Vizing’s theorem states that any \(n\)-vertex \(m\)-edge graph of maximum degree \(\Delta\) can be edge colored using at most \(\Delta + 1\) different colors. Vizing’s original proof is easily translated into a deterministic \(O(mn)\) time algorithm. This deterministic time bound was subsequently improved to \(\tilde{O}(m\sqrt{n})\) time, independently by [Arjomandi, 1982] and by [Gabow et al., 1985].

We establish the first update-time separation between dynamic algorithms against oblivious adversaries and those against adaptive adversaries in natural dynamic graph problems, based on popular fine-grained complexity hypotheses.

We give the first algorithms for fully dynamic set cover with non-trivial worst-case guarantees for both recourse and update time. Specifically, we achieve O(logn) recourse and f· log(n) update time in the worst-case, for both approximation regimes: O(logn) and O(f) approximation. Prior to our work, all results for this problem either settled for amortized bounds on recourse and update time, or obtained f· log(n) update time in the worst-case but at the cost of Ω(m) worst-case recourse. (Here, m, n, f respectively denote the number of sets, maximum number of elements, and maximum frequency.)

We consider the “minimum degree spanning tree” problem. As input, we receive an undirected, connected graph G=(V, E) with n nodes and m edges, and our task is to find a spanning tree T of G that minimizes maxu ∈ V degT(u), where degT(u) denotes the degree of u ∈ V in T.

Vizing’s theorem states that any n -vertex m -edge graph of maximum degree Δ can be edge colored using at most Δ + 1 different colors [Vizing, 1964]. Vizing’s original proof is algorithmic and shows that such an edge coloring can be found in O(mn) time. This was subsequently improved to \(\tilde{O}(m\sqrt {n})\) time, independently by [Arjomandi, 1982] and by [Gabow et al., 1985]. 1 Very recently, independently and concurrently, using randomization, this runtime bound was further improved to \(\tilde{O}(n^2)\) by [Assadi, 2024] and \(\tilde{O}(mn^{1/3})\) by [Bhattacharya, Carmon, Costa, Solomon and Zhang, 2024] (and subsequently to \(\tilde{O}(mn^{1/4})\) by [Bhattacharya, Costa, Solomon and Zhang, 2024]). In this article, we present a randomized algorithm that computes a Δ + 1-edge coloring in near-linear time—in fact, only O(m log Δ) time—with high probability, giving a near-optimal algorithm for this fundamental problem .

In this paper, we consider the *metric $k$-center* problem in the fully dynamic setting, where we are given a metric space $(V,d)$ evolving via a sequence of point insertions and deletions and our task is to maintain a subset $S \subseteq V$ of at most $k$ points that minimizes the objective $\max_{x \in V} \min_{y \in S}d(x, y)$. We want to design our algorithm so that we minimize its *approximation ratio*, *recourse* (the number of changes it makes to the solution $S$) and *update time* (the time it takes to handle an update). We give a simple algorithm for dynamic $k$-center that maintains a $O(1)$-approximate solution with $O(1)$ amortized recourse and $\tilde O(k)$ amortized update time, *obtaining near-optimal approximation, recourse and update time simultaneously*. We obtain our result by combining a variant of the dynamic $k$-center algorithm of Bateni et al. [SODA'23] with the dynamic sparsifier of Bhattacharya et al. [NeurIPS'23].

Vizing’s Theorem from 1964 states that any n-vertex m-edge graph with maximum degree Δ can be edge colored using at most Δ + 1 colors. For over 40 years, the state-of-the-art running time for computing such a coloring, obtained independently by Arjomandi [1982] and by Gabow, Nishizeki, Kariv, Leven and Terada [1985], was . Very recently, this time bound was improved in two independent works, by Bhattacharya, Carmon, Costa, Solomon and Zhang to , and by Assadi to Õ (n2).

Vizing’s theorem states that any n-vertex m-edge graph of maximum degree Δ can be edge colored using at most Δ + 1 different colors [Vizing, 1964]. Vizing’s original proof is algorithmic and shows that such an edge coloring can be found in O(mn) time. This was subsequently improved to Õ(m√n) time, independently by [Arjomandi, 1982] and by [Gabow et al., 1985]. Very recently, independently and concurrently, using randomization, this runtime bound was further improved to Õ(n2) by [Assadi, 2024] and Õ(mn1/3) by [Bhattacharya, Carmon, Costa, Solomon and Zhang, 2024] (and subsequently to Õ(mn1/4) by [Bhattacharya, Costa, Solomon and Zhang, 2024]). In this paper, we present a randomized algorithm that computes a (Δ+1)-edge coloring in near-linear time—in fact, only O(mlogΔ) time—with high probability, giving a near-optimal algorithm for this fundamental problem.

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Vizing's theorem asserts the existence of a $(Δ+1)$-edge coloring for any graph $G$, where $Δ= Δ(G)$ denotes the maximum degree of $G$. Several polynomial time $(Δ+1)$-edge coloring algorithms are known, and the state-of-the-art running time (up to polylogarithmic factors) is $\tilde{O}(\min\{m \cdot \sqrt{n}, m \cdot Δ\})$, by Gabow et al.\ from 1985, where $n$ and $m$ denote the number of vertices and edges in the graph, respectively. (The $\tilde{O}$ notation suppresses polylogarithmic factors.) Recently, Sinnamon shaved off a polylogarithmic factor from the time bound of Gabow et al. The {arboricity} $α= α(G)$ of a graph $G$ is the minimum number of edge-disjoint forests into which its edge set can be partitioned, and it is a measure of the graph's "uniform density". While $α\le Δ$ in any graph, many natural and real-world graphs exhibit a significant separation between $α$ and $Δ$. In this work we design a $(Δ+1)$-edge coloring algorithm with a running time of $\tilde{O}(\min\{m \cdot \sqrt{n}, m \cdot Δ\})\cdot \fracαΔ$, thus improving the longstanding time barrier by a factor of $\fracαΔ$. In particular, we achieve a near-linear runtime for bounded arboricity graphs (i.e., $α= \tilde{O}(1)$) as well as when $α= \tilde{O}(\fracΔ{\sqrt{n}})$. Our algorithm builds on Sinnamon's algorithm, and can be viewed as a density-sensitive refinement of it.

