problem that works in $\tilde{O}(n^{1+1/k})$ time [Kadria et al. SODA’22], and obtain an almost k-approximation algorithm for ANSC in $\tilde{O}(mn^{1/k})$ time.

The goal of the paper is to give fine-grained hardness results for the Subgraph Isomorphism (SI) problem for fixed size induced patterns H, based on the k-Clique hypothesis that the current best algorithms for Clique are optimal. Our first main result is that for any pattern graph H that is a core, the SI problem for H is at least as hard as t-Clique, where t is the size of the largest clique minor of H. This improves (for cores) the previous known results [Dalirrooyfard-Vassilevska W. STOC’20] that the SI for H is at least as hard as k-clique where k is the size of the largest clique subgraph in H, or the chromatic number of H (under the Hadwiger conjecture). For detecting any graph pattern H, we further remove the dependency of the result of [Dalirrooyfard-Vassilevska W. STOC’20] on the Hadwiger conjecture at the cost of a sub-polynomial decrease in the lower bound. The result for cores allows us to prove that the SI problem for induced k-Path and k-Cycle is harder than previously known. Previously [Floderus et al. Theor. CS 2015] had shown that k-Path and k-Cycle are at least as hard to detect as a $\lfloor$k/2$\rfloor -$Clique. We show that they are in fact at least as hard as 3k/4-O(1)-Clique, improving the conditional lower bound exponent by a factor of 3/2. This shoivs for instance that the knoivn $O(n^{5})$ combinatorial algorithm for 7-cycle detection is conditionally tight. Finally, we provide a new conditional lower bound for detecting induced 4-cycles: $n^{2-o(1)}$ time is necessary even in graphs with n nodes and $O(n^{15})$ edges. The 4-cycle is the smallest induced pattern whose running time is not well-understood. It can be solved in matrix multiplication, $O(n^{\omega})$ time, but no conditional lower bounds were known until ours. We provide evidence that certain types of reductions from triangle detection to 4-Cycle would not be possible. We do this by studying a new problem called Paired Pattern Detection.

This paper considers a natural fault-tolerant shortest paths problem: for some constant integer f, given a directed weighted graph with no negative cycles and two fixed vertices s and t, compute (either explicitly or implicitly) for every tuple of f edges, the distance from s to t if these edges fail. We call this problem f-Fault Replacement Paths (f FRP).We first present an $\tilde{O}(n^{3}$) time algorithm for 2FRP in n-vertex directed graphs with arbitrary edge weights and no negative cycles. As 2FRP is a generalization of the well-studied Replacement Paths problem (RP) that asks for the distances between s and t for any single edge failure, 2FRP is at least as hard as RP. Since RP in graphs with arbitrary weights is equivalent in a fine-grained sense to All-Pairs Shortest Paths (APSP) [Vassilevska Williams and Williams FOCS’10, J. ACM’18], 2FRP is at least as hard as APSP, and thus a substantially subcubic time algorithm in the number of vertices for 2FRP would be a breakthrough. Therefore, our algorithm in $\tilde{O}(n^{3})$ time is conditionally nearly optimal. Our algorithm immediately implies an $\tilde{O}(n^{f+1})$ time algorithm for the more general f FRP problem, giving the first improvement over the straightforward $O(n^{f+2})$ time algorithm.Then we focus on the restriction of 2FRP to graphs with small integer weights bounded by M in absolute values. We show that similar to $\mathrm{R}\mathrm{P}, 2\mathrm{F}\mathrm{R}\mathrm{P}$ has a substantially subcubic time algorithm for small enough M. Using the current best algorithms for rectangular matrix multiplication, we obtain a randomized algorithm that runs in $\tilde{O}(M^{2/3}n^{2.9153})$ time. This immediately implies an improvement over our $\tilde{O}(n^{f+1})$ time arbitrary weight algorithm for all $f\gt1$. We also present a data structure variant of the algorithm that can trade off pre-processing and query time. In addition to the algebraic algorithms, we also give an $n^{8/3-o(1)}$ conditional lower bound for combinatorial 2FRP algorithms in directed unweighted graphs, and more generally, combinatorial lower bounds for the data structure version of $fF\mathrm{R}\mathrm{P}$.

The AP-LCA problem asks, given an n-node directed acyclic graph (DAG), to compute for every pair of vertices u and v in the DAG a lowest common ancestor (LCA) of u and v if one exists, i.e. a node that is an ancestor of both u and v but no proper descendent of it is their common ancestor. Recently [Grandoni et al. SODA'21] obtained the first sub-n^{2.5} time algorithm for AP-LCA running in O(n^{2.447}) time. Meanwhile, the only known conditional lower bound for AP-LCA is that the problem requires n^{ω-o(1)} time where ω is the matrix multiplication exponent. In this paper we study several interesting variants of AP-LCA, providing both algorithms and fine-grained lower bounds for them. The lower bounds we obtain are the first conditional lower bounds for LCA problems higher than n^{ω-o(1)}. Some of our results include: - In any DAG, we can detect all vertex pairs that have at most two LCAs and list all of their LCAs in O(n^ω) time. This algorithm extends a result of [Kowaluk and Lingas ESA'07] which showed an Õ(n^ω) time algorithm that detects all pairs with a unique LCA in a DAG and outputs their corresponding LCAs. - Listing 7 LCAs per vertex pair in DAGs requires n^{3-o(1)} time under the popular assumption that 3-uniform 5-hyperclique detection requires n^{5-o(1)} time. This is surprising since essentially cubic time is sufficient to list all LCAs (if ω = 2). - Counting the number of LCAs for every vertex pair in a DAG requires n^{3-o(1)} time under the Strong Exponential Time Hypothesis, and n^{ω(1,2,1)-o(1)} time under the 4-Clique hypothesis. This shows that the algorithm of [Echkardt, Mühling and Nowak ESA'07] for listing all LCAs for every pair of vertices is likely optimal. - Given a DAG and a vertex w_{u,v} for every vertex pair u,v, verifying whether all w_{u,v} are valid LCAs requires n^{2.5-o(1)} time assuming 3-uniform 4-hyperclique requires n^{4-o(1)} time. This defies the common intuition that verification is easier than computation since returning some LCA per vertex pair can be solved in O(n^{2.447}) time.

We present an almost optimal algorithm for the classic Chamberlin-Courant multiwinner voting rule (CC) on single-peaked preference profiles. Given n voters and m candidates, it runs in almost linear time in the input size improving the previous best O(nm^2) time algorithm. We also study multiwinner voting rules on nearly single-peaked preference profiles in terms of the candidate-deletion operation. We show a polynomial-time algorithm for CC where a given candidate-deletion set D has logarithmic size. Actually, our algorithm runs in 2^|D| * poly(n,m) time and the base of the power cannot be improved under the Strong Exponential Time Hypothesis. We also adapt these results to all non-constant Thiele rules which generalize CC with approval ballots.

A simplicial vertex of a graph is a vertex whose neighborhood is a clique. It is known that listing all simplicial vertices can be done in $O(nm)$ time or $O(n^ω)$ time, where $O(n^ω)$ is the time needed to perform a fast matrix multiplication. The notion of avoidable vertices generalizes the concept of simplicial vertices in the following way: a vertex $u$ is avoidable if every induced path on three vertices with middle vertex $u$ is contained in an induced cycle. We present algorithms for listing all avoidable vertices of a graph through the notion of minimal triangulations and common neighborhood detection. In particular we give algorithms with running times $O(n^{2}m)$ and $O(n^{1+ω})$, respectively. Additionally, based on a simplified graph traversal we propose a fast algorithm that runs in time $O(n^2 + m^2)$ and matches the corresponding running time of listing all simplicial vertices on sparse graphs with $m=O(n)$. Moreover, we show that our algorithms cannot be improved significantly, as we prove that under plausible complexity assumptions there is no truly subquadratic algorithm for recognizing an avoidable vertex. To complement our results, we consider their natural generalizations of avoidable edges and avoidable paths. We propose an $O(nm)$-time algorithm that recognizes whether a given induced path is avoidable.

We present several new efficient algorithms for approximating the girth, g, of weighted and unweighted n-vertex, m-edge undirected graphs. For undirected graphs with polynomially bounded, integer, non-negative edge weights, we provide an algorithm that for every integer k ≥ 1, runs in Õ(m + n1 + 1/k log g) time and returns a cycle of length at most 2kg. For unweighted, undirected graphs we present an algorithm that for every k ≥ 1, runs in Õ(n1 + 1/k) time and returns a cycle of length at most 2k[g/2], an almost k-approximation. Both algorithms provide trade-offs between the running time and the quality of the approximation. We also obtain faster algorithms for approximation factors better than 2, and improved approximations when the girth is odd or small (e.g., 3 and 4).

We obtain improved lower bounds for additive spanners, additive emulators, and diameter-reducing shortcut sets. Spanners and emulators are sparse graphs that approximately preserve the distances of a given graph. A shortcut set is a set of edges that when added to a directed graph, decreases its diameter. The previous best known lower bounds for these three structures are given by Huang and Pettie [HP18]. For O(n)-sized spanners, we improve the lower bound on the additive stretch from Ω(n1/11) to Ω(n2/21). For O(n)-sized emulators, we improve the lower bound on the additive stretch from Ω(n1/18) to Ω(n2/29). For O(m)-sized shortcut sets, we improve the lower bound on the graph diameter from Ω(n1/11) to Ω(n1/8). Our key technical contribution, which is the basis of all of our bounds, is an improvement of a graph product known as an alternation product.

The 3SUM hypothesis, the All-Pairs Shortest Paths (APSP) hypothesis and the Strong Exponential Time Hypothesis are the three main hypotheses in the area of fine-grained complexity. So far, within the area, the first two hypotheses have mainly been about integer inputs in the Word RAM model of computation. The “Real APSP” and “Real 3SUM” hypotheses, which assert that the APSP and 3SUM hypotheses hold for real-valued inputs in a reasonable version of the Real RAM model, are even more believable than their integer counterparts.

3SUM is a simple to state problem: given a set S of n numbers, determine whether S contains three a,b,c so that a+b+c = 0. The fastest algorithms for the problem run in n² poly(log log n)/(log n)² time both when the input numbers are integers [Ilya Baran et al., 2005] (in the word RAM model with O(log n) bit words) and when they are real numbers [Timothy M. Chan, 2020] (in the real RAM model). A hypothesis that is now central in Fine-Grained Complexity (FGC) states that 3SUM requires n^{2-o(1)} time (on the real RAM for real inputs and on the word RAM with O(log n) bit numbers for integer inputs). This hypothesis was first used in Computational Geometry by Gajentaan and Overmars [A. Gajentaan and M. Overmars, 1995] who built a web of reductions showing that many geometric problems are hard, assuming that 3SUM is hard. The web of reductions within computational geometry has grown considerably since then (see some citations in [V. Vassilevska Williams, 2018]). A seminal paper by Pǎtraşcu [Mihai Pǎtraşcu, 2010] showed that the integer version of the 3SUM hypothesis can be used to prove polynomial conditional lower bounds for several problems in data structures and graph algorithms as well, extending the implications of the hypothesis to outside computational geometry. Pǎtraşcu proved an important tight equivalence between (integer) 3SUM and a problem called 3SUM-Convolution (see also [Timothy M. Chan and Qizheng He, 2020]) that is easier to use in reductions: given an integer array a of length n, do there exist i,j ∈ [n] so that a[i]+a[j] = a[i+j]. From 3SUM-Convolution, many 3SUM-based hardness results have been proven: e.g. to listing graphs in triangles, dynamically maintaining shortest paths or bipartite matching, subset intersection and many more. It is interesting to consider more runtime-equivalent formulations of 3SUM, with the goal of uncovering more relationships to different problems. The talk will outline some such equivalences. For instance, 3SUM (over the reals or the integers) is equivalent to All-Numbers-3SUM: given a set S of n numbers, determine for every a ∈ S whether there are b,c ∈ S with a+b+c = 0 (e.g. [V. Vassilevska Williams and R. Williams, 2018]). The equivalences between 3SUM, 3SUM-Convolution and All-Numbers 3SUM are (n²,n²)-fine-grained equivalences that imply that if there is an O(n^{2-ε}) time algorithm for one of the problems for ε > 0, then there is also an O(n^{2-ε'}) time algorithm for the other problems for some ε' > 0. More generally, for functions a(n),b(n), there is an (a,b)-fine-grained reduction [V. Vassilevska Williams, 2018; V. Vassilevska Williams and R. Williams, 2010; V. Vassilevska Williams and R. Williams, 2018] from problem A to problem B if for every ε > 0 there is a δ > 0 and an O(a(n)^{1-δ}) time algorithm for A that does oracle calls to instances of B of sizes n₁,…,n_k (for some k) so that ∑_{j = 1}^k b(n_j)^{1-ε} ≤ a(n)^{1-δ}. With such a reduction, an O(b(n)^{1-ε}) time algorithm for B can be converted into an O(a(n)^{1-δ}) time algorithm for A by replacing the oracle calls by calls to the B algorithm. A and B are (a,b)-fine-grained equivalent if A (a,b)-reduces to B and B (b,a)-reduces to A. One of the main open problems in FGC is to determine the relationship between 3SUM and the other central FGC problems, in particular All-Pairs Shortest Paths (APSP). A classical graph problem, APSP in n node graphs has been known to be solvable in O(n³) time since the 1950s. Its fastest known algorithm runs in n³/exp(√{log n}) time [Ryan Williams, 2014]. The APSP Hypothesis states that n^{3-o(1)} time is needed to solve APSP in graphs with integer edge weights in the word-RAM model with O(log n) bit words. It is unknown whether APSP and 3SUM are fine-grained reducible to each other, in either direction. The two problems are very similar. Problems such as (min,+)-convolution (believed to require n^{2-o(1)} time) have tight fine-grained reductions to both APSP and 3SUM, and both 3SUM and APSP have tight fine-grained reductions to problems such as Exact Triangle [V. Vassilevska Williams and R. Williams, 2018; V. Vassilevska and R. Williams, 2009; V. Vassilevska Williams and Ryan Williams, 2013] and (since very recently) Listing triangles in sparse graphs [Mihai Pǎtraşcu, 2010; Tsvi Kopelowitz et al., 2016; V. Vassilevska Williams and Yinzhan Xu, 2020]. The talk will discuss these relationships and some of their implications, e.g. to dynamic algorithms.

