 NSFPAR ID:
 10065947
 Date Published:
 Journal Name:
 Proceedings of the 37th ACM Principles of Distributed Computing (PODC 2018)
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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Given a graph and an integer k, Densest kSubgraph is the algorithmic task of finding the subgraph on k vertices with the maximum number of edges. This is a fundamental problem that has been subject to intense study for decades, with applications spanning a wide variety of fields. The stateoftheart algorithm is an O(n^{1/4+ϵ})factor approximation (for any ϵ>0) due to Bhaskara et al. [STOC '10]. Moreover, the socalled logdensity framework predicts that this is optimal, i.e. it is impossible for an efficient algorithm to achieve an O(n^{1/4−ϵ})factor approximation. In the average case, Densest kSubgraph is a prototypical noisy inference task which is conjectured to exhibit a statisticalcomputational gap. In this work, we provide the strongest evidence yet of hardness for Densest kSubgraph by showing matching lower bounds against the powerful SumofSquares (SoS) algorithm, a metaalgorithm based on convex programming that achieves stateofart algorithmic guarantees for many optimization and inference problems. For k ≤ n^1/2, we obtain a degree n^δ SoS lower bound for the hard regime as predicted by the logdensity framework. To show this, we utilize the modern framework for proving SoS lower bounds on averagecase problems pioneered by Barak et al. [FOCS '16]. A key issue is that small denserthanaverage subgraphs in the input will greatly affect the value of the candidate pseudoexpectation operator around the subgraph. To handle this challenge, we devise a novel matrix factorization scheme based on the positive minimum vertex separator. We then prove an intersection tradeoff lemma to show that the error terms when using this separator are indeed small.more » « less

Abstract We consider the problem of covering multiple submodular constraints. Given a finite ground set
N , a weight function ,$$w: N \rightarrow \mathbb {R}_+$$ $w:N\to {R}_{+}$r monotone submodular functions over$$f_1,f_2,\ldots ,f_r$$ ${f}_{1},{f}_{2},\dots ,{f}_{r}$N and requirements the goal is to find a minimum weight subset$$k_1,k_2,\ldots ,k_r$$ ${k}_{1},{k}_{2},\dots ,{k}_{r}$ such that$$S \subseteq N$$ $S\subseteq N$ for$$f_i(S) \ge k_i$$ ${f}_{i}\left(S\right)\ge {k}_{i}$ . We refer to this problem as$$1 \le i \le r$$ $1\le i\le r$MultiSubmodCover and it was recently considered by HarPeled and Jones (Few cuts meet many point sets. CoRR.arxiv:abs1808.03260 HarPeled and Jones 2018) who were motivated by an application in geometry. Even with$$r=1$$ $r=1$MultiSubmodCover generalizes the wellknown Submodular Set Cover problem (SubmodSC ), and it can also be easily reduced toSubmodSC . A simple greedy algorithm gives an approximation where$$O(\log (kr))$$ $O(log(kr\left)\right)$ and this ratio cannot be improved in the general case. In this paper, motivated by several concrete applications, we consider two ways to improve upon the approximation given by the greedy algorithm. First, we give a bicriteria approximation algorithm for$$k = \sum _i k_i$$ $k={\sum}_{i}{k}_{i}$MultiSubmodCover that covers each constraint to within a factor of while incurring an approximation of$$(11/e\varepsilon )$$ $(11/e\epsilon )$ in the cost. Second, we consider the special case when each$$O(\frac{1}{\epsilon }\log r)$$ $O(\frac{1}{\u03f5}logr)$ is a obtained from a truncated coverage function and obtain an algorithm that generalizes previous work on partial set cover ($$f_i$$ ${f}_{i}$PartialSC ), covering integer programs (CIPs ) and multiple vertex cover constraints Bera et al. (Theoret Comput Sci 555:2–8 Bera et al. 2014). Both these algorithms are based on mathematical programming relaxations that avoid the limitations of the greedy algorithm. We demonstrate the implications of our algorithms and related ideas to several applications ranging from geometric covering problems to clustering with outliers. Our work highlights the utility of the highlevel model and the lens of submodularity in addressing this class of covering problems. 
We study local symmetry breaking problems in the Congest model, focusing on ruling set problems, which generalize the fundamental Maximal Independent Set (MIS) problem. The time (round) complexity of MIS (and ruling sets) have attracted much attention in the Local model. Indeed, recent results (Barenboim et al., FOCS 2012, Ghaffari SODA 2016) for the MIS problem have tried to break the longstanding O(log n)round "barrier" achieved by Luby's algorithm, but these yield o(log n)round complexity only when the maximum degree Delta is somewhat small relative to n. More importantly, these results apply only in the Local model. In fact, the best known time bound in the Congest model is still O(log n) (via Luby's algorithm) even for moderately small Delta (i.e., for Delta = Omega(log n) and Delta = o(n)). Furthermore, message complexity has been largely ignored in the context of local symmetry breaking. Luby's algorithm takes O(m) messages on medge graphs and this is the best known bound with respect to messages. Our work is motivated by the following central question: can we break the Theta(log n) time complexity barrier and the Theta(m) message complexity barrier in the Congest model for MIS or closelyrelated symmetry breaking problems? This paper presents progress towards this question for the distributed ruling set problem in the Congest model. A betaruling set is an independent set such that every node in the graph is at most beta hops from a node in the independent set. We present the following results:  Time Complexity: We show that we can break the O(log n) "barrier" for 2 and 3ruling sets. We compute 3ruling sets in O(log n/log log n) rounds with high probability (whp). More generally we show that 2ruling sets can be computed in O(log Delta (log n)^(1/2 + epsilon) + log n/log log n) rounds for any epsilon > 0, which is o(log n) for a wide range of Delta values (e.g., Delta = 2^(log n)^(1/2epsilon)). These are the first 2 and 3ruling set algorithms to improve over the O(log n)round complexity of Luby's algorithm in the Congest model.  Message Complexity: We show an Omega(n^2) lower bound on the message complexity of computing an MIS (i.e., 1ruling set) which holds also for randomized algorithms and present a contrast to this by showing a randomized algorithm for 2ruling sets that, whp, uses only O(n log^2 n) messages and runs in O(Delta log n) rounds. This is the first messageefficient algorithm known for ruling sets, which has message complexity nearly linear in n (which is optimal up to a polylogarithmic factor).more » « less