Vizing's theorem states that any n-vertex m-edge graph of maximum degree$\Delta$can be edge colored using at most$\Delta+1$different colors [Diskret. Analiz, '64]. Vizing's original proof is algorithmic and shows that such an edge coloring can be found in$\tilde{O}(mn)$time. This was subsequently improved to$\tilde{O}(m\sqrt{n})$, independently by Arjomandi [1982] and by Gabow et al. [1985]. In this paper we present an algorithm that computes such an edge coloring in$\tilde{O}(mn^{1/3})$, time, giving the first polynomial improvement for this fundamental problem in over 40 years.

In the dynamic metric$k-\mathbf{median}$problem, we wish to maintain a set of$k$centers$S\subseteq V$in an input metric space$(V, d)$that gets updated via point insertions/deletions, so as to minimize the objective$\sum\nolimits_{x\in V}\min\nolimits_{y\in S}d(x, y)$. The quality of a dynamic algorithm is measured in terms of its approximation ratio, “recourse” (the number of changes in$S$per update) and “update time” (the time it takes to handle an update). The ultimate goal in this line of research is to obtain a dynamic$O(1)$approximation algorithm with$\tilde{O}(1)$recourse and$\tilde{O}(k)$update time. Dynamic$k-\mathbf{median}$is a canonical example of a class of problems known as dynamic$k-\mathbf{clustering}$, that has received significant attention in recent years [Fichtenberger et al, SODA'21], [Bateni et al, SODA'23], [Lacki et al, SODA'24]. To the best of our knowledge, however, all these previous papers either attempt to minimize the algorithm's recourse while ignoring its update time, or minimize the algorithm's update time while ignoring its recourse. For dynamic$k-\mathbf{median}$in particular, the state-of-the-art results get$\tilde{O}(k^{2})$update time and$O(k)$recourse [Cohen-Addad et al, ICML'19], [Henzinger and Kale, ESA'20], [Bhattacharya et al, NeurIPS'23]. But, this recourse bound of$O(k)$can be trivially obtained by recomputing an optimal solution from scratch after every update, provided we ignore the update time. In addition, the update time of$\tilde{O}(k^{2})$is polynomially far away from the desired bound of$\tilde{O}(k)$. We come arbitrarily close to resolving the main open question on this topic, with the following results. (I) We develop a new framework of randomized local search that is suitable for adaptation in a dynamic setting. For every$\epsilon > 0$, this gives us a dynamic$k-\mathbf{median}$algorithm with$O(k^{\epsilon})$approximation ratio,$\tilde{O}(k^{\epsilon})$recourse and$\tilde{O}(k^{1+\epsilon})$update time. This framework also generalizes to dynamic$k-\mathbf{clustering}$with$\ell^{p}$-norm objectives. As a corollary, we obtain similar bounds for the dynamic$k-\mathbf{means}$problem, and a new trade-off between approximation ratio, recourse and update time for the dynamic$k-\mathbf{center}$problem. (II) If it suffices to maintain only an estimate of the value of the optimal$k-\mathbf{median}$objective, then we obtain a$O(1)$approximation algorithm with$\tilde{O}(k)$update time. We achieve this result via adapting the Lagrangian Relaxation framework of [Jain and Vazirani, JACM'01], and a facility location algorithm of [Mettu and Plaxton, FOCS'00] in the dynamic setting.

We study the facility location problem in the dynamic setting, where the goal is to efficiently process an intermixed sequence of point insertions and deletions while maintaining a high quality and stable solution. Although the problem has been studied in the context of general metrics and low-dimensional spaces, much remains unknown concerning dynamic facility location in high dimensional spaces. In this work, we present the first fully dynamic algorithm for facility location in high-dimensional spaces $\mathbb{R}^{d}$. For any $c \geq 1$, our algorithm achieves $O(c)$-approximation, supports point updates in $\tilde{O}(\mathrm{poly}(d)n^{1/c + o(1)})$ amortized time and incurs $O(1)$ amortized recourse. More generally, our result shows that despite the linear-time lower bound on the update time for general metrics, it is possible to achieve sub-linear update times for metric spaces that admit dynamic nearest neighbour oracles. Experiments on real datasets confirm that our algorithm achieves high-quality solutions with low running time, and incurs minimal recourse.

We consider the problem of maintaining a (1 + ɛ)∆-edge coloring in a dynamic graph G with n nodes and maximum degree at most Δ. The state-of-the-art update time is Oɛ(polylog(n)), by Duan, He and Zhang [SODA’19] and by Christiansen [STOC’23], and more precisely O(log7 n/ɛ2), where Δ = Ω(log2 n/ɛ2).

We study dynamic (1−є)-approximate rounding of fractional matchings—a key ingredient in numerous breakthroughs in the dynamic graph algorithms literature. Our first contribution is a surprisingly simple deterministic rounding algorithm in bipartite graphs with amortized update time O(є−1 log2 (є−1 · n)), matching an (unconditional) recourse lower bound of Ω(є−1) up to logarithmic factors. Moreover, this algorithm’s update time improves provided the minimum (non-zero) weight in the fractional matching is lower bounded throughout. Combining this algorithm with novel dynamic partial rounding algorithms to increase this minimum weight, we obtain a number of algorithms that improve this dependence on n. For example, we give a high-probability randomized algorithm with Õ(є−1 · (loglogn)2)-update time against adaptive adversaries. Using our rounding algorithms, we also round known (1−є)-decremental fractional bipartite matching algorithms with no asymptotic overhead, thus improving on state-of-the-art algorithms for the decremental bipartite matching problem. Further, we provide extensions of our results to general graphs and to maintaining almost-maximal matchings.