Approximating the graph diameter is a basic task of both theoretical and practical interest. A simple folklore algorithm can output a 2-approximation to the diameter in linear time by running BFS from an arbitrary vertex. It has been open whether a better approximation is possible in near-linear time. A series of papers on fine-grained complexity have led to strong hardness results for diameter in directed graphs, culminating in a recent tradeoff curve independently discovered by [Li, STOC'21] and [Dalirrooyfard and Wein, STOC'21], showing that under the Strong Exponential Time Hypothesis (SETH), for any integer$k\geq 2$and$\delta > 0$, a$2-\frac{1}{k}-\delta$approximation for diameter in directed$m$-edge graphs requires$mn^{1+1/(k-1)-o(1)}$time. In particular, the simple linear time 2-approximation algorithm is optimal for directed graphs. In this paper we prove that the same tradeoff lower bound curve is possible for undirected graphs as well, extending results of [Roditty and Vassilevska W., STOC'13], [Li'20] and [Bonnet, ICALP'21] who proved the first few cases of the curve,$k=2,3$and 4, respectively. Our result shows in particular that the simple linear time 2-approximation algorithm is also optimal for undirected graphs. To obtain our result we develop new tools for fine-grained reductions that could be useful for proving SETH-based hardness for other problems in undirected graphs related to distance computation.

Nearly all quadratic lower bounds conditioned on the Strong Exponential Time Hypothesis (SETH) start by reducing k-SAT to the Orthogonal Vectors (OV) problem: Given two sets A,B of n binary vectors, decide if there is an orthogonal pair a ∈ A, b ∈ B. In this paper, we give an alternative reduction in which the set A does not depend on the input to k-SAT; thus, the quadratic lower bound for OV holds even if one of the sets is fixed in advance. Using the reductions in the literature from OV to other problems such as computing similarity measures on strings, we get hardness results of a stronger kind: there is a family of sequences {S_n}_{n = 1}^{∞}, |S_n| = n such that computing the Edit Distance between an input sequence X of length n and the (fixed) sequence S_n requires n^{2-o(1)} time under SETH.

This paper investigates the approximability of the Longest Common Subsequence (LCS) problem. The fastest algorithm for solving the LCS problem exactly runs in essentially quadratic time in the length of the input, and it is known that under the Strong Exponential Time Hypothesis the quadratic running time cannot be beaten. There are no such limitations for the approximate computation of the LCS however, except in some limited scenarios. There is also a scarcity of approximation algorithms. When the two given strings are over an alphabet of size $k$, returning the subsequence formed by the most frequent symbol occurring in both strings achieves a $1/k$ approximation for the LCS. It is an open problem whether a better than $1/k$ approximation can be achieved in truly subquadratic time ($O(n^{2-δ})$ time for constant $δ>0$). A recent result [Rubinstein and Song SODA'2020] showed that a $1/2+ε$ approximation for the LCS over a binary alphabet is possible in truly subquadratic time, provided the input strings have the same length. In this paper we show that if a $1/2+ε$ approximation (for $ε>0$) is achievable for binary LCS in truly subquadratic time when the input strings can be unequal, then for every constant $k$, there is a truly subquadratic time algorithm that achieves a $1/k+δ$ approximation for $k$-ary alphabet LCS for some $δ>0$. Thus the binary case is the hardest. We also show that for every constant $k$, if one is given two strings of \emph{equal} length over a $k$-ary alphabet, one can obtain a $1/k+ε$ approximation for some constant $ε>0$ in truly subquadratic time, thus extending the Rubinstein and Song result to all alphabets of constant size.

All-Pairs Shortest Paths (APSP) is one of the most well studied problems in graph algorithms. This paper studies several variants of APSP in unweighted graphs or graphs with small integer weights. APSP with small integer weights in undirected graphs [Seidel'95, Galil and Margalit'97] has an Õ(n^ω) time algorithm, where ω < 2.373 is the matrix multiplication exponent. APSP in directed graphs with small weights however, has a much slower running time that would be Ω(n^{2.5}) even if ω = 2 [Zwick'02]. To understand this n^{2.5} bottleneck, we build a web of reductions around directed unweighted APSP . We show that it is fine-grained equivalent to computing a rectangular Min-Plus product for matrices with integer entries; the dimensions and entry size of the matrices depend on the value of ω. As a consequence, we establish an equivalence between APSP in directed unweighted graphs, APSP in directed graphs with small (Õ(1)) integer weights, All-Pairs Longest Paths in DAGs with small weights, cRed-APSP in undirected graphs with small weights, for any c ≥ 2 (computing all-pairs shortest path distances among paths that use at most c red edges), #_{≤ c}APSP in directed graphs with small weights (counting the number of shortest paths for each vertex pair, up to c), and approximate APSP with additive error c in directed graphs with small weights, for c ≤ Õ(1). We also provide fine-grained reductions from directed unweighted APSP to All-Pairs Shortest Lightest Paths (APSLP) in undirected graphs with {0,1} weights and #_{mod c}APSP in directed unweighted graphs (computing counts mod c), thus showing that unless the current algorithms for APSP in directed unweighted graphs can be improved substantially, these problems need at least Ω(n^{2.528}) time. We complement our hardness results with new algorithms. We improve the known algorithms for APSLP in directed graphs with small integer weights (previously studied by Zwick [STOC'99]) and for approximate APSP with sublinear additive error in directed unweighted graphs (previously studied by Roditty and Shapira [ICALP'08]). Our algorithm for approximate APSP with sublinear additive error is optimal, when viewed as a reduction to Min-Plus product. We also give new algorithms for variants of #APSP (such as #_{≤ U}APSP and #_{mod U}APSP for U ≤ n^{Õ(1)}) in unweighted graphs, as well as a near-optimal Õ(n³)-time algorithm for the original #APSP problem in unweighted graphs (when counts may be exponentially large). This also implies an Õ(n³)-time algorithm for Betweenness Centrality, improving on the previous Õ(n⁴) running time for the problem. Our techniques also lead to a simpler alternative to Shoshan and Zwick’s algorithm [FOCS'99] for the original APSP problem in undirected graphs with small integer weights.

One of the most basic graph problems, All-Pairs Shortest Paths (APSP) is known to be solvable in $n^{3-o(1)}$ time, and it is widely open whether it has an $O(n^{3-ε})$ time algorithm for $ε> 0$. To better understand APSP, one often strives to obtain subcubic time algorithms for structured instances of APSP and problems equivalent to it, such as the Min-Plus matrix product. A natural structured version of Min-Plus product is Monotone Min-Plus product which has been studied in the context of the Batch Range Mode [SODA'20] and Dynamic Range Mode [ICALP'20] problems. This paper improves the known algorithms for Monotone Min-Plus Product and for Batch and Dynamic Range Mode, and establishes a connection between Monotone Min-Plus Product and the Single Source Replacement Paths (SSRP) problem on an $n$-vertex graph with potentially negative edge weights in $\{-M, \ldots, M\}$. SSRP with positive integer edge weights bounded by $M$ can be solved in $\tilde{O}(Mn^ω)$ time, whereas the prior fastest algorithm for graphs with possibly negative weights [FOCS'12] runs in $O(M^{0.7519} n^{2.5286})$ time, the current best running time for directed APSP with small integer weights. Using Monotone Min-Plus Product, we obtain an improved $O(M^{0.8043} n^{2.4957})$ time SSRP algorithm, showing that SSRP with constant negative integer weights is likely easier than directed unweighted APSP, a problem that is believed to require $n^{2.5-o(1)}$ time. Complementing our algorithm for SSRP, we give a reduction from the Bounded-Difference Min-Plus Product problem studied by Bringmann et al. [FOCS'16] to negative weight SSRP. This reduction shows that it might be difficult to obtain an $\tilde{O}(M n^ω)$ time algorithm for SSRP with negative weight edges, thus separating the problem from SSRP with only positive weight edges.

We study computational aspects of a well-known single-winner voting rule called the Schulze method [Schulze, 2003] which is used broadly in practice. In this method the voters give (weak) ordinal preference ballots which are used to define the weighted majority graph of direct comparisons between pairs of candidates. The choice of the winner comes from indirect comparisons in the graph, and more specifically from considering directed paths instead of direct comparisons between candidates. When the input is the weighted majority graph, to our knowledge, the fastest algorithm for computing all winners in the Schulze method uses a folklore reduction to the All-Pairs Bottleneck Paths (APBP) problem and runs in $Øh(m2.69) time, where m is the number of candidates. It is an interesting open question whether this can be improved. Our first result is a combinatorial algorithm with a nearly quadratic running time for computing all winners. This running time is essentially optimal as it is nearly linear in the size of the weighted majority graph. If the input to the Schulze winners problem is not the weighted majority graph but the preference profile, then constructing the weighted majority graph is a bottleneck that increases the running time significantly; in the special case when there are m candidates and n=Øh(m) voters, the running time is Øh(m2.69), or $Øh(m2.5) if there is a nearly-linear time algorithm for multiplying dense square matrices. To address this bottleneck, we prove a formal equivalence between the well-studied Dominance Product problem and the problem of computing the weighted majority graph. As the Dominance Product problem is believed to require at least time r2.5-o(1) on r x r matrices, our equivalence implies that constructing the weighted majority graph in $Øh(m2.499) time for m candidates and n = Øh(m) voters would imply a breakthrough in the study of "intermediate" problems [Lincoln et al., 2020] in fine-grained complexity. We prove a similar connection between the so called Dominating Pairs problem and the problem of verifying whether a given candidate is a winner. Our paper is the first to bring fine-grained complexity into the field of computational social choice. Previous approaches say nothing about lower bounds for problems that already have polynomial time algorithms. By bringing fine-grained complexity into the picture we can identify voting protocols that are unlikely to be practical for large numbers of candidates and/or voters, as their complexity is likely, say at least cubic.

The complexity of matrix multiplication is measured in terms of ω, the smallest real number such that two n × n matrices can be multiplied using O(nω+∊) field operations for all ∊ > 0; the best bound until now is ω < 2.37287 [Le Gall'14]. All bounds on ω since 1986 have been obtained using the so-called laser method, a way to lower-bound the ‘value’ of a tensor in designing matrix multiplication algorithms. The main result of this paper is a refinement of the laser method that improves the resulting value bound for most sufficiently large tensors. Thus, even before computing any specific values, it is clear that we achieve an improved bound on ω, and we indeed obtain the best bound on ω to date: ω < 2.37286. The improvement is of the same magnitude as the improvement that [Le Gall'14] obtained over the previous bound [Vassilevska W.'12]. Our improvement to the laser method is quite general, and we believe it will have further applications in arithmetic complexity.

Maintaining and updating shortest paths information in a graph is a fundamental problem with many applications. As computations on dense graphs can be prohibitively expensive, and it is preferable to perform the computations on a sparse skeleton of the given graph that roughly preserves the shortest paths information. Spanners and emulators serve this purpose. Unfortunately, very little is known about dynamically maintaining sparse spanners and emulators as the graph is modified by a sequence of edge insertions and deletions. This paper develops fast dynamic algorithms for spanner and emulator maintenance and provides evidence from fine-grained complexity that these algorithms are tight. For unweighted undirected m-edge n-node graphs we obtain the following results. Under the popular OMv conjecture, there can be no decremental or incremental algorithm that maintains an n1+o(1) edge (purely additive) +nδ-emulator for any δ < 1/2 with arbitrary polynomial preprocessing time and total update time m1+o(1). Also, under the Combinatorial k-Clique hypothesis, any fully dynamic combinatorial algorithm that maintains an n1+o(1) edge (1 + ∊, no(1))-spanner or emulator for small ∊ must either have preprocessing time mn1–o(1) or amortized update time m1–o(1). Both of our conditional lower bounds are tight. As the above fully dynamic lower bound only applies to combinatorial algorithms, we also develop an algebraic spanner algorithm that improves over the m1–o(1) update time for dense graphs. For any constant ∊ ∊ (0, 1], there is a fully dynamic algorithm with worst-case update time O(n1.529) that whp maintains an n1+o(1) edge (1 + ∊, no(1))-spanner. Our new algebraic techniques allow us to also obtain a new fully dynamic algorithm for All-Pairs Shortest Paths (APSP) that can perform both edge updates and can report shortest paths in worst-case time O(n1.9), which are correct whp. This is the first path-reporting fully dynamic APSP algorithm with a truly subquadratic query time that beats O(n2.5) update time. It works against an oblivious adversary. Finally, we give two applications of our new dynamic spanner algorithms: (1) a fully dynamic (1 + ∊)-approximate APSP algorithm with update time O(n1.529) that can report approximate shortest paths in n1+o(1) time per query; previous subquadratic update/query algorithms could only report the distance, but not obtain the paths; (2) a fully dynamic algorithm for near-2-approximate Steiner tree maintenance with both terminal and edge updates.

Among the most important graph parameters is the diameter, the largest distance between any two vertices. There are no known very efficient algorithms for computing the diameter exactly. Thus, much research has been devoted to how fast this parameter can be approximated. Chechik et al. [ Proceedings of SODA 2014, Portland, OR, 2014, pp. 1041--1052] showed that the diameter can be approximated within a multiplicative factor of 3/2 in $\tilde{O}(m^{3/2})$ time. Furthermore, Roditty and Vassilevska W. [ Proceedings of STOC '13, New York, ACM, 2013, pp. 515--524] showed that unless the strong exponential time hypothesis (SETH) fails, no $O(n^{2-{\varepsilon}})$ time algorithm can achieve an approximation factor better than 3/2 in sparse graphs. Thus the above algorithm is essentially optimal for sparse graphs for approximation factors less than 3/2. It was, however, completely plausible that a 3/2-approximation is possible in linear time. In this work we conditionally rule out such a possibility by showing that unless SETH fails no $O(m^{3/2-{\varepsilon}})$ time algorithm can achieve an approximation factor better than 5/3. Another fundamental set of graph parameters is the eccentricities. The eccentricity of a vertex $v$ is the distance between $v$ and the farthest vertex from $v$. Chechik et al. [ Proceedings of SODA 2014, Portland, OR, 2014, pp. 1041--1052] showed that the eccentricities of all vertices can be approximated within a factor of $5/3$ in $\tilde{O}(m^{3/2})$ time and Abboud, Vassilevska W., and Wang [ Proceedings of SODA 2016, Arlington, VA, 2016, pp. 377--391] showed that no $O(n^{2-{\varepsilon}})$ algorithm can achieve better than 5/3 approximation in sparse graphs. We show that the runtime of the 5/3 approximation algorithm is also optimal by proving that under SETH, there is no $O(m^{3/2-{\varepsilon}})$ algorithm that achieves a better than 9/5 approximation. We also show that no near-linear time algorithm can achieve a better than 2 approximation for the eccentricities. This is the first lower bound in fine-grained complexity that addresses near-linear time computation. We show that our lower bound for near-linear time algorithms is essentially tight by giving an algorithm that approximates eccentricities within a $2+\delta$ factor in $\tilde{O}(m/\delta)$ time for any $0<\delta<1$. This beats all eccentricity algorithms in Cairo, Grossi, and Rizzi [ Proceedings of SODA 2016, Arlington, VA, 2016, pp. 363--376] and is the first constant factor approximation for eccentricities in directed graphs. To establish the above lower bounds we study the $S$-$T$ diameter problem: Given a graph and two subsets $S$ and $T$ of vertices, output the largest distance between a vertex in $S$ and a vertex in $T$. We give new algorithms and show tight lower bounds that serve as a starting point for all other hardness results. Our lower bounds apply only to sparse graphs. We show that for dense graphs, there are near-linear time algorithms for $S$-$T$ diameter, diameter, and eccentricities, with almost the same approximation guarantees as their $\tilde{O}(m^{3/2})$ counterparts, improving upon the best known algorithms for dense graphs.