We study local symmetry breaking problems in the Congest model, focusing on ruling set problems, which generalize the fundamental Maximal Independent Set (MIS) problem. The time (round) complexity of MIS (and ruling sets) have attracted much attention in the Local model. Indeed, recent results (Barenboim et al., FOCS 2012, Ghaffari SODA 2016) for the MIS problem have tried to break the longstanding O(log n)round “barrier” achieved by Luby’s algorithm, but these yield o(log n)round complexity only when the maximum degree is somewhat small relative to n. More importantly, these results apply only in the Local model. In fact, the best known time bound in the Congest model is still O(log n) (via Luby’s algorithm) even for moderately small (i.e., for = (log n) and = o(n)). Furthermore, message complexity has been largely ignored in the context of local symmetry breaking. Luby’s algorithm takes O(m) messages on medge graphs and this is the best known bound with respect to messages. Our work is motivated by the following central question: can we break the (log n) time complexity barrier and the (m) message complexity barrier in the Congest model for MIS or closelyrelated symmetry breaking problems? This paper presents progress towards this question for the distributed ruling set problem in the Congest model. A ruling set is an independent set such that every node in the graph is at most hops from a node in the independent set. We present the following results: Time Complexity: We show that we can break the O(log n) “barrier” for 2 and 3ruling sets. We compute 3ruling sets in O log n log log n rounds with high probability (whp). More generally we show that 2ruling sets can be computed in O log · (log n)1/2+" + log n log log n rounds for any " > 0, which is o(log n) for a wide range of values (e.g., = 2(log n)1/2−" ). These are the first 2 and 3ruling set algorithms to improve over the O(log n)round complexity of Luby’s algorithm in the Congest model. Message Complexity: We show an (n2) lower bound on the message complexity of computing an MIS (i.e., 1ruling set) which holds also for randomized algorithms and present a contrast to this by showing a randomized algorithm for 2ruling sets that, whp, uses only O(n log2 n) messages and runs in O( log n) rounds. This is the first messageefficient algorithm known for ruling sets, which has message complexity nearly linear in n (which is optimal up to a polylogarithmic factor).more » « less

null (Ed.)The 2Wasserstein distance (or RMS distance) is a useful measure of similarity between probability distributions with exciting applications in machine learning. For discrete distributions, the problem of computing this distance can be expressed in terms of finding a minimumcost perfect matching on a complete bipartite graph given by two multisets of points A, B ⊂ ℝ2, with A = B = n, where the ground distance between any two points is the squared Euclidean distance between them. Although there is a nearlinear time relative ∊approximation algorithm for the case where the ground distance is Euclidean (Sharathkumar and Agarwal, JACM 2020), all existing relative ∊approximation algorithms for the RMS distance take Ω(n3/2) time. This is primarily because, unlike Euclidean distance, squared Euclidean distance is not a metric. In this paper, for the RMS distance, we present a new ∊approximation algorithm that runs in O(n^5/4 poly{log n, 1/∊}) time. Our algorithm is inspired by a recent approach for finding a minimumcost perfect matching in bipartite planar graphs (Asathulla et al, TALG 2020). Their algorithm depends heavily on the existence of sublinear sized vertex separators as well as shortest path data structures that require planarity. Surprisingly, we are able to design a similar algorithm for a complete geometric graph that is far from planar and does not have any vertex separators. Central components of our algorithm include a quadtreebased distance that approximates the squared Euclidean distance and a data structure that supports both Hungarian search and augmentation in sublinear time.more » « less