We present dynamic algorithms with polylogarithmic update time for estimating the size of the maximum matching of a graph undergoing edge insertions and deletions with approximation ratio strictly better than 2 . Specifically, we obtain a \(1+\tfrac{1}{\sqrt {2}}+\epsilon \approx 1.707+\epsilon\) approximation in bipartite graphs and a \(1.973+\epsilon\) approximation in general graphs. We thus answer in the affirmative the value version of the major open question repeatedly asked in the dynamic graph algorithms literature. Our randomized algorithms’ approximation and worst-case update time bounds both hold w.h.p. against adaptive adversaries. Our algorithms are based on simulating new two-pass streaming matching algorithms in the dynamic setting. Our key new idea is to invoke the recent sublinear-time matching algorithm of Behnezhad (FOCS’21) in a white-box manner to efficiently simulate the second pass of our streaming algorithms, while bypassing the well-known vertex-update barrier.

We study the problem of chasing positive bodies in $\ell_{1}$: given a sequence of bodies $K_{t}=\left\{x^{t} \in \mathbb{R}_{+}^{n} \mid C^{t} x^{t} \geq 1, P^{t} x^{t} \leq 1\right\}$ revealed online, where $C^{t}$ and $P^{t}$ are nonnegative matrices, the goal is to (approximately) maintain a point $x_{t} \in K_{t}$ such that $\sum_{t}\left\|x_{t}-x_{t-1}\right\|_{1}$ is minimized. This captures the fully-dynamic low-recourse variant of any problem that can be expressed as a mixed packing-covering linear program and thus also the fractional version of many central problems in dynamic algorithms such as set cover, load balancing, hyperedge orientation, minimum spanning tree, and matching.We give an $O(\log d)$-competitive algorithm for this problem, where d is the maximum row sparsity of any matrix $C^{t}$. This bypasses and improves exponentially over the lower bound of $\sqrt{n}$ known for general convex bodies. Our algorithm is based on iterated information projections, and, in contrast to general convex body chasing algorithms, is entirely memoryless.We also show how to round our solution dynamically to obtain the first fully dynamic algorithms with competitive recourse for all the stated problems above; i.e. their recourse is less than the recourse of every other algorithm on every update sequence, up to polylogarithmic factors. This is a significantly stronger notion than the notion of absolute recourse in the dynamic algorithms literature.

We show a fully dynamic algorithm for maintaining $(1+\epsilon)$-approximate size of maximum matching of the graph with n vertices and m edges using $m^{0.5-\Omega_{\epsilon}(1)}$ update time. This is the first polynomial improvement over the long-standing $O(n)$ update time, which can be trivially obtained by periodic recomputation. Thus, we resolve the value version of a major open question of the dynamic graph algorithms literature (see, e.g., [Gupta and Peng FOCS’13], [Bernstein and Stein SODA’16], [Behnezhad and Khanna SODA’22]). Our key technical component is the first sublinear algorithm for $(1, \epsilon n)$-approximate maximum matching with sublinear running time on dense graphs. All previous algorithms suffered a multiplicative approximation factor of at least 1.499 or assumed that the graph has a very small maximum degree.

In the dynamic linear program (LP) problem, we are given an LP undergoing updates and we need to maintain an approximately optimal solution. Recently, significant attention (e.g. [Gupta et al. STOC'17; Arar et al. ICALP'18, Wajc STOC'20]) has been devoted to the study of special cases of dynamic packing and covering LPs, such as the dynamic fractional matching and set cover problems. But until now, there is no non-trivial dynamic algorithm for general packing and covering LPs.

We present dynamic algorithms with polylogarithmic update time for estimating the size of the maximum matching of a graph undergoing edge insertions and deletions with approximation ratio strictly better than 2. Specifically, we obtain a approximation in bipartite graphs and a 1.973 + ε approximation in general graphs. We thus answer in the affirmative the value version of the major open question repeatedly asked in the dynamic graph algorithms literature. Our randomized algorithms' approximation and worst-case update time bounds both hold w.h.p. against adaptive adversaries. Our algorithms are based on simulating new two-pass streaming matching algorithms in the dynamic setting. Our key new idea is to invoke the recent sublinear-time matching algorithm of Behnezhad (FOCS'21) in a white-box manner to efficiently simulate the second pass of our streaming algorithms, while bypassing the well-known vertex-update barrier.

We study sublinear time algorithms for estimating the size of maximum matching. After a long line of research, the problem was finally settled by Behnezhad ‍[FOCS’22], in the regime where one is willing to pay an approximation factor of 2. Very recently, Behnezhad et al. ‍[SODA’23] improved the approximation factor to (2−1/2O(1/γ)) using n1+γ time. This improvement over the factor 2 is, however, minuscule and they asked if even 1.99-approximation is possible in n2−Ω(1) time.

Abstract. In the dynamic minimum set cover problem, the challenge is to minimize the update time while guaranteeing a close-to-optimal [Formula: see text] approximation factor. (Throughout, [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] are parameters denoting the maximum number of elements, the number of sets, the frequency, and the cost range.) In the high-frequency range, when [Formula: see text], this was achieved by a deterministic [Formula: see text]-approximation algorithm with [Formula: see text] amortized update time by Gupta et al. [ Online and dynamic algorithms for set cover, in Proceedings STOC 2017, ACM, pp. 537–550]. In this paper we consider the low-frequency range, when [Formula: see text], and obtain deterministic algorithms with a [Formula: see text]-approximation ratio and the following guarantees on the update time. (1) [Formula: see text] amortized update time: Prior to our work, the best approximation ratio guaranteed by deterministic algorithms was [Formula: see text] of Bhattacharya, Henzinger, and Italiano [ Design of dynamic algorithms via primal-dual method, in Proceedings ICALP 2015, Springer, pp. 206–218]. In contrast, the only result with [Formula: see text]-approximation was that of Abboud et al. [ Dynamic set cover: Improved algorithms and lower bounds, in Proceedings STOC 2019, ACM, pp. 114–125], who designed a randomized [Formula: see text]-approximation algorithm with [Formula: see text] amortized update time. (2) [Formula: see text] amortized update time: This result improves the above update time bound for most values of [Formula: see text] in the low-frequency range, i.e., [Formula: see text]. It is also the first result that is independent of [Formula: see text] and [Formula: see text]. It subsumes the constant amortized update time of Bhattacharya and Kulkarni [ Deterministically maintaining a [Formula: see text]-approximate minimum vertex cover in [Formula: see text] amortized update time, in Proceedings SODA 2019, SIAM, pp. 1872–1885] for unweighted dynamic vertex cover (i.e., when [Formula: see text] and [Formula: see text]). (3) [Formula: see text] worst-case update time: No nontrivial worst-case update time was previously known for the dynamic set cover problem. Our bound subsumes and improves by a logarithmic factor the [Formula: see text] worst-case update time for the unweighted dynamic vertex cover problem (i.e., when [Formula: see text] and [Formula: see text]) of Bhattacharya, Henzinger, and Nanongkai [ Fully dynamic approximate maximum matching and minimum vertex cover in [Formula: see text] worst case update time, in Proceedings SODA 2017, SIAM, pp. 470–489]. We achieve our results via the primal-dual approach, by maintaining a fractional packing solution as a dual certificate. Prior work in dynamic algorithms that employs the primal-dual approach uses a local update scheme that maintains relaxed complementary slackness conditions for every set. For our first result we use instead a global update scheme that does not always maintain complementary slackness conditions. For our second result we combine the global and the local update schema. To achieve our third result we use a hierarchy of background schedulers. It is an interesting open question whether this background scheduler technique can also be used to transform algorithms with amortized running time bounds into algorithms with worst-case running time bounds.