We consider the pattern detection problem in graphs: given a constant size pattern graph $H$ and a host graph $G$, determine whether $G$ contains a subgraph isomorphic to $H$. We present the following new improved upper and lower bounds: We prove that if a pattern $H$ contains a $k$-clique subgraph, then detecting whether an $n$ node host graph contains a not necessarily induced copy of $H$ requires at least the time for detecting whether an $n$ node graph contains a $k$-clique. The previous result of this nature required that $H$ contains a $k$-clique which is disjoint from all other $k$-cliques of $H$. We show that if the famous Hadwiger conjecture from graph theory is true, then detecting whether an $n$ node host graph contains a not necessarily induced copy of a pattern with chromatic number $t$ requires at least the time for detecting whether an $n$ node graph contains a $t$-clique. This implies that (1) under Hadwiger's conjecture for every $k$-node pattern $H$, finding an induced copy of $H$ requires at least the time of $\sqrt k$-clique detection and size $\omega(n^{\sqrt{k}/4})$ for any constant depth circuit, and (2) unconditionally, detecting an induced copy of a random $G(k,p)$ pattern with high probability requires at least the time of $\Theta(k/\log k)$-clique detection, and hence also at least size $n^{\Omega(k/\log k)}$ for circuits of constant depth. We show that for every $k$, there exists a $k$-node pattern that contains a $k-1$-clique and that can be detected as an induced subgraph in $n$ node graphs in the best known running time for $k-1$-clique detection. Previously such a result was only known for infinitely many $k$. Finally, we consider the case when the pattern is a directed cycle on $k$ nodes, and we would like to detect whether a directed $m$-edge graph $G$ contains a $k$-cycle as a not necessarily induced subgraph. We resolve a 14- year-old conjecture of [Yuster and Zwick, Proceedings of SODA, 2004, pp. 247--253] on the complexity of $k$-cycle detection by giving a tight analysis of their $k$-cycle algorithm. Our analysis improves the best bounds for $k$-cycle detection in directed graphs for all $k>5$.

We study two popular ways to sketch the shortest path distances of an input graph. The first is distance preservers , which are sparse subgraphs that agree with the distances of the original graph on a given set of demand pairs. Prior work on distance preservers has exploited only a simple structural property of shortest paths, called consistency , stating that one can break shortest path ties such that no two paths intersect, split apart, and then intersect again later. We prove that consistency alone is not enough to understand distance preservers, by showing both a lower bound on the power of consistency and a new general upper bound that polynomially surpasses it. Specifically, our new upper bound is that any p demand pairs in an n -node undirected unweighted graph have a distance preserver on O( n 2/3 p 2/3 + np 1/3 edges. We leave a conjecture that the right bound is O ( n 2/3 p 2/3 + n ) or better. The second part of this paper leverages these distance preservers in a new construction of additive spanners , which are subgraphs that preserve all pairwise distances up to an additive error function. We give improved error bounds for spanners with relatively few edges; for example, we prove that all graphs have spanners on O(n) edges with + O ( n 3/7 + ε ) error. Our construction can be viewed as an extension of the popular path-buying framework to clusters of larger radii.

The recent emergence of fine-grained cryptography strongly motivates developing an average-case analogue of Fine-Grained Complexity (FGC). Prior work [Goldreich-Rothblum 2018, Boix-Adserà et al. 2019, Ball et al. 2017] developed worst-case to average-case fine-grained reductions (WCtoACFG) for certain algebraic and counting problems over natural distributions and used them to obtain a limited set of cryptographic primitives. To obtain stronger cryptographic primitives based on standard FGC assumptions, ideally, one would like to develop WCtoACFG reductions from the core hard problems of FGC, Orthogonal Vectors (OV), CNF-SAT, 3SUM, All-Pairs Shortest Paths (APSP) and zero- k clique. Unfortunately, it is unclear whether these problems actually are hard for any natural distribution. It is known, that e.g. OV can be solved quickly for very natural distributions [Kane-Williams 2019], and in this paper we show that even counting the number of OV pairs on average has a fast algorithm. This paper defines new versions of OV, kSUM and zero- k-clique that are both worst-case and average-case fine-grained hard assuming the core hypotheses of FGC. We then use these as a basis for fine-grained hardness and average-case hardness of other problems. The new problems represent their inputs in a certain “factored” form. We call them “factored”-OV, “factored”-zero- k-clique and “factored”-3SUM. We show that factored- k-OV and factored kSUM are equivalent and are complete for a class of problems defined over Boolean functions. Factored zero- k-clique is also complete, for a different class of problems. Our hard factored problems are also simple enough that we can reduce them to many other problems, e.g. to edit distance, k-LCS and versions of Max-Flow. We further consider counting variants of the factored problems and give WCtoACFG reductions for them for a natural distribution. Through FGC reductions we then get average-case hardness for well-studied problems like regular expression matching from standard worst-case FGC assumptions. To obtain our WCtoACFG reductions, we formalize the framework of [Boix-Adserà et al. 2019] that was used to give a WCtoACFG reduction for counting k-cliques. We define an explicit property of problems such that if a problem has that property one can use the framework on the problem to get a WCtoACFG self reduction. We then use the framework to slightly extend Bolx-Adserà et al.'s average-case counting k-cliques result to average-case hardness for counting arbitrary subgraph patterns of constant size in -partite graphs. The fine-grained public-key encryption scheme of [LaVigne et al.'20] is based on an average-case hardness hypothesis for the decision problem, zero- k-clique, and the known techniques for building such schemes break down for algebraic/counting problems. Meanwhile, the WCtoACFG reductions so far have only been for counting problems. To bridge this gap, we show that for a natural distribution, an algorithm that detects a zero- k-clique with high enough probability also implies an algorithm that can count zero- k-cliques with high probability. This gives hope that the FGC cryptoscheme of [LaVigne et al.'20] can be based on standard FGC assumptions.

All-Pairs Shortest Paths (APSP) is one of the most basic problems in graph algorithms. Given an n-node directed or undirected graph with integer weights in {-nc, ..., nc} and no negative cycles, APSP asks to compute the shortest paths distance between every pair of vertices. The fastest known algorithm for APSP runs in n3/2Θ(√{logn})time [Williams'14], and no truly subcubic time algorithms are known. One of the main hypotheses in fine-grained complexity is that APSP requires n3-o(1)time. Another famous hypothesis in fine-grained complexity is that the 3SUM problem for n integers (which can be solved in O(n2) time) requires n2-o(1)time. Although there are no direct reductions between 3SUM and APSP, it is known that they are related: the (min, +)-convolution problem reduces in a fine-grained way to both, and both fine-grained reduce to the Exact Triangle problem. In this paper we find more relationships between these two problems and other basic problems. Pătraşcu had shown that under the 3SUM hypothesis the All-Edges Sparse Triangle problem in m-edge graphs requires m4/3-o(1)time. The latter problem asks to determine for every edge e, whether e is in a triangle. It is equivalent to the problem of listing m triangles in an m-edge graph where m=-Õ(n1.5), and can be solved in O(m1.41) time [Alon et al.'97] with the current matrix multiplication bounds, and in Õ(m4/3) time if ω = 2. We show that one can reduce Exact Triangle to All-Edges Sparse Triangle, showing that All-Edges Sparse Triangle (and hence Triangle Listing) requires m4/3-o(1)time also assuming the APSP hypothesis. This allows us to provide APSP-hardness for many dynamic problems that were previously known to be hard under the 3SUM hypothesis. We also consider the All-Edges Monochromatic Triangle problem. Via work of [Lincoln et al.'20], our result on All-Edges Sparse Triangle implies that if the All-Edges Monochromatic Triangle problem has an O(n2.5-ε) time algorithm for , then both the APSP and 3SUM hypotheses are false. The fastest algorithm for All-Edges Monochromatic Triangle runs in Õ(n(3+ω)/2) time [Vassilevska et al.'06], and our new reduction shows that if ω = 2, this algorithm is best possible, unless 3SUM or APSP can be solved faster. Besides 3SUM, previously the only problems known to be fine-grained reducible to All-Edges Monochromatic Triangle were the seemingly easier problems directed unweighted APSP and Min-Witness Product [Lincoln et al.'20]. Our reduction shows that this problem is much harder. We also connect the problem to other “intermediate” problems, whose runtimes are between O(nω) and O(n3), such as the Max-Min product problem.

It is known that a better than $2$-approximation algorithm for the girth in dense directed unweighted graphs needs $n^{3-o(1)}$ time unless one uses fast matrix multiplication. Meanwhile, the best known approximation factor for a combinatorial algorithm running in $O(mn^{1-ε})$ time (by Chechik et al.) is $3$. Is the true answer $2$ or $3$? The main result of this paper is a (conditionally) tight approximation algorithm for directed graphs. First, we show that under a popular hardness assumption, any algorithm, even one that exploits fast matrix multiplication, would need to take at least $mn^{1-o(1)}$ time for some sparsity $m$ if it achieves a $(2-ε)$-approximation for any $ε>0$. Second we give a $2$-approximation algorithm for the girth of unweighted graphs running in $\tilde{O}(mn^{3/4})$ time, and a $(2+ε)$-approximation algorithm (for any $ε>0$) that works in weighted graphs and runs in $\tilde{O}(m\sqrt n)$ time. Our algorithms are combinatorial. We also obtain a $(4+ε)$-approximation of the girth running in $\tilde{O}(mn^{\sqrt{2}-1})$ time, improving upon the previous best $\tilde{O}(m\sqrt n)$ running time by Chechik et al. Finally, we consider the computation of roundtrip spanners. We obtain a $(5+ε)$-approximate roundtrip spanner on $\tilde{O}(n^{1.5}/ε^2)$ edges in $\tilde{O}(m\sqrt n/ε^2)$ time. This improves upon the previous approximation factor $(8+ε)$ of Chechik et al. for the same running time.

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.

A graph G on n nodes is an Orthogonal Vectors (OV) graph of dimension d if there are vectors v_1, …, v_n ∈ {0,1}^d such that nodes i and j are adjacent in G if and only if ⟨v_i,v_j⟩ = 0 over Z. In this paper, we study a number of basic graph algorithm problems, except where one is given as input the vectors defining an OV graph instead of a general graph. We show that for each of the following problems, an algorithm solving it faster on such OV graphs G of dimension only d=O(log n) than in the general case would refute a plausible conjecture about the time required to solve sparse MAX-k-SAT instances: - Determining whether G contains a triangle. - More generally, determining whether G contains a directed k-cycle for any k ≥ 3. - Computing the square of the adjacency matrix of G over ℤ or ?_2. - Maintaining the shortest distance between two fixed nodes of G, or whether G has a perfect matching, when G is a dynamically updating OV graph. We also prove some complementary results about OV graphs. We show that any problem which is NP-hard on constant-degree graphs is also NP-hard on OV graphs of dimension O(log n), and we give two problems which can be solved faster on OV graphs than in general: Maximum Clique, and Online Matrix-Vector Multiplication.

The most studied linear algebraic operation, matrix multiplication, has surprisingly fast $O(n^ω)$ time algorithms for $ω<2.373$. On the other hand, the $(\min,+)$ matrix product which is at the heart of many fundamental graph problems such as APSP, has received only minor improvements over its brute-force cubic running time and is widely conjectured to require $n^{3-o(1)}$ time. There is a plethora of matrix products and graph problems whose complexity seems to lie in the middle of these two problems. For instance, the Min-Max matrix product, the Minimum Witness matrix product, APSP in directed unweighted graphs and determining whether an edge-colored graph contains a monochromatic triangle, can all be solved in $\tilde O(n^{(3+ω)/2})$ time. A similar phenomenon occurs for convolution problems, where analogous intermediate problems can be solved in $\tilde O(n^{1.5})$ time. Can one improve upon the running times for these intermediate problems, in either the matrix product or the convolution world? Or, alternatively, can one relate these problems to each other and to other key problems in a meaningful way? This paper makes progress on these questions by providing a network of fine-grained reductions. We show for instance that APSP in directed unweighted graphs and Minimum Witness product can be reduced to both the Min-Max product and a variant of the monochromatic triangle problem. We also show that a natural convolution variant of monochromatic triangle is fine-grained equivalent to the famous 3SUM problem. As this variant is solvable in $O(n^{1.5})$ time and 3SUM is in $O(n^2)$ time (and is conjectured to require $n^{2-o(1)}$ time), our result gives the first fine-grained equivalence between natural problems of different running times.

Diameter, radius and eccentricities are fundamental graph parameters, which are extensively studied in various computational settings. Typically, computing approximate answers can be much more efficient compared with computing exact solutions. In this paper, we give a near complete characterization of the trade-offs between approximation ratios and round complexity of distributed algorithms for approximating these parameters, with a focus on the weighted and directed variants. Furthermore, we study bi-chromatic variants of these parameters defined on a graph whose vertices are colored either red or blue, and one focuses only on distances for pairs of vertices that are colored differently. Motivated by applications in computational geometry, bi-chromatic diameter, radius and eccentricities have been recently studied in the sequential setting [Backurs et al. STOC'18, Dalirrooyfard et al. ICALP'19]. We provide the first distributed upper and lower bounds for such problems. Our technical contributions include introducing the notion of approximate pseudo-center, which extends the pseudo-centers of [Choudhary and Gold SODA'20], and presenting an efficient distributed algorithm for computing approximate pseudo-centers. On the lower bound side, our constructions introduce the usage of new functions into the framework of reductions from 2-party communication complexity to distributed algorithms.

