 Award ID(s):
 1741137
 Publication Date:
 NSFPAR ID:
 10220505
 Journal Name:
 ACMSIAM Symposium on Discrete Algorithms (SODA 2020)
 Sponsoring Org:
 National Science Foundation
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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 papermore »

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 papermore »

Mikolaj Bojanczyk ; Emanuela Merelli ; David P. Woodruff (Ed.)Two equal length strings are a parameterized match (pmatch) iff there exists a onetoone function that renames the symbols in one string to those in the other. The Parameterized Suffix Tree (PST) [Baker, STOC' 93] is a fundamental data structure that handles various string matching problems under this setting. The PST of a text T[1,n] over an alphabet Σ of size σ takes O(nlog n) bits of space. It can report any entry in (parameterized) (i) suffix array, (ii) inverse suffix array, and (iii) longest common prefix (LCP) array in O(1) time. Given any pattern P as a query, a position i in T is an occurrence iff T[i,i+P1] and P are a pmatch. The PST can count the number of occurrences of P in T in time O(Plog σ) and then report each occurrence in time proportional to that of accessing a suffix array entry. An important question is, can we obtain a compressed version of PST that takes space close to the text’s size of nlogσ bits and still support all three functionalities mentioned earlier? In SODA' 17, Ganguly et al. answered this question partially by presenting an O(nlogσ) bit index that can support (parameterized) suffix array andmore »

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 constraintsmore » 
Abstract Sequence mappability is an important task in genome resequencing. In the (
k ,m )mappability problem, for a given sequenceT of lengthn , the goal is to compute a table whosei th entry is the number of indices such that the length$$j \ne i$$ $j\ne i$m substrings ofT starting at positionsi andj have at mostk mismatches. Previous works on this problem focused on heuristics computing a rough approximation of the result or on the case of . We present several efficient algorithms for the general case of the problem. Our main result is an algorithm that, for$$k=1$$ $k=1$ , works in$$k=O(1)$$ $k=O\left(1\right)$ space and, with high probability, in$$O(n)$$ $O\left(n\right)$ time. Our algorithm requires a careful adaptation of the$$O(n \cdot \min \{m^k,\log ^k n\})$$ $O(n\xb7min\{{m}^{k},{log}^{k}n\})$k errata trees of Cole et al. [STOC 2004] to avoid multiple counting of pairs of substrings. Our technique can also be applied to solve the allpairs Hamming distance problem introduced by Crochemore et al. [WABI 2017]. We further develop time algorithms to compute$$O(n^2)$$ $O\left({n}^{2}\right)$all (k ,m )mappability tables for a fixedm and all or a fixed$$k\in \{0,\ldots ,m\}$$ $k\in \{0,\dots ,m\}$k and all . Finally, we show that, for$$m\in \{k,\ldots ,n\}$$ $m\in \{k,\dots ,n\}$ , the ($$k,m = \Theta (\log n)$$ $k,m=\Theta (logn)$k ,m )mappability problem cannot be solved in strongly subquadratic time unless the Strong Exponential Time Hypothesis fails. This is an improved and extended version of a paper presented at SPIRE 2018.