Designing dynamic algorithms against an adaptive adversary whose performance match the ones assuming an oblivious adversary is a major research program in the field of dynamic graph algorithms. One of the prominent examples whose oblivious-vs-adaptive gap remains maximally large is the \emph{fully dynamic spanner} problem; there exist algorithms assuming an oblivious adversary with near-optimal size-stretch trade-off using only $\operatorname{polylog}(n)$ update time [Baswana, Khurana, and Sarkar TALG'12; Forster and Goranci STOC'19; Bernstein, Forster, and Henzinger SODA'20], while against an adaptive adversary, even when we allow infinite time and only count recourse (i.e. the number of edge changes per update in the maintained spanner), all previous algorithms with stretch at most $\log^{5}(n)$ require at least $Ω(n)$ amortized recourse [Ausiello, Franciosa, and Italiano ESA'05]. In this paper, we completely close this gap with respect to recourse by showing algorithms against an adaptive adversary with near-optimal size-stretch trade-off and recourse. More precisely, for any $k\ge1$, our algorithm maintains a $(2k-1)$-spanner of size $O(n^{1+1/k}\log n)$ with $O(\log n)$ amortized recourse, which is optimal in all parameters up to a $O(\log n)$ factor. As a step toward algorithms with small update time (not just recourse), we show another algorithm that maintains a $3$-spanner of size $\tilde O(n^{1.5})$ with $\operatorname{polylog}(n)$ amortized recourse \emph{and} simultaneously $\tilde O(\sqrt{n})$ worst-case update time.

We consider the classic facility location problem in fully dynamic data streams, where elements can be both inserted and deleted. In this problem, one is interested in maintaining a stable and high quality solution throughout the data stream while using only little time per update (insertion or deletion). We study the problem and provide the first algorithm that at the same time maintains a constant approximation and incurs polylogarithmic amortized recourse per update. We complement our theoretical results with an experimental analysis showing the practical efficiency of our method.

Computing a dense subgraph is a fundamental problem in graph mining, with a diverse set of applications ranging from electronic commerce to community detection in social networks. In many of these applications, the underlying context is better modelled as a weighted hypergraph that keeps evolving with time.

The problem of (Δ +1)-vertex coloring a graph of maximum degree Δ has been extremely well studied over the years in various settings and models. Surprisingly, for the dynamic setting, almost nothing was known until recently. In SODA’18, Bhattacharya, Chakrabarty, Henzinger and Nanongkai devised a randomized algorithm for maintaining a (Δ +1)-coloring with O (log Δ) expected amortized update time. In this article, we present an improved randomized algorithm for (Δ +1)-coloring that achieves O (1) amortized update time and show that this bound holds not only in expectation but also with high probability. Our starting point is the state-of-the-art randomized algorithm for maintaining a maximal matching (Solomon, FOCS’16). We carefully build on the approach of Solomon, but, due to inherent differences between the maximal matching and (Δ +1)-coloring problems, we need to deviate significantly from it in several crucial and highly nontrivial points. 1

We present a framework for deterministically rounding a dynamic fractional matching. Applying our framework in a black-box manner on top of existing fractional matching algorithms, we derive the following new results: (1) The first deterministic algorithm for maintaining a (2-δ)-approximate maximum matching in a fully dynamic bipartite graph, in arbitrarily small polynomial update time. (2) The first deterministic algorithm for maintaining a (1+δ)-approximate maximum matching in a decremental bipartite graph, in polylogarithmic update time. (3) The first deterministic algorithm for maintaining a (2+δ)-approximate maximum matching in a fully dynamic general graph, in small polylogarithmic (specifically, O(log⁴ n)) update time. These results are respectively obtained by applying our framework on top of the fractional matching algorithms of Bhattacharya et al. [STOC'16], Bernstein et al. [FOCS'20], and Bhattacharya and Kulkarni [SODA'19]. Previously, there were two known general-purpose rounding schemes for dynamic fractional matchings. Both these schemes, by Arar et al. [ICALP'18] and Wajc [STOC'20], were randomized. Our rounding scheme works by maintaining a good matching-sparsifier with bounded arboricity, and then applying the algorithm of Peleg and Solomon [SODA'16] to maintain a near-optimal matching in this low arboricity graph. To the best of our knowledge, this is the first dynamic matching algorithm that works on general graphs by using an algorithm for low-arboricity graphs as a black-box subroutine. This feature of our rounding scheme might be of independent interest.