We define a natural class of range query problems, and prove that all problems within this class have the same time complexity (up to polylogarithmic factors). The equivalence is very general, and even applies to online algorithms. This allows us to obtain new improved algorithms for all of the problems in the class. We then focus on the special case of the problems when the queries are offline and the number of queries is linear. We show that our range query problems are runtime-equivalent (up to polylogarithmic factors) to counting for each edge e in an m-edge graph the number of triangles through e. This natural triangle problem can be solved using the best known triangle counting algorithm, running in (m2ω/(ω + 1) < (m1.41) time. Moreover, if ω = 2, the (m2ω/(ω + 1)) running time is known to be tight (within mo(1) factors) under the 3SUM Hypothesis. In this case, our equivalence settles the complexity of the range query problems. Our problems constitute the first equivalence class with this peculiar running time bound. To better understand the complexity of these problems, we also provide a deeper insight into the family of triangle problems, in particular showing black-box reductions between triangle listing and per-edge triangle detection and counting. As a byproduct of our reductions, we obtain a simple triangle listing algorithm matching the state-of-the-art for all regimes of the number of triangles. We also give some not necessarily tight, but still surprising reductions from variants of matrix products, such as the (min, max)-product.

The All-Pairs Shortest Paths (APSP) problem is one of the most basic problems in computer science. The fastest known algorithms for APSP in n-node graphs run in n3−o(1) time, and it is a big open problem whether a truly subcubic, O(n3−ε) for ε > 0 time algorithm exists for APSP. The Min-Plus product of two n × n matrices is known to be equivalent to APSP, where the optimal running times of the two problems differ by at most a constant factor. A natural way to approach understanding the complexity of APSP is thus understanding what structure (if any) is needed to solve Min-Plus product in truly subcubic time. The goal of this paper is to get truly subcubic algorithms for Min-Plus product for less structured inputs than what was previously known, and to apply them to versions of APSP and other problems. The results are as follows: (1) Our main result is the first truly subcubic algorithm for the Min-Plus product of two n × n matrices A and B with polylog n bit integer entries, where B has a partitioning into nε × nε blocks (for any ε > 0) where each block is at most nδ-far (for δ < 3 – ω, where 2 ≤ ω < 2.373) in ℓ∞ norm from a constant rank integer matrix. This result presents the most general case to date of Min-Plus product that is still solvable in truly subcubic time. (2) The first application of our main result is a truly subcubic algorithm for APSP in a new type of geometric graph. Chan '10 solved APSP in truly subcubic time in geometric graphs whose edges have weights that are a function of the identities of the edge's end-points. Our result extends Chan's result in the case of integer edge weights by allowing the weights to differ from a function of the end-point identities by at most nδ for small δ. (3) In a second application we consider a batch version of the range mode problem in which one is given a sequence of numbers a1, … , an and n intervals defining contiguous subsequences, and one is asked to compute the range mode of each subsequence. Chan et al.’14 showed that any O(n1.5−ε) time combinatorial algorithm for ε > 0 for this problem can be used to solve Boolean matrix multiplication combinatorially in truly subcubic time. We give the first O(n1–5−ε) time for ε > 0 algorithm for this batch range mode problem, showing that the hardness is indeed constrained to combinatorial algorithms. (4) Our final application is to the Maximum Subarray problem: given an n × n integer matrix, find the contiguous subarray of maximum entry sum. We show that Maximum Subarray can be solved in truly subcubic, O(n3−ε) (for ε > 0) time, as long as every entry of the input matrix is no larger than O(n0.62) in absolute value. This is the first truly subcubic algorithm for an interesting case of Maximum Subarray. The Maximum Subarray problem with arbitrary integer entries is known to be subcubically equivalent to APSP, in that a truly subcubic, O(n3−ε) time algorithm for ε > 0 for one problem would imply a truly subcubic algorithm for the other. Because of this it is believed that Maximum Subarray does not admit truly subcubic algorithms, without a restriction on the inputs. We also improve all the known conditional hardness results for the d-dimensional variant of Maximum Subarray, showing that many of the known algorithms are likely tight.

In the dynamic Single-Source Shortest Paths (SSSP) problem, we are given a graph G=(V,E) subject to edge insertions and deletions and a source vertex s∈ V, and the goal is to maintain the distance d(s,t) for all t∈ V.

Shortest paths computation is one of the most fundamental problems in computer science. An important variant of the problem is when edges can fail, and one needs to compute shortest paths that avoid a (failing) edge. More formally, given a source node s , a target node t , and an edge e , a replacement path for the triple ( s , t , e ) is a shortest s - t path avoiding edge e . Replacement paths computation can be seen either as a static problem or as a data structure problem. In the static setting, a typical goal is to compute for fixed s and t , for every possible failed edge e , the length of the best replacement path around e ( replacement paths problem ). In the data structure setting, a typical goal is to design a data structure ( distance sensitivity oracle ) that, after some preprocessing, quickly answers queries of the form: What is the length of the replacement path for the triple ( s , t , e )? In this article, we focus on n -node directed graphs with integer edge weights in [− M , M ], and present improved replacement paths algorithms and distance sensitivity oracles based on fast matrix multiplication. In more detail, we obtain the following main results: • We describe a replacement paths algorithm with runtime Õ( Mn ω ), where ω < 2.373 is the fast matrix multiplication exponent. For a comparison, the previous fastest algorithms have runtime õ( Mn 1+2ω /3 ) [Weimann,Yuster—FOCS’10] and, in the unweighted case, õ( n 2.5 ) [Roditty, Zwick—ICALP’05]. Our result shows that, at least for small integer weights, the replacement paths problem in directed graphs may be easier than the related all-pairs shortest paths problem, as the current best runtime for the latter is õ( M 1\4−ω n 2+1 \ 4−ω ): this is Ω ( n 2.5 ) even if ω = 2. Our algorithm also implies that the k shortest simple s - t paths can be computed in õ( kMn ω ) time. • We consider the single-source generalization of the replacement paths problem, where only the source s is fixed. We show how to solve this problem in all-pairs shortest paths time, currently õ( M 1\4−ω n 2+1\4−ω ). Our runtime reduces to õ( Mn ω ) for positive weights, hence matching our mentioned result for the simpler replacement paths case (that, however, holds also for nonpositive weights). One of the ingredients that we use is an algorithm to compute the distances from a set s of source nodes to a set T of target nodes in õ( Mn ω +| S |ṡ | T |ṡ ( Mn ) 1\4−ω ) time. This improves on a result in Yuster,Zwick—FOCS’05. • We present the first distance sensitivity oracle that achieves simultaneously subcubic preprocessing time and sublinear query time. More precisely, for a given parameter α ∈ [0,1], our oracle has preprocessing time Õ( Mn ω + 1\ 2 + Mn ω + α (4−ω) ) and query time Õ( n 1−&alpha ). The previous best oracle for small integer weights has Õ( Mn ω +1−α ) preprocessing time and (superlinear) Õ( n 1+α ) query time [Weimann,Yuster-FOCS’10]. From a technical point of view, an interesting and novel aspect of our oracle is that it exploits as a subroutine our single-source replacement paths algorithm. We also present an oracle with the same preprocessing time as in Weimann,Yuster—FOCS’10 and with smaller query time õ( n 1−1−α\4−ω + n 2α ).

Fixed-parameter algorithms and kernelization are two powerful methods to solve NP-hard problems. Yet so far those algorithms have been largely restricted to static inputs. In this article, we provide fixed-parameter algorithms and kernelizations for fundamental NP-hard problems with dynamic inputs. We consider a variety of parameterized graph and hitting set problems that are known to have f ( k ) n 1+o(1) time algorithms on inputs of size n , and we consider the question of whether there is a data structure that supports small updates (such as edge/vertex/set/element insertions and deletions) with an update time of g ( k ) n o(1) ; such an update time would be essentially optimal. Update and query times independent of n are particularly desirable. Among many other results, we show that F EEDBACK V ERTEX S ET and k -P ATH admit dynamic algorithms with f ( k )log O(1) update and query times for some function f depending on the solution size k only. We complement our positive results by several conditional and unconditional lower bounds. For example, we show that unlike their undirected counterparts, D IRECTED F EEDBACK V ERTEX S ET and D IRECTED k -P ATH do not admit dynamic algorithms with n o(1) update and query times even for constant solution sizes k ≤ 3 , assuming popular hardness hypotheses. We also show that unconditionally, in the cell probe model, D IRECTED F EEDBACK V ERTEX S ET cannot be solved with update time that is purely a function of k .

The diameter, radius and eccentricities are natural graph parameters. While these problems have been studied extensively, there are no known dynamic algorithms for them beyond the ones that follow from trivial recomputation after each update or from solving dynamic All-Pairs Shortest Paths (APSP), which is very computationally intensive. This is the situation for dynamic approximation algorithms as well, and even if only edge insertions or edge deletions need to be supported. This paper provides a comprehensive study of the dynamic approximation of Diameter, Radius and Eccentricities, providing both conditional lower bounds, and new algorithms whose bounds are optimal under popular hypotheses in fine-grained complexity. Some of the highlights include: - Under popular hardness hypotheses, there can be no significantly better fully dynamic approximation algorithms than recomputing the answer after each update, or maintaining full APSP. - Nearly optimal partially dynamic (incremental/decremental) algorithms can be achieved via efficient reductions to (incremental/decremental) maintenance of Single-Source Shortest Paths. For instance, a nearly $(3/2+ε)$-approximation to Diameter in directed or undirected graphs can be maintained decrementally in total time $m^{1+o(1)}\sqrt{n}/ε^2$. This nearly matches the static $3/2$-approximation algorithm for the problem that is known to be conditionally optimal.

We study fundamental graph parameters such as the Diameter and Radius in directed graphs, when distances are measured using a somewhat unorthodox but natural measure: the distance between $u$ and $v$ is the minimum of the shortest path distances from $u$ to $v$ and from $v$ to $u$. The center node in a graph under this measure can for instance represent the optimal location for a hospital to ensure the fastest medical care for everyone, as one can either go to the hospital, or a doctor can be sent to help. By computing All-Pairs Shortest Paths, all pairwise distances and thus the parameters we study can be computed exactly in $\tilde{O}(mn)$ time for directed graphs on $n$ vertices, $m$ edges and nonnegative edge weights. Furthermore, this time bound is tight under the Strong Exponential Time Hypothesis [Roditty-Vassilevska W. STOC 2013] so it is natural to study how well these parameters can be approximated in $O(mn^{1-ε})$ time for constant $ε>0$. Abboud, Vassilevska Williams, and Wang [SODA 2016] gave a polynomial factor approximation for Diameter and Radius, as well as a constant factor approximation for both problems in the special case where the graph is a DAG. We greatly improve upon these bounds by providing the first constant factor approximations for Diameter, Radius and the related Eccentricities problem in general graphs. Additionally, we provide a hierarchy of algorithms for Diameter that gives a time/accuracy trade-off.

Some of the most fundamental and well-studied graph parameters are the Diameter (the largest shortest paths distance) and Radius (the smallest distance for which a "center" node can reach all other nodes). The natural and important $ST$-variant considers two subsets $S$ and $T$ of the vertex set and lets the $ST$-diameter be the maximum distance between a node in $S$ and a node in $T$, and the $ST$-radius be the minimum distance for a node of $S$ to reach all nodes of $T$. The bichromatic variant is the special case in which $S$ and $T$ partition the vertex set. In this paper we present a comprehensive study of the approximability of $ST$ and Bichromatic Diameter, Radius, and Eccentricities, and variants, in graphs with and without directions and weights. We give the first nontrivial approximation algorithms for most of these problems, including time/accuracy trade-off upper and lower bounds. We show that nearly all of our obtained bounds are tight under the Strong Exponential Time Hypothesis (SETH), or the related Hitting Set Hypothesis. For instance, for Bichromatic Diameter in undirected weighted graphs with $m$ edges, we present an $\tilde{O}(m^{3/2})$ time $5/3$-approximation algorithm, and show that under SETH, neither the running time, nor the approximation factor can be significantly improved while keeping the other unchanged.

In this talk we will consider the known techniques for designing Matrix Multiplication algorithms. The two main approaches are the Laser method of Strassen, and the Group theoretic approach of Cohn and Umans. We define generalizations that subsume these two approaches: the Galactic and the Universal method; the latter is the most general method there is. We then design a suite of techniques for proving lower bounds on the value of ω, the exponent of matrix multiplication, which can be achieved by algorithms using many tensors T and the Galactic method. Some of our techniques exploit 'local' properties of~T, like finding a sub-tensor of T which is so 'weak' that T itself couldn't be used to achieve a good bound on ω, while others exploit 'global' properties, like T being a monomial degeneration of the structural tensor of a group algebra.

No abstract available.

We study the known techniques for designing Matrix Multiplication algorithms. The two main approaches are the Laser method of Strassen, and the Group theoretic approach of Cohn and Umans. We define a generalization based on zeroing outs which subsumes these two approaches, which we call the Solar method, and an even more general method based on monomial degenerations, which we call the Galactic method. We then design a suite of techniques for proving lower bounds on the value of omega, the exponent of matrix multiplication, which can be achieved by algorithms using many tensors T and the Galactic method. Some of our techniques exploit 'local' properties of T, like finding a sub-tensor of T which is so 'weak' that T itself couldn't be used to achieve a good bound on omega, while others exploit 'global' properties, like T being a monomial degeneration of the structural tensor of a group algebra. Our main result is that there is a universal constant ℓ>2 such that a large class of tensors generalizing the Coppersmith-Winograd tensor CW_q cannot be used within the Galactic method to show a bound on omega better than ell, for any q. We give evidence that previous lower-bounding techniques were not strong enough to show this. We also prove a number of complementary results along the way, including that for any group G, the structural tensor of C[G] can be used to recover the best bound on omega which the Coppersmith-Winograd approach gets using CW_|G|-2 as long as the asymptotic rank of the structural tensor is not too large.