Nearly thirty years ago, Bar-Noy, Motwani and Naor [IPL'92] conjectured that an online (1 + o(1))Δ-edge-coloring algorithm exists for n-node graphs of maximum degree Δ = ω(log n). This conjecture remains open in general, though it was recently proven for bipartite graphs under one-sided vertex arrivals by Cohen et al. [FOCS'19]. In a similar vein, we study edge coloring under widely-studied relaxations of the online model. Our main result is in the random-order online model. For this model, known results fall short of the Bar-Noy et al. conjecture, either in the degree bound [Aggarwal et al. FOCS'03], or number of colors used [Bahmani et al. SODA'10]. We achieve the best of both worlds, thus resolving the Bar-Noy et al. conjecture in the affirmative for this model. Our second result is in the adversarial online (and dynamic) model with recourse. A recent algorithm of Duan et al. [SODA'19] yields a (1 + ∊) Δ-edge-coloring with poly(log n/∊) recourse. We achieve the same with poly(1/∊) recourse, thus removing all dependence on n. Underlying our results is one common offline algorithm, which we show how to implement in these two online models. Our algorithm, based on the Rödl Nibble Method, is an adaptation of the distributed algorithm of Dubhashi et al. [TCS'98]. The Nibble Method has proven successful for distributed edge coloring. We display its usefulness in the context of online algorithms.

In the dynamic minimum set cover problem, a challenge is to minimize the update time while guaranteeing close to the optimal min(O(log n), f) approximation factor. (Throughout, m, n, f, and C are parameters denoting the maximum number of sets, number of elements, frequency, and the cost range.) In the high-frequency range, when f = Ω(log n), this was achieved by a deterministic O(log n)-approximation algorithm with O(f log n) amortized update time [Gupta et al. STOC'17]. In the low-frequency range, the line of work by Gupta et al. [STOC'17], Abboud et al. [STOC'19], and Bhattacharya et al. [ICALP'15, IPCO'17, FOCS'19] led to a deterministic (1 + ∊) f-approximation algorithm with O(f log(Cn)/∊2) amortized update time. In this paper we improve the latter update time and provide the first bounds that subsume (and sometimes improve) the state-of-the-art dynamic vertex cover algorithms. We obtain: (1) (1 + ∊) f-approximation ratio in O(f log2(Cn)/∊3) worst-case update time: No non-trivial worst-case update time was previously known for dynamic set cover. Our bound subsumes and improves by a logarithmic factor the O(log3 n/poly(∊)) worst-case update time for unweighted dynamic vertex cover (i.e., when f = 2 and C = 1) by Bhattacharya et al. [SODA'17]. (2) (1 + ∊) f-approximation ratio in O ((f2/∊3) + (f/∊2) log C) amortized update time: This result improves the previous O(f log (Cn)/∊2) update time bound for most values of f in the low-frequency range, i.e. whenever f = o(log n). It is the first that is independent of m and n. It subsumes the constant amortized update time of Bhattacharya and Kulkarni [SODA'19] for unweighted dynamic vertex cover (i.e., when f = 2 and C = 1). These results are achieved by leveraging the approximate complementary slackness and background schedulers techniques. These techniques were used in the local update scheme for dynamic vertex cover. Our main technical contribution is to adapt these techniques within the global update scheme of Bhattacharya et al. [FOCS'19] for the dynamic set cover problem.

We consider the problem of incremental cycle detection and topological ordering in a directed graph G = (V, E) with |V| = n nodes. In this setting, initially the edge-set E of the graph is empty. Subsequently, at each time-step an edge gets inserted into G. After every edge-insertion, we have to report if the current graph contains a cycle, and as long as the graph remains acyclic, we have to maintain a topological ordering of the node-set V. Let m be the total number of edges that get inserted into G. We present a randomized algorithm for this problem with Õ(m4/3) total expected update time. Our result improves the Õ(m • min(m1/2, n2/3)) total update time bound of [5, 9, 10, 7]. In particular, for m = O(n), our result breaks the longstanding barrier on the total update time. Furthermore, whenever m = o(n3/2), our result improves upon the recently obtained total update time bound of [6]. We note that if m = Ω(n3/2), then the algorithm of [5, 4, 7], which has Õ(n2) total update time, beats the performance of the time algorithm of [6]. It follows that we improve upon the total update time of the algorithm of [6] in the “interesting” range of sparsity where m = o(n3/2). Our result also happens to be the first one that breaks the lower bound of [9] on the total update time of any local algorithm for a nontrivial range of sparsity. Specifically, the total update time of our algorithm is whenever . From a technical perspective, we obtain our result by combining the algorithm of [6] with the balanced search framework of [10].

To date, the only way to argue polynomial lower bounds for dynamic algorithms is via fine-grained complexity arguments. These arguments rely on strong assumptions about specific problems such as the Strong Exponential Time Hypothesis (SETH) and the Online Matrix-Vector Multiplication Conjecture (OMv). While they have led to many exciting discoveries, dynamic algorithms still miss out some benefits and lessons from the traditional “coarse-grained” approach that relates together classes of problems such as P and NP. In this paper we initiate the study of coarse-grained complexity theory for dynamic algorithms. Below are among questions that this theory can answer. What if dynamic Orthogonal Vector (OV) is easy in the cell-probe model? A research program for proving polynomial unconditional lower bounds for dynamic OV in the cell-probe model is motivated by the fact that many conditional lower bounds can be shown via reductions from the dynamic OV problem (e.g. [Abboud, V.-Williams, FOCS 2014]). Since the cell-probe model is more powerful than word RAM and has historically allowed smaller upper bounds (e.g. [Larsen, Williams, SODA 2017; Chakraborty, Kamma, Larsen, STOC 2018]), it might turn out that dynamic OV is easy in the cell-probe model, making this research direction infeasible. Our theory implies that if this is the case, there will be very interesting algorithmic consequences: If dynamic OV can be maintained in polylogarithmic worst-case update time in the cell-probe model, then so are several important dynamic problems such as k-edge connectivity, (1 + ϵ)-approximate mincut, (1 + ϵ)-approximate matching, planar nearest neighbors, Chan's subset union and 3-vs-4 diameter. The same conclusion can be made when we replace dynamic OV by, e.g., subgraph connectivity, single source reachability, Chan's subset union, and 3-vs-4 diameter. Lower bounds for k-edge connectivity via dynamic OV? The ubiquity of reductions from dynamic OV raises a question whether we can prove conditional lower bounds for, e.g., k-edge connectivity, approximate mincut, and approximate matching, via the same approach. Our theory provides a method to refute such possibility (the so-called non-reducibility). In particular, we show that there are no “efficient” reductions (in both cell-probe and word RAM models) from dynamic OV to k-edge connectivity under an assumption about the classes of dynamic algorithms whose analogue in the static setting is widely believed. We are not aware of any existing assumptions that can play the same role. (The NSETH of Carmosino et al. [ITCS 2016] is the closest one, but is not enough.) To show similar results for other problems, one only need to develop efficient randomized verification protocols for such problems.

Abstract We consider the problems of maintaining an approximate maximum matching and an approximate minimum vertex cover in a dynamic graph undergoing a sequence of edge insertions/deletions. Starting with the seminal work of Onak and Rubinfeld (in: Proceedings of the ACM symposium on theory of computing (STOC), 2010), this problem has received significant attention in recent years. Very recently, extending the framework of Baswana et al. (in: Proceedings of the IEEE symposium on foundations of computer science (FOCS), 2011) , Solomon (in: Proceedings of the IEEE symposium on foundations of computer science (FOCS), 2016) gave a randomized dynamic algorithm for this problem that has an approximation ratio of 2 and an amortized update time of O(1) with high probability. This algorithm requires the assumption of an oblivious adversary, meaning that the future sequence of edge insertions/deletions in the graph cannot depend in any way on the algorithm’s past output. A natural way to remove the assumption on oblivious adversary is to give a deterministic dynamic algorithm for the same problem in O(1) update time. In this paper, we resolve this question. We present a new deterministic fully dynamic algorithm that maintains a O(1)-approximate minimum vertex cover and maximum fractional matching, with an amortized update time of O(1). Previously, the best deterministic algorithm for this problem was due to Bhattacharya et al. (in: Proceedings of the ACM-SIAM symposium on discrete algorithms (SODA), 2015); it had an approximation ratio of $$(2+\varepsilon )$$ (2+ε) and an amortized update time of $$O(\log n/\varepsilon ^2)$$ O(logn/ε2) . Our result can be generalized to give a fully dynamic $$O(f^3)$$ O(f3) -approximate algorithm with $$O(f^2)$$ O(f2) amortized update time for the hypergraph vertex cover and fractional hypergraph matching problem, where every hyperedge has at most f vertices.

We present a deterministic dynamic algorithm for maintaining a (1+ε)f-approximate minimum cost set cover with O(f log(Cn)/ε^2) amortized update time, when the input set system is undergoing element insertions and deletions. Here, n denotes the number of elements, each element appears in at most f sets, and the cost of each set lies in the range [1/C, 1]. Our result, together with that of Gupta~et~al.~[STOC'17], implies that there is a deterministic algorithm for this problem with O(f log(Cn)) amortized update time and O(min(log n, f)) -approximation ratio, which nearly matches the polynomial-time hardness of approximation for minimum set cover in the static setting. Our update time is only O(log (Cn)) away from a trivial lower bound. Prior to our work, the previous best approximation ratio guaranteed by deterministic algorithms was O(f^2), which was due to Bhattacharya~et~al.~[ICALP`15]. In contrast, the only result that guaranteed O(f) -approximation was obtained very recently by Abboud~et~al.~[STOC`19], who designed a dynamic algorithm with (1+ε)f-approximation ratio and O(f^2 log n/ε) amortized update time. Besides the extra O(f) factor in the update time compared to our and Gupta~et~al.'s results, the Abboud~et~al.~algorithm is randomized, and works only when the adversary is oblivious and the sets are unweighted (each set has the same cost). We achieve our result via the primal-dual approach, by maintaining a fractional packing solution as a dual certificate. This approach was pursued previously by Bhattacharya~et~al.~and Gupta~et~al., but not in the recent paper by Abboud~et~al. Unlike previous primal-dual algorithms that try to satisfy some local constraints for individual sets at all time, our algorithm basically waits until the dual solution changes significantly globally, and fixes the solution only where the fix is needed.

We consider the problem of maintaining an (approximately) minimum vertex cover in an n-node graph G = (V, E) that is getting updated dynamically via a sequence of edge insertions/deletions. We show how to maintain a (2 + ∊)-approximate minimum vertex cover, deterministically, in this setting in O(1/∊2) amortized update time. Prior to our work, the best known deterministic algorithm for maintaining a (2 + ∊)-approximate minimum vertex cover was due to Bhattacharya, Henzinger and Italiano [SODA 2015]. Their algorithm has an update time of O(log n/∊2). Recently, Bhattacharya, Chakrabarty, Henzinger [IPCO 2017] and Gupta, Krishnaswamy, Kumar, Panigrahi [STOC 2017] showed how to maintain an O(1)-approximation in O(1)-amortized update time for the same problem. Our result gives an exponential improvement over the update time of Bhattacharya et al. [SODA 2015], and nearly matches the performance of the randomized algorithm of Solomon [FOCS 2016] who gets an approximation ratio of 2 and an expected amortized update time of O(1). We derive our result by analyzing, via a novel technique, a variant of the algorithm by Bhattacharya et al. We consider an idealized setting where the update time of an algorithm can take any arbitrary fractional value, and use insights from this setting to come up with an appropriate potential function. Conceptually, this framework mimics the idea of an LP-relaxation for an optimization problem. The difference is that instead of relaxing an integral objective function, we relax the update time of an algorithm itself. We believe that this technique will find further applications in the analysis of dynamic algorithms.

We design fast dynamic algorithms for proper vertex and edge colorings in a graph undergoing edge insertions and deletions. In the static setting, there are simple linear time algorithms for (Δ + 1)- vertex coloring and (2Δ – 1)-edge coloring in a graph with maximum degree Δ. It is natural to ask if we can efficiently maintain such colorings in the dynamic setting as well. We get the following three results. (1) We present a randomized algorithm which maintains a (Δ + 1)-vertex coloring with O(log Δ) expected amortized update time. (2) We present a deterministic algorithm which maintains a (1 + o(1)Δ-vertex coloring with O(polylog Δ) amortized update time. (3) We present a simple, deterministic algorithm which maintains a (2Δ – 1)-edge coloring with O(log Δ) worst-case update time. This improves the recent O(Δ)-edge coloring algorithm with worst-case update time [4].