A central goal of algorithmic research is to determine how fast computational problems can be solved in the worst case. Theorems from complexity theory state that there are problems that, on inputs of size n, can be solved in t(n) time but not in O(t(n)^{1-epsilon}) time for epsilon>0. The main challenge is to determine where in this hierarchy various natural and important problems lie. Throughout the years, many ingenious algorithmic techniques have been developed and applied to obtain blazingly fast algorithms for many problems. Nevertheless, for many other central problems, the best known running times are essentially those of their classical algorithms from the 1950s and 1960s. Unconditional lower bounds seem very difficult to obtain, and so practically all known time lower bounds are conditional. For years, the main tool for proving hardness of computational problems have been NP-hardness reductions, basing hardness on P neq NP. However, when we care about the exact running time (as opposed to merely polynomial vs non-polynomial), NP-hardness is not applicable, especially if the problem is already solvable in polynomial time. In recent years, a new theory has been developed, based on "fine-grained reductions" that focus on exact running times. Mimicking NP-hardness, the approach is to (1) select a key problem X that is conjectured to require essentially t(n) time for some t, and (2) reduce X in a fine-grained way to many important problems. This approach has led to the discovery of many meaningful relationships between problems, and even sometimes to equivalence classes. The main key problems used to base hardness on have been: the 3SUM problem, the CNF-SAT problem (based on the Strong Exponential Time Hypothesis (SETH)) and the All Pairs Shortest Paths Problem. Research on SETH-based lower bounds has flourished in particular in recent years showing that the classical algorithms are optimal for problems such as Approximate Diameter, Edit Distance, Frechet Distance, Longest Common Subsequence etc. In this talk I will give an overview of the current progress in this area of study, and will highlight some exciting new developments and their relationship to database theory.

We consider the techniques behind the current best algorithms for matrix multiplication. Our results are threefold. (1) We provide a unifying framework, showing that all known matrix multiplication running times since 1986 can be achieved from a single very natural tensor - the structural tensor $T_q$ of addition modulo an integer $q$. (2) We show that if one applies a generalization of the known techniques (arbitrary zeroing out of tensor powers to obtain independent matrix products in order to use the asymptotic sum inequality of Schönhage) to an arbitrary monomial degeneration of $T_q$, then there is an explicit lower bound, depending on $q$, on the bound on the matrix multiplication exponent $ω$ that one can achieve. We also show upper bounds on the value $α$ that one can achieve, where $α$ is such that $n\times n^α\times n$ matrix multiplication can be computed in $n^{2+o(1)}$ time. (3) We show that our lower bound on $ω$ approaches $2$ as $q$ goes to infinity. This suggests a promising approach to improving the bound on $ω$: for variable $q$, find a monomial degeneration of $T_q$ which, using the known techniques, produces an upper bound on $ω$ as a function of $q$. Then, take $q$ to infinity. It is not ruled out, and hence possible, that one can obtain $ω=2$ in this way.

This paper initiates the study of I/O algorithms (minimizing cache misses) from the perspective of fine-grained complexity (conditional polynomial lower bounds). Specifically, we aim to answer why sparse graph problems are so hard, and why the Longest Common Subsequence problem gets a savings of a factor of the size of cache times the length of a cache line, but no more. We take the reductions and techniques from complexity and fine-grained complexity and apply them to the I/O model to generate new (conditional) lower bounds as well as new faster algorithms. We also prove the existence of a time hierarchy for the I/O model, which motivates the fine-grained reductions. - Using fine-grained reductions, we give an algorithm for distinguishing 2 vs. 3 diameter and radius that runs in O(|E|^2/(MB)) cache misses, which for sparse graphs improves over the previous O(|V|^2/B) running time. - We give new reductions from radius and diameter to Wiener index and median. These reductions are new in both the RAM and I/O models. - We show meaningful reductions between problems that have linear-time solutions in the RAM model. The reductions use low I/O complexity (typically O(n/B)), and thus help to finely capture between "I/O linear time" O(n/B) and RAM linear time O(n). - We generate new I/O assumptions based on the difficulty of improving sparse graph problem running times in the I/O model. We create conjectures that the current best known algorithms for Single Source Shortest Paths (SSSP), diameter, and radius are optimal. - From these I/O-model assumptions, we show that many of the known reductions in the word-RAM model can naturally extend to hold in the I/O model as well (e.g., a lower bound on the I/O complexity of Longest Common Subsequence that matches the best known running time). - We prove an analog of the Time Hierarchy Theorem in the I/O model, further motivating the study of fine-grained algorithmic differences.

A k-spanner of a graph G is a sparse subgraph H whose shortest path distances match those of G up to a multiplicative error k. In this paper we study spanners that are resistant to faults. A subgraph H ⊆ G is an f vertex fault tolerant (VFT) k-spanner if H \ F is a k-spanner of G \ F for any small set F of f vertices that might “fail.” One of the main questions in the area is: what is the minimum size of an f fault tolerant k-spanner that holds for all n node graphs (as a function of f, k and n)? This question was first studied in the context of geometric graphs [Levcopoulos et al. STOC ’98, Czumaj and Zhao SoCG ’03] and has more recently been considered in general undirected graphs [Chechik et al. STOC ’09, Dinitz and Krauthgamer PODC ’11]. In this paper, we settle the question of the optimal size of a VFT spanner, in the setting where the stretch factor k is fixed. Specifically, we prove that every (undirected, possibly weighted) n-node graph G has a (2k – 1)-spanner resilient to f vertex faults with Ok (f1–1/kn1+1/k) edges, and this is fully optimal (unless the famous Erdös Girth Conjecture is false). Our lower bound even generalizes to imply that no data structure capable of approximating distG\F (s, t) similarly can beat the space usage of our spanner in the worst case. To the best of our knowledge, this is the first instance in fault tolerant network design in which introducing fault tolerance to the structure increases the size of the (non-FT) structure by a sublinear factor in f. Another advantage of this result is that our spanners are constructed by a very natural and simple greedy algorithm, which is the obvious extension of the standard greedy algorithm used to build spanners in the non-faulty setting. We also consider the edge fault tolerant (EFT) model, defined analogously with edge failures rather than vertex failures. We show that the same spanner upper bound applies in this setting. Our data structure lower bound extends to the case k = 2 (and hence we close the EFT problem for 3-approximations), but it falls to D(f 1/2-1/(2k) · n1+1/k) for k > 3. We leave it as an open problem to close this gap.

Fine-grained reductions have established equivalences between many core problems with Õ(n3)-time algorithms on n-node weighted graphs, such as Shortest Cycle, All-Pairs Shortest Paths (APSP), Radius, Replacement Paths, Second Shortest Paths, and so on. These problems also have Õ(mn)-time algorithms on m-edge n-node weighted graphs, and such algorithms have wider applicability. Are these mn bounds optimal when m << n2? Starting from the hypothesis that the minimum weight (2ℓ + 1)-Clique problem in edge weighted graphs requires n2ℓ+1–o(1) time, we prove that for all sparsities of the form m = Θ(n1+1/ℓ), there is no O(n2 + mn1–ε) time algorithm for ε > 0 for any of the below problems • Minimum Weight (2ℓ + 1)-Cycle in a directed weighted graph, • Shortest Cycle in a directed weighted graph, • APSP in a directed or undirected weighted graph, • Radius (or Eccentricities) in a directed or undirected weighted graph, • Wiener index of a directed or undirected weighted graph, • Replacement Paths in a directed weighted graph, • Second Shortest Path in a directed weighted graph, • Betweenness Centrality of a given node in a directed weighted graph. That is, we prove hardness for a variety of sparse graph problems from the hardness of a dense graph problem. Our results also lead to new conditional lower bounds from several related hypothesis for unweighted sparse graph problems including k-cycle, shortest cycle, Radius, Wiener index and APSP.

The girth of a graph, i.e. the length of its shortest cycle, is a fundamental graph parameter. Unfortunately all known algorithms for computing, even approximately, the girth and girth-related structures in directed weighted m-edge and n-node graphs require Ω(min{nω, mn}) time (for 2 ≤ ω < 2.373). In this paper, we drastically improve these runtimes as follows: • Multiplicative Approximations in Nearly Linear Time: We give an algorithm that in Õ(m) time computes an Õ(1)-multiplicative approximation of the girth as well as an Õ(1)-multiplicative roundtrip spanner with Õ(n) edges with high probability (w.h.p). • Nearly Tight Additive Approximations: For unweighted graphs and any a ∊ (0, 1) we give an algorithm that in Õ(mn1–a) time computes an O(na)-additive approximation of the girth, w.h.p. We show that the runtime of our algorithm cannot be significantly improved without a breakthrough in combinatorial boolean matrix multiplication. We also show that if the girth is O(na), then the same guarantee can be achieved via a deterministic algorithm. Our main technical contribution to achieve these results is the first nearly linear time algorithm for computing roundtrip covers, a directed graph decomposition concept key to previous roundtrip spanner constructions. Previously it was not known how to compute these significantly faster than Ω(mn) time. Given the traditional difficulty in efficiently processing directed graphs, we hope our techniques may find further applications.

Among the most important graph parameters is the Diameter, the largest distance between any two vertices. There are no known very efficient algorithms for computing the Diameter exactly. Thus, much research has been devoted to how fast this parameter can be approximated. Chechik et al. [SODA 2014] showed that the diameter can be approximated within a multiplicative factor of 3/2 in Õ(m3/2) time. Furthermore, Roditty and Vassilevska W. [STOC 13] showed that unless the Strong Exponential Time Hypothesis (SETH) fails, no O(n2−ε) time algorithm can achieve an approximation factor better than 3/2 in sparse graphs. Thus the above algorithm is essentially optimal for sparse graphs for approximation factors less than 3/2. It was, however, completely plausible that a 3/2-approximation is possible in linear time. In this work we conditionally rule out such a possibility by showing that unless SETH fails no O(m3/2−ε) time algorithm can achieve an approximation factor better than 5/3.

We say an algorithm on n × n matrices with integer entries in [− M , M ] (or n -node graphs with edge weights from [− M , M ]) is truly subcubic if it runs in O ( n 3 − δ ċ poly(log M )) time for some δ > 0. We define a notion of subcubic reducibility and show that many important problems on graphs and matrices solvable in O ( n 3 ) time are equivalent under subcubic reductions. Namely, the following weighted problems either all have truly subcubic algorithms, or none of them do: •The all-pairs shortest paths problem on weighted digraphs (APSP). •Detecting if a weighted graph has a triangle of negative total edge weight. •Listing up to n 2.99 negative triangles in an edge-weighted graph. •Finding a minimum weight cycle in a graph of non-negative edge weights. •The replacement paths problem on weighted digraphs. •Finding the second shortest simple path between two nodes in a weighted digraph. •Checking whether a given matrix defines a metric. •Verifying the correctness of a matrix product over the (min, +)-semiring. •Finding a maximum subarray in a given matrix. Therefore, if APSP cannot be solved in n 3−ε time for any ε > 0, then many other problems also need essentially cubic time. In fact, we show generic equivalences between matrix products over a large class of algebraic structures used in optimization, verifying a matrix product over the same structure, and corresponding triangle detection problems over the structure. These equivalences simplify prior work on subcubic algorithms for all-pairs path problems, since it now suffices to give appropriate subcubic triangle detection algorithms. Other consequences of our work are new combinatorial approaches to Boolean matrix multiplication over the (OR,AND)-semiring (abbreviated as BMM). We show that practical advances in triangle detection would imply practical BMM algorithms, among other results. Building on our techniques, we give two improved BMM algorithms: a derandomization of the combinatorial BMM algorithm of Bansal and Williams (FOCS’09), and an improved quantum algorithm for BMM.

Due to the lack of unconditional polynomial lower bounds, it is now in fashion to prove conditional lower bounds in order to advance our understanding of the class P. The vast majority of these lower bounds are based on one of three famous hypotheses: the 3-SUM conjecture, the all pairs shortest paths (APSP) conjecture, and the Strong Exponential Time Hypothesis. Only circumstantial evidence is known in support of these hypotheses, and no formal relationship between them is known. In hopes of obtaining “less conditional" and therefore more reliable lower bounds, we consider the conjecture that at least one of the above three hypotheses is true. We design novel reductions from 3-SUM, APSP, and CNF-SAT, and derive interesting consequences of this very plausible conjecture, including tight $n^{3-o(1)}$ lower bounds for purely combinatorial problems about the triangles in unweighted graphs; new $n^{1-o(1)}$ lower bounds for the amortized update and query times of dynamic algorithms for Single-Source Reachability, Strongly Connected Components, and Max-Flow; new $n^{1.5-o(1)}$ lower bound for computing a set of $n$ $st$-maximum-flow values in a directed graph with $n$ nodes and $\tilde{O}(n)$ edges; and a hierarchy of natural graph problems on $n$ nodes with complexity $n^{c}$ for $c \in (2,3)$. Only slightly nontrivial consequences of this conjecture were known prior to our work. Along the way we also obtain new conditional lower bounds for the Single-Source Max-Flow problem.

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 reduction from the Orthogonal Vectors problem to Subtree Isomorphism, showing that a truly subquadratic algorithm for the latter refutes the Strong Exponential Time Hypothesis (SETH). In light of this conditional lower bound, we focus on natural special cases for which no truly subquadratic algorithms are known. We classify these cases against the quadratic barrier, showing in particular that: • Even for binary, rooted trees, a truly subquadratic algorithm refutes SETH. • Even for rooted trees of depth O (log log n ), where n is the total number of vertices, a truly subquadratic algorithm refutes SETH. • For every constant d , there is a constant ε d > 0 and a randomized, truly subquadratic algorithm for degree- d rooted trees of depth at most (1+ ε d ) log d n . In particular, there is an O (min { 2.85 h , n 2 }) algorithm for binary trees of depth h . Our reductions utilize new “tree gadgets” that are likely useful for future SETH-based lower bounds for problems on trees. Our upper bounds apply a folklore result from randomized decision tree complexity.

We study the Stable Invitations Problem (SIP) in which an event organizer is to invite a subset of agents (from a group of agents) to an event, subject to certain rationality criteria. In SIP, the agents have friends, enemies, and preferences on the number of attendees at the event; an agent is willing to attend the event if all friends of the agent attend, no enemy of the agent attends, and the number of attendees is acceptable to the agent. We consider two solution concepts: (1) individual rationality (everyone who is invited is willing to attend) and (2) (Nash) stability (no agent wants to deviate from the given invitation).It is known that finding an invitation of given size for either concept is NP-complete. In this work, we study the complexity of SIP on a finer scale, through the lense of parameterized complexity.For the two solution concepts and the special cases where the number of friends and/or enemies is bounded above by a constant, we show that the problems belong to different complexity classes when parameterized by the size of solutions.For instance finding an individually rational invitation of size k is W[1]-complete, yet finding a stable invitation is W[2]-complete.Moreover, when all friend and enemy relations are symmetric, finding a solution of either of the concepts becomes fixed-parameter tractable unless agents have unbounded number(s) of enemies.

Fixed-parameter algorithms and kernelization are two powerful methods to solve NP-hard problems. Yet, so far those algorithms have been largely restricted to static inputs. In this paper we provide fixed-parameter algorithms and kernelizations for fundamental NP-hard problems with dynamic inputs. We consider a variety of parameterized graph and hitting set problems which are known to have f(k)n^{1+o(1)} time algorithms on inputs of size n, and we consider the question of whether there is a data structure that supports small updates (such as edge/vertex/set/element insertions and deletions) with an update time of g(k)n^{o(1)}; such an update time would be essentially optimal. Update and query times independent of n are particularly desirable. Among many other results, we show that Feedback Vertex Set and k-Path admit dynamic algorithms with f(k)log O(1) n update and query times for some function f depending on the solution size k only. We complement our positive results by several conditional and unconditional lower bounds. For example, we show that unlike their undirected counterparts, Directed Feedback Vertex Set and Directed k-Path do not admit dynamic algorithms with n^{o(1) } update and query times even for constant solution sizes k <= 3, assuming popular hardness hypotheses. We also show that unconditionally, in the cell probe model, Directed Feedback Vertex Set cannot be solved with update time that is purely a function of k.

Preservers and additive spanners are sparse (hence cheap to store) subgraphs that preserve the distances between given pairs of nodes exactly or with some small additive error, respectively. Since real-world networks are prone to failures, it makes sense to study fault-tolerant versions of the above structures. This turns out to be a surprisingly difficult task. For every small but arbitrary set of edge or vertex failures, the preservers and spanners need to contain replacement paths around the faulted set. Unfortunately, the complexity of the interaction between replacement paths blows up significantly, even from 1 to 2 faults, and the structure of optimal preservers and spanners is poorly understood. In particular, no nontrivial bounds for preservers and additive spanners are known when the number of faults is bigger than 2. Even the answer to the following innocent question is completely unknown: what is the worst-case size of a preserver for a single pair of nodes in the presence of f edge faults? There are no super-linear lower bounds, nor subquadratic upper bounds for f>2. In this paper we make substantial progress on this and other fundamental questions: - We present the first truly sub-quadratic size fault-tolerant single-pair preserver in unweighted (possibly directed) graphs: for any n node graph and any fixed number f of faults, O~(fn^{2-1/2^f}) size suffices. Our result also generalizes to the single-source (all targets) case, and can be used to build new fault-tolerant additive spanners (for all pairs). - The size of the above single-pair preserver grows to O(n^2) for increasing f. We show that this is necessary even in undirected unweighted graphs, and even if you allow for a small additive error: If you aim at size O(n^{2-eps}) for \eps>0, then the additive error has to be \Omega(eps f). This surprisingly matches known upper bounds in the literature. - For weighted graphs, we provide matching upper and lower bounds for the single pair case. Namely, the size of the preserver is Theta(n^2) for f > 1 in both directed and undirected graphs, while for f=1 the size is Theta(n) in undirected graphs. For directed graphs, we have a superlinear upper bound and a matching lower bound. Most of our lower bounds extend to the distance oracle setting, where rather than a subgraph we ask for any compact data structure.

In recent years it has become popular to study dynamic problems in a sensitivity setting: Instead of allowing for an arbitrary sequence of updates, the sensitivity model only allows to apply batch updates of small size to the original input data. The sensitivity model is particularly appealing since recent strong conditional lower bounds ruled out fast algorithms for many dynamic problems, such as shortest paths, reachability, or subgraph connectivity. In this paper we prove conditional lower bounds for these and additional problems in a sensitivity setting. For example, we show that under the Boolean Matrix Multiplication (BMM) conjecture combinatorial algorithms cannot compute the (4/3-\varepsilon)-approximate diameter of an undirected unweighted dense graph with truly subcubic preprocessing time and truly subquadratic update/query time. This result is surprising since in the static setting it is not clear whether a reduction from BMM to diameter is possible. We further show under the BMM conjecture that many problems, such as reachability or approximate shortest paths, cannot be solved faster than by recomputation from scratch even after only one or two edge insertions. We extend our reduction from BMM to Diameter to give a reduction from All Pairs Shortest Paths to Diameter under one deletion in weighted graphs. This is intriguing, as in the static setting it is a big open problem whether Diameter is as hard as APSP. We further get a nearly tight lower bound for shortest paths after two edge deletions based on the APSP conjecture. We give more lower bounds under the Strong Exponential Time Hypothesis. Many of our lower bounds also hold for static oracle data structures where no sensitivity is required. Finally, we give the first algorithm for the (1+\varepsilon)-approximate radius, diameter, and eccentricity problems in directed or undirected unweighted graphs in case of single edges failures. The algorithm has a truly subcubic running time for graphs with a truly subquadratic number of edges; it is tight w.r.t. the conditional lower bounds we obtain.

We consider the maximum weight matching (MWM) problem in dynamic graphs. We provide two reductions. The first reduces the dynamic MWM problem on m-edge, n-node graphs with weights bounded by N to the problem with weights bounded by (n/eps)^2, so that if the MWM problem can be alpha-approximated with update time t(m,n,N), then it can also be (1+eps)alpha-approximated with update time O(t(m,n,(n/eps)^2)log^2 n+log n loglog N)). The second reduction reduces the dynamic MWM problem to the dynamic maximum cardinality matching (MCM) problem in which the graph is unweighted. This reduction shows that if there is an \alpha-approximation algorithm for MCM with update time t(m,n) in m-edge n-node graphs, then there is also a (2+eps)alpha-approximation algorithm for MWM with update time O(t(m,n)eps^{-2}log^2 N). We also obtain better bounds in our reductions if the ratio between the largest and the smallest edge weight is small. Combined with recent work on MCM, these two reductions substantially improve upon the state-of-the-art of dynamic MWM algorithms.

A single-elimination (SE) tournament is a popular way to select a winner both in sports competitions and in elections. A natural and well-studied question is the tournament fixing problem (TFP): given the set of all pairwise match outcomes, can a tournament organizer rig an SE tournament by adjusting the initial seeding so that the organizer's favorite player wins? We prove new sufficient conditions on the pairwise match outcome information and the favorite player, under which there is guaranteed to be a seeding where the player wins the tournament. Our results greatly generalize previous results. We also investigate the relationship between the set of players that can win an SE tournament under some seeding (so-called SE winners) and other traditional tournament solutions. In addition, we generalize and strengthen prior work on probabilistic models for generating tournaments. For instance, we show that every player in an $n$ player tournament generated by the Condorcet random model will be an SE winner even when the noise is as small as possible, $p=\Theta(\ln n/n)$; prior work only had such results for $p\geq \Omega(\sqrt{\ln n/n})$. We also establish new results for significantly more general generative models.

A single-elimination (SE) tournament is a popular way to select a winner in both sports competitions and in elections. A natural and well-studied question is the tournament fixing problem (TFP): given the set of all pairwise match outcomes, can a tournament organizer rig an SE tournament by adjusting the initial seeding so that their favorite player wins? We prove new sufficient conditions on the pairwise match outcome information and the favorite player, under which there is guaranteed to be a seeding where the player wins the tournament. Our results greatly generalize previous results. We also investigate the relationship between the set of players that can win an SE tournament under some seeding (so called SE winners) and other traditional tournament solutions. In addition, we generalize and strengthen prior work on probabilistic models for generating tournaments. For instance, we show that every player in an n player tournament generated by the Condorcet Random Model will be an SE winner even when the noise is as small as possible, p = Θ(ln n/n); prior work only had such results for p ≥ Ω( ln n/n). We also establish new results for significantly more general generative models.

We consider the minimum-weight feedback vertex set problem in tournaments: given a tournament with non-negative vertex weights, remove a minimum-weight set of vertices that intersects all cycles. This problem is $\mathsf{NP}$-hard to solve exactly, and Unique Games-hard to approximate by a factor better than 2. We present the first $7/3$ approximation algorithm for this problem, improving on the previously best known ratio $5/2$ given by Cai et al. [FOCS 1998, SICOMP 2001].

It is a major open problem whether the (min,+)-product of two n by n matrices has a truly sub-cubic time algorithm, as it is equivalent to the famous All-Pairs-Shortest-Paths problem (APSP) in n-vertex graphs. There are some restrictions of the (min,+)-product to special types of matrices that admit truly sub-cubic algorithms, each giving rise to a special case of APSP that can be solved faster. In this paper we consider a new, different and powerful restriction in which one matrix can be arbitrary, as long as the other matrix has "bounded differences" in either its columns or rows, i.e. any two consecutive entries differ by only a small amount. We obtain the first truly sub-cubic algorithm for this Bounded Differences (min,+)-product (answering an open problem of Chan and Lewenstein). Our new algorithm, combined with a strengthening of an approach of L. Valiant for solving context-free grammar parsing with matrix multiplication, yields the first truly sub-cubic algorithms for the following problems: Language Edit Distance (a major problem in the parsing community), RNA-folding (a major problem in bioinformatics) and Optimum Stack Generation (answering an open problem of Tarjan).

Given a set of numbers, the k-SUM problem asks for a subset of k numbers that sums to zero. When the numbers are integers, the time and space complexity of k-SUM is generally studied in the word-RAM model; when the numbers are reals, the complexity is studied in the real-RAM model, and space is measured by the number of reals held in memory at any point. We present a time and space efficient deterministic self-reduction for the k-SUM problem which holds for both models, and has many interesting consequences. To illustrate: - 3-SUM is in deterministic time O(n^2*lg(lg(n))/lg(n)) and space O(sqrt(n*lg(n)/lg(lg(n)))). In general, any polylogarithmic-time improvement over quadratic time for 3-SUM can be converted into an algorithm with an identical time improvement but low space complexity as well. - 3-SUM is in deterministic time O(n^2) and space O(sqrt(n)), derandomizing an algorithm of Wang. - A popular conjecture states that 3-SUM requires n^{2-o(1)} time on the word-RAM. We show that the 3-SUM Conjecture is in fact equivalent to the (seemingly weaker) conjecture that every O(n^{.51})-space algorithm for 3-SUM requires at least n^{2-o(1)} time on the word-RAM. - For k >= 4, k-SUM is in deterministic O(n^{k-2+2/k}) time and O(sqrt(n)) space.

This is an overview of the invited talk delivered at the 41st International Symposium on Mathematical Foundations of Computer Science (MFCS-2016).

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 reduction from the Orthogonal Vectors problem to Subtree Isomorphism, showing that a truly subquadratic algorithm for the latter refutes the Strong Exponential Time Hypothesis (SETH). In light of this conditional lower bound, we focus on natural special cases for which no truly subquadratic algorithms are known. We classify these cases against the quadratic barrier, showing in particular that: Even for binary, rooted trees, a truly subquadratic algorithm refutes SETH. Even for rooted trees of depth O (log log n), where n is the total number of vertices, a truly subquadratic algorithm refutes SETH. For every constant d, there is a constant ∊d > 0 and a randomized, truly subquadratic algorithm for degree-d rooted trees of depth at most (1 + ∊d)logdn. In particular, there is an O(min{2.85h, n2}) algorithm for binary trees of depth h. Our reductions utilize new “tree gadgets” that are likely useful for future SETH-based lower bounds for problems on trees. Our upper bounds apply a folklore result from randomized decision tree complexity.

The radius and diameter are fundamental graph parameters, with several natural definitions for directed graphs. Each definition is well-motivated in a variety of applications. All versions of diameter and radius can be solved via solving all-pairs shortest paths (APSP), followed by a fast postprocessing step. However, solving APSP on n-node graphs requires Ω(n2) time even in sparse graphs. We study the question: when can diameter and radius in sparse graphs be solved in truly subquadratic time, and when is such an algorithm unlikely? Motivated by our conditional lower bounds on computing these measures exactly in truly subquadratic time, we search for approximation and fixed parameter subquadratic algorithms, and alternatively, for reasons why they do not exist. We find that: Most versions of Diameter and Radius can be solved in truly subquadratic time with optimal approximation guarantees, under plausible assumptions. For example, there is a 2-approximation algorithm for directed Radius with one-way distances that runs in time, while a (2 – δ)-approximation algorithm in O(n2–∊) time is considered unlikely. On graphs with treewidth k, we can solve all versions in 2O(klogk)n1+O(1) time. We show that these algorithms are near optimal since even a (3/2 – δ)-approximation algorithm that runs in time 2o(k)n2–∊ would refute plausible assumptions. Two conceptual contributions of this work that we hope will incite future work are: the introduction of a Fixed Parameter Tractability in P framework, and the statement of a differently-quantified variant of the Orthogonal Vectors Conjecture, which we call the Hitting Set Conjecture.

We make improvements to the upper bounds on several popular types of distance preserving graph sketches. The first part of our paper concerns pairwise distance preservers, which are sparse subgraphs that exactly preserve the pairwise distances for a set of given pairs of vertices. Our main result here is that all unweighted, undirected n-node graphs G and all pair sets P have distance preservers on |H| = O(n2/3|P|2/3 + n|P|1/3) edges. This improves the known bounds whenever |P| = ω(n3/4). We then develop a new graph clustering technique, based on distance preservers, and we apply this technique to show new upper bounds for additive (standard) spanners, in which all pairwise distances must be preserved up to an additive error function, and for subset spanners, in which only distances within a given node subset must be preserved up to an error function. For both of these objects, we obtain the new best tradeoff between spanner sparsity and error allowance in the regime where the error is polynomial in the graph size. We leave open a conjecture that O(n2/3|P|2/3 + n) pairwise distance preservers are possible for undirected unweighted graphs. Resolving this conjecture in the affirmative would improve and simplify our upper bounds for all the graph sketches mentioned above.

A central goal of algorithmic research is to determine how fast computational problems can be solved in the worst case. Theorems from complexity theory state that there are problems that, on inputs of size n, can be solved in t(n) time but not in t(n)^{1-epsilon} time for epsilon>0. The main challenge is to determine where in this hierarchy various natural and important problems lie. Throughout the years, many ingenious algorithmic techniques have been developed and applied to obtain blazingly fast algorithms for many problems. Nevertheless, for many other central problems, the best known running times are essentially those of the classical algorithms devised for them in the 1950s and 1960s. Unconditional lower bounds seem very difficult to obtain, and so practically all known time lower bounds are conditional. For years, the main tool for proving hardness of computational problems have been NP-hardness reductions, basing hardness on P != NP. However, when we care about the exact running time (as opposed to merely polynomial vs non-polynomial), NP-hardness is not applicable, especially if the problem can already be solved in polynomial time. In recent years, a new theory has been developed, based on "fine-grained reductions" that focus on exact running times. The goal of these reductions is as follows. Suppose problem A is solvable in a(n) time and problem B in b(n) time, and no a(n)^{1-epsilon} and b(n)^{1-epsilon} algorithms are known for A and B respectively, for any epsilon>0. Then if A is fine-grained reducible to problem B (for a(n) and b(n)), then a b(n)^{1-epsilon} time algorithm for B (for any epsilon>0) implies an a(n)^{1-epsilon'} algorithm for A (for some epsilon'>0). Now, mimicking NP-hardness, the approach is to (1) select a key problem X that is conjectured to require t(n)^{1-o(1)} time for some t(n), and (2) reduce X in a fine-grained way to many important problems. This approach has led to the discovery of many meaningful relationships between problems, and even sometimes to equivalence classes. In this talk I will give an overview of the current progress in this area of study, and will highlight some new exciting developments.