We present the first deterministic data structures for maintaining approximate minimum vertex cover and maximum matching in a fully dynamic graph $G = (V,E)$, with $|V| = n$ and $|E| =m$, in $o(\sqrt{m})$ time per update. In particular, for minimum vertex cover, we provide deterministic data structures for maintaining a $(2+\epsilon)$ approximation in $O(\log n/\epsilon^2)$ amortized time per update. For maximum matching, we show how to maintain a $(3+\epsilon)$ approximation in $O(\min(\sqrt{n}/\epsilon, m^{1/3}/\epsilon^2)$ amortized time per update and a $(4+\epsilon)$ approximation in $O(m^{1/3}/\epsilon^2)$ worst-case time per update. Our data structure for fully dynamic minimum vertex cover is essentially near-optimal and settles an open problem by Onak and Rubinfeld [in 42nd ACM Symposium on Theory of Computing, Cambridge, MA, ACM, 2010, pp. 457--464].

Recently there has been extensive work on maintaining (approximate) maximum matchings in dynamic graphs. We consider a generalisation of this problem known as the maximum b-matching: Every node v has a positive integral capacity b_v, and the goal is to maintain an (approximate) maximum-cardinality subset of edges that contains at most b_v edges incident on every node v. The maximum matching problem is a special case of this problem where b_v = 1 for every node v. Bhattacharya, Henzinger and Italiano [ICALP 2015] showed how to maintain a O(1) approximate maximum b-matching in a graph in O(log^3 n) amortised update time. Their approximation ratio was a large (double digit) constant. We significantly improve their result both in terms of approximation ratio as well as update time. Specifically, we design a randomised dynamic algorithm that maintains a (2+epsilon)-approximate maximum $b$-matching in expected amortised O(1/epsilon^4) update time. Thus, for every constant epsilon in (0, 1), we get expected amortised O(1) update time. Our algorithm generalises the framework of Baswana, Gupta, Sen [FOCS 2011] and Solomon [FOCS 2016] for maintaining a maximal matching in a dynamic graph.

We consider the problem of maintaining an approximately maximum (fractional) matching and an approximately minimum vertex cover in a dynamic graph. Starting with the seminal paper by Onak and Rubinfeld [STOC 2010], this problem has received significant attention in recent years. There remains, however, a polynomial gap between the best known worst case update time and the best known amortised update time for this problem, even after allowing for randomisation. Specifically, Bernstein and Stein [ICALP 2015, SODA 2016] have the best known worst case update time. They present a deterministic data structure with approximation ratio (3/2 + ∊) and worst case update time O(m1/4/ ∊2), where m is the number of edges in the graph. In recent past, Gupta and Peng [FOCS 2013] gave a deterministic data structure with approximation ratio (1+ ∊) and worst case update time No known randomised data structure beats the worst case update times of these two results. In contrast, the paper by Onak and Rubinfeld [STOC 2010] gave a randomised data structure with approximation ratio O(1) and amortised update time O(log2 n), where n is the number of nodes in the graph. This was later improved by Baswana, Gupta and Sen [FOCS 2011] and Solomon [FOCS 2016], leading to a randomised date structure with approximation ratio 2 and amortised update time O(1). We bridge the polynomial gap between the worst case and amortised update times for this problem, without using any randomisation. We present a deterministic data structure with approximation ratio (2 + ∊) and worst case update time O(log3 n), for all sufficiently small constants ∊.

Online social networks allow the collection of large amounts of data about the influence between users connected by a friendship-like relationship. When distributing items among agents forming a social network, this information allows us to exploit network externalities that each agent receives from his neighbors that get the same item. In this paper we consider Friends-of-Friends (2-hop) network externalities, i.e., externalities that not only depend on the neighbors that get the same item but also on neighbors of neighbors. For these externalities we study a setting where multiple different items are assigned to unit-demand agents. Specifically, we study the problem of welfare maximization under different types of externality functions. Let n be the number of agents and m be the number of items. Our contributions are the following: (1) We show that welfare maximization is APX-hard; we show that even for step functions with 2-hop (and also with 1-hop) externalities it is NP-hard to approximate social welfare better than (1−1/e). (2) On the positive side we present (i) an $O(\sqrt n)$ -approximation algorithm for general concave externality functions, (ii) an O(log m)-approximation algorithm for linear externality functions, and (iii) a $\frac {5}{18}(1-1/e)$ -approximation algorithm for 2-hop step function externalities. We also improve the result from [7] for 1-hop step function externalities by giving a $\frac {1}{2}(1-1/e)$ -approximation algorithm.

We present two deterministic dynamic algorithms for the maximum matching problem. (1) An algorithm that maintains a (2+є)-approximate maximum matching in general graphs with O(poly(logn, 1/є)) update time. (2) An algorithm that maintains an αK approximation of the value of the maximum matching with O(n2/K) update time in bipartite graphs, for every sufficiently large constant positive integer K. Here, 1≤ αK < 2 is a constant determined by the value of K. Result (1) is the first deterministic algorithm that can maintain an o(logn)-approximate maximum matching with polylogarithmic update time, improving the seminal result of Onak et al. [STOC 2010]. Its approximation guarantee almost matches the guarantee of the best randomized polylogarithmic update time algorithm [Baswana et al. FOCS 2011]. Result (2) achieves a better-than-two approximation with arbitrarily small polynomial update time on bipartite graphs. Previously the best update time for this problem was O(m1/4) [Bernstein et al. ICALP 2015], where m is the current number of edges in the graph.

We present the first deterministic data structures for maintaining approximate minimum vertex cover and maximum matching in a fully dynamic graph in time per update. In particular, for minimum vertex cover we provide deterministic data structures for maintaining a (2 + ε) approximation in O(log n/ε2) amortized time per update. For maximum matching, we show how to maintain a (3 + e) approximation in O(m1/3/ε2) amortized time per update, and a (4 + ε) approximation in O(m1/3/ε2) worst-case time per update. Our data structure for fully dynamic minimum vertex cover is essentially near-optimal and settles an open problem by Onak and Rubinfeld [13].