A recent, active line of work achieves tight lower bounds for fundamental problems under the Strong Exponential Time Hypothesis (SETH). A celebrated result of Backurs and Indyk (STOC’15) proves that computing the Edit Distance of two sequences of length n in truly subquadratic O(n2−ε) time, for some ε>0, is impossible under SETH. The result was extended by follow-up works to simpler looking problems like finding the Longest Common Subsequence (LCS).

This special section comprises seven fully refereed papers whose extended abstracts were presented at the 54th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2013) in Berkeley, California, October 27--29, 2013. The unrefereed conference versions of these papers were published by IEEE in the FOCS 2013 proceedings. The regular conference program consisted of 79 papers chosen from among 281 submissions. These were selected by a program committee consisting of Alexandr Andoni, Maria-Florina Balcan, Kenneth L. Clarkson, Moritz Hardt, Johan Hast\aad, Nicole Immorlica, Yuval Ishai, Yael Tauman Kalai, Tali Kaufman, Jonathan Kelner, Adam Klivans, Amit Kumar, Yury Makarychev, Raghu Meka, Peter Bro Miltersen, Seffi Naor, Harald Räcke, Omer Reingold, Mohit Singh, Madhu Sudan, Paul Valiant, Virginia Vassilevska Williams, and John Watrous. The program committee was chaired by Omer Reingold. The papers invited to this special section were also selected with the input of the program committee. The seven papers in this section span a broad range of topics, including cryptography, approximation algorithms, dynamic algorithms, hardness of approximation, arithmetic complexity theory, quantum complexity, and learning theory. Each paper underwent an extensive refereeing process; we thank both the authors and the anonymous referees for their efforts. In addition, we would like to thank SICOMP Editor-in-Chief Leonard Schulman and SIAM Senior Publications Coordinator Heather Blythe for their help in preparing this special section.

Two important similarity measures between sequences are the longest common subsequence (LCS) and the dynamic time warping distance (DTWD). The computations of these measures for two given sequences are central tasks in a variety of applications. Simple dynamic programming algorithms solve these tasks in O(n2) time, and despite an extensive amount of research, no algorithms with significantly better worst case upper bounds are known. In this paper, we show that for any constant ε >0, an O(n2-ε) time algorithm for computing the LCS or the DTWD of two sequences of length n over a constant size alphabet, refutes the popular Strong Exponential Time Hypothesis (SETH).

The CFG recognition problem is: given a context-free grammar G and a string w of length n, decide if w can be obtained from G. This is the most basic parsing question and is a core computer science problem. Valiant's parser from 1975 solves the problem in O(nO) time, where ? <; 2:373 is the matrix multiplication exponent. Dozens of parsing algorithms have been proposed over the years, yet Valiant's upper bound remains unbeaten. The best combinatorial algorithms have mildly subcubic O(n3= log3 n) complexity. Lee (JACM'01) provided evidence that fast matrix multiplication is needed for CFG parsing, and that very efficient and practical algorithms might be hard or even impossible to obtain. Lee showed that any algorithm for a more general parsing problem with running time O(|G| n3 -- e) can be converted into a surprising subcubic algorithm for Boolean Matrix Multiplication. Unfortunately, Lee' s hardness result required that the grammar size be |G| = O(n6). Nothing was known for the more relevant case of constant size grammars. In this work, we prove that any improvement on Valiant' s algorithm, even for constant size grammars, either in terms of runtime or by avoiding the inefficiencies of fast matrix multiplication, would imply a breakthrough algorithm for the k-Clique problem: given a graph on n nodes, decide if there are k that form a clique. Besides classifying the complexity of a fundamental problem, our reduction has led us to similar lower bounds for more modern and well-studied cubic time problems for which faster algorithms are highly desirable in practice: RNA Folding, a central problem in computational biology, and Dyck Language Edit Distance, answering an open question of Saha (FOCS'14).

We obtain new upper bounds on the additive distortion for graph emulators and spanners on relatively few edges. We introduce a new subroutine called "strip creation," and we combine this subroutine with several other ideas to obtain the following results:

Algorithmic research strives to develop fast algorithms for fundamental problems. Despite its many successes, however, many problems still do not have very efficient algorithms. For years researchers have explained the hardness for key problems by proving NP-hardness, utilizing polynomial time reductions to base the hardness of key problems on the famous conjecture P != NP. For problems that already have polynomial time algorithms, however, it does not seem that one can show any sort of hardness based on P != NP. Nevertheless, we would like to provide evidence that a problem $A$ with a running time O(n^k) that has not been improved in decades, also requires n^{k-o(1)} time, thus explaining the lack of progress on the problem. Such unconditional time lower bounds seem very difficult to obtain, unfortunately. Recent work has concentrated on an approach mimicking NP-hardness: (1) select a few key problems that are conjectured to require T(n) time to solve, (2) use special, fine-grained reductions to prove time lower bounds for many diverse problems in P based on the conjectured hardness of the key problems. In this abstract we outline the approach, give some examples of hardness results based on the Strong Exponential Time Hypothesis, and present an overview of some of the recent work on the topic.

Measuring the importance of a node in a network is a major goal in the analysis of social networks, biological systems, transportation networks etc. Different centrality measures have been proposed to capture the notion of node importance. For example, the center of a graph is a node that minimizes the maximum distance to any other node (the latter distance is the radius of the graph). The median of a graph is a node that minimizes the sum of the distances to all other nodes. Informally, the betweenness centrality of a node w measures the fraction of shortest paths that have w as an intermediate node. Finally, the reach centrality of a node w is the smallest distance r such that any s-t shortest path passing through w has either s or t in the ball of radius r around w. The fastest known algorithms to compute the center and the median of a graph, and to compute the betweenness or reach centrality even of a single node take roughly cubic time in the number n of nodes in the input graph. It is open whether these problems admit truly subcubic algorithms, i.e. algorithms with running time Õ(n3–δ) for some constant δ > 01. We relate the complexity of the mentioned centrality problems to two classical problems for which no truly subcubic algorithm is known, namely All Pairs Shortest Paths (APSP) and Diameter. We show that Radius, Median and Betweenness Centrality are equivalent under subcubic reductions to APSP, i.e. that a truly subcubic algorithm for any of these problems implies a truly subcubic algorithm for all of them. We then show that Reach Centrality is equivalent to Diameter under subcubic reductions. The same holds for the problem of approximating Betweenness Centrality within any constant factor. Thus the latter two centrality problems could potentially be solved in truly subcubic time, even if APSP requires essentially cubic time.

We present new algorithms for finding induced four-node subgraphs in a given graph, which run in time roughly that of detecting a clique on three nodes (i.e., a triangle). The best known algorithms for triangle finding in an n-node graph take O(nω) time, where ω < 2.373 is the matrix multiplication exponent. We give a general randomized technique for finding any induced four-node subgraph, except for the clique or independent set on 4 nodes, in Õ (nω) time with high probability. The algorithm can be derandomized in some cases: we show how to detect a diamond (or its complement) in deterministic Õ(nω) time. Our approach substantially improves on prior work. For instance, the previous best algorithm for C4 detection ran in O(n3.3) time, and for diamond detection in O(n3) time. For sparse graphs with m edges, the best known triangle finding algorithm runs in O(m2ω/(ω+1)) ≤ O(m1.41) time. We give a randomized Õ(m2ω/(ω+1)) time algorithm (analogous to the best known for triangle finding) for finding any induced four-node subgraph other than C4, K4 and their complements. In the case of diamond detection, we also design a deterministic Õ(m2ω/(ω+1)) time algorithm. For C4 or its complement, we give randomized Õ(m(4ω–1)/(2ω+1)) ≤ O(m1.48) time finding algorithms. These algorithms substantially improve on prior work. For instance, the best algorithm for diamond detection ran in O(m1.5) time.

Due to the lack of unconditional polynomial lower bounds, it is now in fashion to prove conditional lower bounds in order to advance our understanding of the class P. The vast majority of these lower bounds are based on one of three famous hypotheses: the 3-SUM conjecture, the APSP conjecture, and the Strong Exponential Time Hypothesis. Only circumstantial evidence is known in support of these hypotheses, and no formal relationship between them is known. In hopes of obtaining "less conditional" and therefore more reliable lower bounds, we consider the conjecture that at least one of the above three hypotheses is true. We design novel reductions from 3-SUM, APSP, and CNF-SAT, and derive interesting consequences of this very plausible conjecture, including: Tight n3-o(1) lower bounds for purely-combinatorial problems about the triangles in unweighted graphs. New n1-o(1) lower bounds for the amortized update and query times of dynamic algorithms for single-source reachability, strongly connected components, and Max-Flow. New n1.5-o(1) lower bound for computing a set of n st-maximum-flow values in a directed graph with n nodes and ~O(n) edges. There is a hierarchy of natural graph problems on n nodes with complexity nc for c ∈ (2,3).

We consider several well-studied problems in dynamic algorithms and prove that sufficient progress on any of them would imply a breakthrough on one of five major open problems in the theory of algorithms: 1) Is the 3SUM problem on n numbers in O(n2-ε) time for some ε > 0? 2) Can one determine the satisfiability of a CNF formula on n variables and poly n clauses in O((2 - ε)npoly n) time for some ε > 0? 3) Is the All Pairs Shortest Paths problem for graphs on n vertices in O(n3-ε) time for some ε > 0? 4) Is there a linear time algorithm that detects whether a given graph contains a triangle? 5) Is there an O(n3-ε) time combinatorial algorithm for n×n Boolean matrix multiplication? The problems we consider include dynamic versions of bipartite perfect matching, bipartite maximum weight matching, single source reachability, single source shortest paths, strong connectivity, subgraph connectivity, diameter approximation and some nongraph problems such as Pagh's problem defined in a recent paper by Patrascu[STOC 2010].

The diameter is a fundamental graph parameter and its computation is necessary in many applications. The fastest known way to compute the diameter exactly is to solve the All-Pairs Shortest Paths (APSP) problem. In the absence of fast algorithms, attempts were made to seek fast algorithms that approximate the diameter. In a seminal result Aingworth, Chekuri, Indyk and Motwani [SODA'96 and SICOMP'99] designed an algorithm that computes in time an estimate for the diameter D in directed graphs with nonnegative edge weights, such that ⌊⅔ · D⌋ – (M – 1) ≤ ≤ D, where M is the maximum edge weight in the graph. In recent work, Roditty and Vassilevska W. [STOC 13] gave a Las Vegas algorithm that has the same approximation guarantee but improves the (expected) runtime to . Roditty and Vassilevska W. also showed that unless the Strong Exponential Time Hypothesis fails, no (n2−∊) time algorithm for sparse unweighted undirected graphs can achieve an approximation ratio better than . Thus their algorithm is essentially tight for sparse unweighted graphs. For weighted graphs however, the approximation guarantee can be meaningless, as M can be arbitrarily large. In this paper we exhibit two algorithms that achieve a genuine -approximation for the diameter, one running in time, and one running in time. Furthermore, our algorithms are deterministic, and thus we present the first deterministic (2 – ∊)-approximation algorithm for the diameter that takes subquadratic time in sparse graphs. In addition, we address the question of obtaining an additive c-approximation for the diameter, i.e. an estimate such that D – c ≤ ≤ D. An extremely simple time algorithm achieves an additive n∊-approximation; no better results are known. We show that for any ∊ > 0, getting an additive n∊-approximation algorithm for the diameter running in (n2−δ) time for any δ > 2∊ would falsify the Strong Exponential Time Hypothesis. Thus the simple algorithm is probably essentially tight for sparse graphs, and moreover, obtaining a subquadratic time additive c-approximation for any constant c is unlikely. Finally, we consider the problem of computing the eccentricities of all vertices in an undirected graph, i.e. the largest distance from each vertex. Roditty and Vassilevska W. [STOC 13] show that in time, one can compute for each v ∊ V in an undirected graph, an estimate ∊(v) for the eccentricity ∊(v) such that max {R, · ∊(v)} ≤ ∊(v) ≤ min {D, · ∊(v)} where R = minv ∊(v) is the radius of the graph. Here we improve the approximation guarantee by showing that a variant of the same algorithm can achieve estimates ∊′(v) with · ∊(v) ≤ ∊′(v) ≤ ∊(v).

The diameter and the radius of a graph are fundamental topological parameters that have many important practical applications in real world networks. The fastest combinatorial algorithm for both parameters works by solving the all-pairs shortest paths problem (APSP) and has a running time of ~O(mn) in m-edge, n-node graphs. In a seminal paper, Aingworth, Chekuri, Indyk and Motwani [SODA'96 and SICOMP'99] presented an algorithm that computes in ~O(m√ n + n2) time an estimate D for the diameter D, such that ⌊ 2/3 D ⌋ ≤ ^D ≤ D. Their paper spawned a long line of research on approximate APSP. For the specific problem of diameter approximation, however, no improvement has been achieved in over 15 years.

For a pattern graph $H$ on $k$ nodes, we consider the problems of finding and counting the number of (not necessarily induced) copies of $H$ in a given large graph $G$ on $n$ nodes, as well as finding minimum weight copies in both node-weighted and edge-weighted graphs. Our results include the following: 1. The number of copies of an $H$ with an independent set of size $s$ can be computed exactly in $O^*(2^s n^{k-s+3})$ time and $poly(n,k)+O(2^s n^2)$ space, or in $O^*(4^s n^{k-s+3})$ time and $poly(n,k)$ space. (The $O^*$ notation omits $poly(k)$ factors.) To obtain these algorithms we provide fast algorithms for computing the permanent of a $k \times n$ matrix over rings and semirings. 2. The number of copies of any $H$ having minimum (or maximum) node-weight (with arbitrary real weights on nodes) can be found in $O(n^{\omega k/3} + n^{2k/3+o(1)})$ time, where $\omega < 2.38$ is the matrix multiplication exponent and $k$ is divisible by $3$. Similar results hold for other values of $k$. Also, the number of copies having exactly a prescribed weight can be found within this time. These algorithms extend the technique of Czumaj and Lingas [in Proceedings of SODA 2007, ACM, New York, SIAM, Philadelphia, 2007, pp. 986--994] and give a new (algorithmic) application of multiparty communication complexity. 3. Finding an edge-weighted triangle of weight exactly $0$ in general graphs requires $\Omega(n^{3-\varepsilon})$ time for all $\varepsilon > 0$, unless the $3$SUM problem on $N$ numbers can be solved in $O(N^{2 - \varepsilon})$ time. This suggests that the edge-weighted problem is much harder than its node-weighted version.

A distance sensitivity oracle is a data structure which, given two nodes s and t in a directed edge-weighted graph G and an edge e, returns the shortest length of an s-t path not containing e, a so called replacement path for the triple (s, t, e). Such oracles are used to quickly recover from edge failures. In this paper we consider the case of integer weights in the interval [-M, M], and present the first distance sensitivity oracle that achieves simultaneously subcubic preprocessing time and sublinear query time. More precisely, for a given parameter α ∈ [0, 1], our oracle has preprocessing time Õ(Mnω+1/2+Mnω+α(4-ω)) and query time Õ(n1-α). Here wω+1-α) preprocessing time and (superlinear) Õ(n1+α) query time [Weimann, Yuster-FOCS'10]. The main novelty in our approach is an algorithm to compute all the replacement paths from a given source s, an interesting problem on its own. We can solve the latter single-source replacement paths problem in Õ(APSP(n, M))) time, where APSP(n, M)0.681n2.575) [Zwick-JACM'02] is the runtime for computing all-pairs shortest paths in a graph with n vertices and integer edge weights in [-M, M]. For positive weights the runtime of our algorithm reduces to Õ(Mnω). This matches the best known runtime for the simpler replacement paths problem in which both the source s and the target t are fixed [Vassilevska-SODA'11].

We study the problem of determining the girth of an unweighted undirected graph. We obtain several new efficient approximation algorithms for graphs with n nodes and m edges and unknown girth g. We consider additive and multiplicative approximations. Additive Approximations. We present: an Õ(n3/m)-time algorithm which returns a cycle of length at most g + 2 if g is even and g + 3 if g is odd. This complements the seminal work of Itai and Rodeh [SIAM J. Computing'78] who gave an algorithm that in O(n2) time finds a cycle of length g if g is even, and g + 1 if g is odd. an Õ(n3/m)-time algorithm which returns a cycle of length at most g′ + 2, where g′ is the length of the shortest even cycle in G. This result complements the work of Yuster and Zwick [SIAM J. Discrete Math'97] who showed how to compute g′ in O(n2) time. Multiplicative Approximations. We present: an Õ(n5/3)-time algorithm which returns a cycle of length at most 3g/2 + z/2 when g is even and 3g/2 + z/2 + 1 when g is odd, where z = –g mod 4, z ∊ {0, 1, 2, 3}. This gives an Õ(n5/3)-time 2-approximation for the girth, the first subquadratic 2-approximation algorithm, resolving an open question of Lingas and Lundell [IPL'09]. an O(n1.968)-time (8/5)-approximation algorithm for the girth in graphs with girth at least 4 (i.e., triangle-free graphs). This is the first subquadratic time (2–ε)-approximation algorithm for the girth for triangle-free graphs, for any ε > 0. We prove that a deterministic algorithm of this kind is not possible for directed graphs, thus showing a strong separation between undirected and directed graphs for girth approximation.

We develop an automated approach for designing matrix multiplication algorithms based on constructions similar to the Coppersmith-Winograd construction. Using this approach we obtain a new improved bound on the matrix multiplication exponent ω<2.3727.

We consider the fundamental algorithmic problem of finding a cycle of minimum weight in a weighted graph. In particular, we show that the minimum weight cycle problem in an undirected n-node graph with edge weights in {1,..., M} or in a directed n-node graph with edge weights in {-M,..., M} and no negative cycles can be efficiently reduced to finding a minimum weight triangle in an Θ(n)- node undirected graph with weights in {1,..., O(M)}. Roughly speaking, our reductions imply the following surprising phenomenon: a minimum cycle with an arbitrary number of weighted edges can be "encoded" using only three edges within roughly the same weight interval! This resolves a longstanding open problem posed in a seminal work by Itai and Rodeh [SIAM J. Computing 1978] on minimum cycle in unweighted graphs. A direct consequence of our efficient reductions are Õ(Mnω) ≤ 6(Mn2.376)-time algorithms using fast matrix multiplication (FMM) for finding a minimum weight cycle in both undirected graphs with integral weights from the interval [1, M] and directed graphs with integral weights from the interval [-M,M]. The latter seems to reveal a strong separation between the all pairs shortest paths (APSP) problem and the minimum weight cycle problem in directed graphs as the fastest known APSP algorithm has a running time of O(M0.681n2.575) by Zwick [J. ACM 2002]. In contrast, when only combinatorial algorithms are allowed (that is, without FMM) the only known solution to minimum weight cycle is by computing APSP. Interestingly, any separation between the two problems in this case would be an amazing breakthrough as by a recent paper by Vassilevska W. and Williams [FOCS'10], any O(n3-ε)-time algorithm (ε >; 0) for minimum weight cycle immediately implies a O(n3-δ)-time algorithm (δ >; 0) for APSP.

The replacement paths problem for directed graphs is to find for given nodes s and t and every edge e on the shortest path between them, the shortest path between s and t which avoids e. For unweighted directed graphs on n vertices, the best known algorithm runtime was \tilde{O}(n^{2.5}) by Roditty and Zwick. For graphs with integer weights in {-M,...,M}, Weimann and Yuster recently showed that one can use fast matrix multiplication and solve the problem in O(Mn^{2.584}) time, a runtime which would be O(Mn^{2.33}) if the exponent ωof matrix multiplication is 2. We improve both of these algorithms. Our new algorithm also relies on fast matrix multiplication and runs in O(M n^ω polylog(n)) time if ω>2 and O(n^{2+\eps}) for any \eps>0 if ω=2. Our result shows that, at least for small integer weights, the replacement paths problem in directed graphs may be easier than the related all pairs shortest paths problem in directed graphs, as the current best runtime for the latter is Ω(n^{2.5}) time even if ω=2.

We consider a very natural problem concerned with game manipulation. Let G be a directed graph where the nodes represent players of a game, and an edge from u to v means that u can beat v in the game. (If an edge (u, v) is not present, one cannot match u and v.) Given G and a "favorite" node A, is it possible to set up the bracket of a balanced single-elimination tournament so that A is guaranteed to win, if matches occur as predicted by G? We show that the problem is NP-complete for general graphs. For the case when G is a tournament graph we give several interesting conditions on the desired winner A for which there exists a balanced single-elimination tournament which A wins, and it can be found in polynomial time.

We say an algorithm on n × n matrices with entries in [-M, M] (or n-node graphs with edge weights from [-M, M]) is truly subcubic if it runs in O(n3-δ- poly(log M)) time for some δ > 0. We define a notion of subcubic reducibility, and show that many important problems on graphs and matrices solvable in O(n3) time are equivalent under subcubic reductions. Namely, the following weighted problems either all have truly subcubic algorithms, or none of them do: The all-pairs shortest paths problem (APSP). Detecting if a weighted graph has a triangle of negative total edge weight. Listing up to n2.99negative triangles in an edge-weighted graph. Finding a minimum weight cycle in a graph of nonnegative edge weights. The replacement paths problem in an edge-weighted digraph. Finding the second shortest simple path between two nodes in an edge-weighted digraph. Checking whether a given matrix defines a metric. Verifying the correctness of a matrix product over the (min, +)-semiring. Therefore, if APSP cannot be solved in n3-ε time for any ε > 0, then many other problems also need essentially cubic time. In fact we show generic equivalences between matrix products over a large class of algebraic structures used in optimization, verifying a matrix product over the same structure, and corresponding triangle detection problems over the structure. These equivalences simplify prior work on subcubic algorithms for all-pairs path problems, since it now suffices to give appropriate subcubic triangle detection algorithms. Other consequences of our work are new combinatorial approaches to Boolean matrix multiplication over the (OR, AND)semiring (abbreviated as BMM). We show that practical advances in triangle detection would imply practical BMM algorithms, among other results. Building on our techniques, we give two new BMM algorithms: a derandomization of the recent combinatorial BMM algorithm of Bansal and Williams (FOCS'09), and an improved quantum algorithm for BMM.

For a graph G with real weights assigned to the vertices (edges), the MAX H -SUBGRAPH problem is to find an H -subgraph of G with maximum total weight, if one exists. Our main results are new strongly polynomial algorithms for the MAX H -SUBGRAPH problem. Some of our algorithms are based, in part, on fast matrix multiplication. For vertex-weighted graphs with n vertices we solve a more general problem: the all pairs MAX H -SUBGRAPH problem, where the task is to find for every pair of vertices u,v, a maximum H -subgraph containing both u and v , if one exists. We obtain an O ( n t (ω, h )) -time algorithm for the all pairs MAX H -SUBGRAPH problem in the case where H is a fixed graph with h vertices and ω < 2.376 is the exponent of matrix multiplication. The value of t (ω, h ) is determined by solving a small integer program. In particular, heaviest triangles for all pairs can be found in O ( n 2+1/(4-ω) ) ≤ o ( n 2.616 )-time. For h =4,5,8 the running time of our algorithm essentially matches that of the (unweighted) H -subgraph detection problem. Using rectangular matrix multiplication, the value of t ( ω,h ) can be improved; for example, the runtime for triangles becomes O ( n 2.575 ). We also present improved algorithms for the MAX H -SUBGRAPH problem in the edge-weighted case. In particular, we obtain an O ( m 2−1/ k log n )-time algorithm for the heaviest cycle of length 2 k or 2 k −1 in a graph with m edges and an O ( n 3 /log n )-time randomized algorithm for finding the heaviest cycle of any fixed length. Our methods also yield efficient algorithms for several related problems that are faster than any previously existing algorithms. For example, we show how to find chromatic H -subgraphs in edge-colored graphs, and how to compute the most significant bits of the distance product of two real matrices, in truly subcubic time.

A travel booking office has timetables giving arrival and departure times for all scheduled trains, including their origins and destinations. A customer presents a starting station and demands a route with perhaps several train connections taking him to his destination as early as possible. The booking office must find the best route for its customers. This problem was first considered in the theory of algorithms by Minty [1958], who reduced it to a problem on directed edge-weighted graphs: find a path from a given source to a given target such that the consecutive weights on the path are nondecreasing and the last weight on the path is minimized. Minty gave the first algorithm for the single-source version of the problem, in which one finds minimum last weight nondecreasing paths from the source to every other vertex. In this article we give the first linear -time algorithm for this problem in the word-RAM model of computation. We also define an all-pairs version for the problem and give a strongly polynomial truly subcubic algorithm for it. Finally, we discuss an extension of the problem in which one also has prices on trip segments and one wishes to find a cheapest valid itinerary.

For a pattern graph H on k nodes, we consider the problems of finding and counting the number of (not necessarily induced) copies of H in a given large graph G on n nodes, as well as finding minimum weight copies in both node-weighted and edge-weighted graphs. Our results include: The number of copies of an H with an independent set of size s can be computed exactly in O*(2s nk-s+3) time. A minimum weight copy of such an H (with arbitrary real weights on nodes and edges) can be found in O(4s+o(s) nk-s+3) time. (The O* notation omits (k) factors.) These algorithms rely on fast algorithms for computing the permanent of a k x n matrix, over rings and semirings. The number of copies of any H having minimum (or maximum) node-weight (with arbitrary real weights on nodes) can be found in O(nω k/3 + n2k/3+o(1)) time, where ω < 2.4 is the matrix multiplication exponent and k is divisible by 3. Similar results hold for other values of k. Also, the number of copies having exactly a prescribed weight can be found within this time. These algorithms extend the technique of Czumaj and Lingas (SODA 2007) and give a new (algorithmic) application of multiparty communication complexity. Finding an edge-weighted triangle of weight exactly 0 in general graphs requires Ω(n2.5-ε) time for all ε > 0, unless the 3SUM problem on N numbers can be solved in O(N2 - ε) time. This suggests that the edge-weighted problem is much harder than its node-weighted version.

In the all-pairs bottleneck paths (APBP) problem (a.k.a. all-pairs maximum capacity paths), one is given a directed graph with real non-negative capacities on its edges and is asked to determine, for all pairs of vertices s and t, the capacity of a single path for which a maximum amount of flow can be routed from s to t. The APBP problem was first studied in operations research, shortly after the introduction of maximum flows and all-pairs shortest paths.

We present the first truly sub-cubic algorithms for finding a maximum node-weighted triangle in directed and undirected graphs with arbitrary real weights. The first is an O(B • n3+ω/2) = O(B • n2.688) deterministic algorithm, where n is the number of nodes, ω is the matrix multiplication exponent, and B is the number of bits of precision. The second is a strongly polynomial randomized algorithm that runs in O(n3+ω/2 log n) expected worst-case time. To achieve this, we show how to efficiently sample a weighted triangle uniformly at random, out of just those triangles whose total weight falls in some prescribed interval (W1,W2) for arbitrary weights W1 and W2. Previous approaches to the problem resulted in time bounds with either an exponential dependence on B, or a runtime of the form Ω(n3/(log n)c). The algorithms are easily extended to finding a maximum node-weighted induced subgraph on 3k nodes in Õ(n(3+ω)k/2) = O(n2.688 k) time.We give applications to a variety of problems, including a stable matching problem between buyers and sellers in computational economics, and discuss the possibility of extending our approach to a truly sub-cubic algorithm for computing all-pairs shortest paths on directed graphs with arbitrary weights.

The paper provides a high level overview of existing and imminent consumer electronics industry standards and related regulatory adoption activities. Developed within the Consumer Electronics Association (CEA), these standards and regulations comprise a hierarchy of work aggregated over the past two decades in home automation, home networking, and related in-home digital signal reception, distribution, and rendering. The standards leverage and converge other prevalent foundation standards from related industries, including information technology, the Internet, and cable and broadcast television. It is assumed that the reader is somewhat familiar with the foundation standards and terminology used in home networking. The paper lists the relevant CEA standards documents to which the reader may refer to obtain an in-depth understanding.