Online social networks allow the collection of large amounts of data about the influence between users connected by a friendship-like relationship. When distributing items among agents forming a social network, this information allows us to exploit network externalities that each agent receives from his neighbors that get the same item. In this paper we consider Friends-of-Friends (2-hop) network externalities, i.e., externalities that not only depend on the neighbors that get the same item but also on neighbors of neighbors. For these externalities we study a setting where multiple different items are assigned to unit-demand agents. Specifically, we study the problem of welfare maximization under different types of externality functions. Let n be the number of agents and m be the number of items. Our contributions are the following: (1) We show that welfare maximization is APX-hard; we show that even for step functions with 2-hop (and also with 1-hop) externalities it is NP-hard to approximate social welfare better than (1-1/e). (2) On the positive side we present (i) an O(sqrt n)-approximation algorithm for general concave externality functions, (ii) an O(\log m)-approximation algorithm for linear externality functions, and (iii) an (1-1/e)\frac{1}{6}-approximation algorithm for 2-hop step function externalities. We also improve the result from [6] for 1-hop step function externalities by giving a (1-1/e)/2-approximation algorithm.

While in many graph mining applications it is crucial to handle a stream of updates efficiently in terms of both time and space, not much was known about achieving such type of algorithm. In this paper we study this issue for a problem which lies at the core of many graph mining applications called densest subgraph problem. We develop an algorithm that achieves time- and space-efficiency for this problem simultaneously. It is one of the first of its kind for graph problems to the best of our knowledge.

We study the price of anarchy of coordination mechanisms for a scheduling problem where each job j has a weight wj, processing time pij, assignment cost hij, and communication delay (or release date) rij, on machine i. Each machine is free to declare its own scheduling policy. Each job is a selfish agent and selects a machine that minimizes its own disutility, which is equal to its weighted completion time plus its assignment cost. The goal is to minimize the total disutility incurred by all the jobs. Our model is general enough to capture scheduling jobs in a distributed environment with heterogeneous machines (or data centers) that are situated across different locations.

We construct prior-free auctions with constant-factor approximation guarantees with ordered bidders, in both unlimited and limited supply settings. We compare the expected revenue of our auctions on a bid vector to the monotone price benchmark, the maximum revenue that can be obtained from a bid vector using supply-respecting prices that are nonincreasing in the bidder ordering and bounded above by the second-highest bid. As a consequence, our auctions are simultaneously near-optimal in a wide range of Bayesian multi-unit environments.

In commerce search, the set of products returned by a search engine often forms the basis for all user interactions leading up to a potential transaction on the web. Such a set of products is known as the consideration set. In this study, we consider the problem of generating consideration set of products in commerce search so as to maximize user satisfaction. One of the key features of commerce search that we exploit in our study is the association of a set of important attributes with the products and a set of specified attributes with the user queries. Those important attributes not used in the query are treated as unspecified. The attribute space admits a natural definition of user satisfaction via user preferences on the attributes and their values, viz. require that the surfaced products be close to the specified attribute values in the query, and diverse with respect to the unspecified attributes. We model this as a general Max-Sum Dispersion problem wherein we are given a set of n nodes in a metric space and the objective is to select a subset of nodes with total cost at most a given budget, and maximize the sum of the pairwise distances between the selected nodes. In our setting, each node denotes a product, the cost of a node being inversely proportional to its relevance with respect to specified attributes. The distance between two nodes quantifies the diversity with respect to the unspecified attributes. The problem is NP-hard and a 2-approximation was previously known only when all the nodes have unit cost.

In this paper, we consider the problem of designing incentive compatible auctions for multiple (homogeneous) units of a good, when bidders have private valuations and private budget constraints. When only the valuations are private and the budgets are public, Dobzinski et al [8] show that the adaptive clinching auction is the unique incentive-compatible auction achieving Pareto-optimality. They further show that this auction is not truthful with private budgets, so that there is no deterministic Pareto-optimal auction with private budgets. Our main contribution is to show the following Budget Monotonicity property of this auction: When there is only one infinitely divisible good, a bidder cannot improve her utility by reporting a budget smaller than the truth. This implies that the adaptive clinching auction is incentive compatible when over-reporting the budget is not possible (for instance, when funds must be shown upfront). We can also make reporting larger budgets suboptimal with a small randomized modification to the auction. In either case, this makes the modified auction Pareto-optimal with private budgets. We also show that the Budget Monotonicity property does not hold for auctioning indivisible units of the good, showing a sharp contrast between the divisible and indivisible cases. The Budget Monotonicity property also implies other improved results in this context. For revenue maximization, the same auction improves the best-known competitive ratio due to Abrams [1] by a factor of 4, and asymptotically approaches the performance of the optimal single-price auction. Finally, we consider the problem of revenue maximization (or social welfare) in a Bayesian setting. We allow the bidders have public size constraints (on the amount of good they are willing to buy) in addition to private budget constraints. We show a simple poly-time computable 5.83-approximation to the optimal Bayesian incentive compatible mechanism, that is implementable in dominant strategies. Our technique again crucially needs the ability to prevent bidders from over-reporting budgets via randomization. We show the approximation result via designing a rounding scheme for an LP relaxation of the problem, which may be of independent interest.

In this paper, we present the first approximation algorithms for the problem of designing revenue optimal Bayesian incentive compatible auctions when there are multiple (heterogeneous) items and when bidders have arbitrary demand and budget constraints (and additive valuations). Our mechanisms are surprisingly simple: We show that a sequential all-pay mechanism is a 4 approximation to the revenue of the optimal ex-interim truthful mechanism with a discrete type space for each bidder, where her valuations for different items can be correlated. We also show that a sequential posted price mechanism is a O(1) approximation to the revenue of the optimal ex-post truthful mechanism when the type space of each bidder is a product distribution that satisfies the standard hazard rate condition. We further show a logarithmic approximation when the hazard rate condition is removed, and complete the picture by showing that achieving a sub-logarithmic approximation, even for regular distributions and one bidder, requires pricing bundles of items. Our results are based on formulating novel LP relaxations for these problems, and developing generic rounding schemes from first principles.