Non-Overlapping Indexing - Cache Obliviously

The non-overlapping indexing problem is defined as follows: pre-process a given text T[1,n] of length n into a data structure such that whenever a pattern P[1,p] comes as an input, we can efficiently report the largest set of non-overlapping occurrences of P in T. The best known solution is by Cohen and Porat [ISAAC, 2009]. Their index size is O(n) words and query time is optimal O(p+nocc), where nocc is the output size. We study this problem in the cache-oblivious model and present a new data structure of size O(n log n) words. It can answer queries in optimal O(p/(B)+log_B n+nocc/B) I/Os, where B is the block size

A 2-Approximation Algorithm for the Complementary Maximal Strip Recovery Problem

The Maximal Strip Recovery problem (MSR) and its complementary (CMSR) are well-studied NP-hard problems in computational genomics. The input of these dual problems are two signed permutations. The goal is to delete some gene markers from both permutations, such that, in the remaining permutations, each gene marker has at least one common neighbor. Equivalently, the resulting permutations could be partitioned into common strips of length at least two. Then MSR is to maximize the number of remaining genes, while the objective of CMSR is to delete the minimum number of gene markers. In this paper, we present a new approximation algorithm for the Complementary Maximal Strip Recovery (CMSR) problem. Our approximation factor is 2, improving the currently best 7/3-approximation algorithm. Although the improvement on the factor is not huge, the analysis is greatly simplified by a compensating method, commonly referred to as the non-oblivious local search technique. In such a method a substitution may not always increase the value of the current solution (it sometimes may even decrease the solution value), though it always improves the value of another function seemingly unrelated to the objective function

Online graph coloring against a randomized adversary

Electronic version of an article published as Online graph coloring against a randomized adversary. "International journal of foundations of computer science", 1 Juny 2018, vol. 29, núm. 4, p. 551-569. DOI:10.1142/S0129054118410058 © 2018 copyright World Scientific Publishing Company. https://www.worldscientific.com/doi/abs/10.1142/S0129054118410058We consider an online model where an adversary constructs a set of 2s instances S instead of one single instance. The algorithm knows S and the adversary will choose one instance from S at random to present to the algorithm. We further focus on adversaries that construct sets of k-chromatic instances. In this setting, we provide upper and lower bounds on the competitive ratio for the online graph coloring problem as a function of the parameters in this model. Both bounds are linear in s and matching upper and lower bound are given for a specific set of algorithms that we call “minimalistic online algorithms”.Peer ReviewedPostprint (author's final draft

The Heaviest Induced Ancestors Problem Revisited

We revisit the heaviest induced ancestors problem, which has several interesting applications in string matching. Let T_1 and T_2 be two weighted trees, where the weight W(u) of a node u in either of the two trees is more than the weight of u\u27s parent. Additionally, the leaves in both trees are labeled and the labeling of the leaves in T_2 is a permutation of those in T_1. A node x in T_1 and a node y in T_2 are induced, iff their subtree have at least one common leaf label. A heaviest induced ancestor query HIA(u_1,u_2) is: given a node u_1 in T_1 and a node u_2 in T_2, output the pair (u_1^*,u_2^*) of induced nodes with the highest combined weight W(u^*_1) + W(u^*_2), such that u_1^* is an ancestor of u_1 and u^*_2 is an ancestor of u_2. Let n be the number of nodes in both trees combined and epsilon >0 be an arbitrarily small constant. Gagie et al. [CCCG\u27 13] introduced this problem and proposed three solutions with the following space-time trade-offs: - an O(n log^2n)-word data structure with O(log n log log n) query time - an O(n log n)-word data structure with O(log^2 n) query time - an O(n)-word data structure with O(log^{3+epsilon}n) query time. In this paper, we revisit this problem and present new data structures, with improved bounds. Our results are as follows. - an O(n log n)-word data structure with O(log n log log n) query time - an O(n)-word data structure with O(log^2 n/log log n) query time. As a corollary, we also improve the LZ compressed index of Gagie et al. [CCCG\u27 13] for answering longest common substring (LCS) queries. Additionally, we show that the LCS after one edit problem of size n [Amir et al., SPIRE\u27 17] can also be reduced to the heaviest induced ancestors problem over two trees of n nodes in total. This yields a straightforward improvement over its current solution of O(n log^3 n) space and O(log^3 n) query time

An Estimator for Matching Size in Low Arboricity Graphs with Two Applications

In this paper, we present a new simple degree-based estimator for the size of maximum matching in bounded arboricity graphs. When the arboricity of the graph is bounded by $\alpha$, the estimator gives a $\alpha+2$ factor approximation of the matching size. For planar graphs, we show the estimator does better and returns a $3.5$ approximation of the matching size. Using this estimator, we get new results for approximating the matching size of planar graphs in the streaming and distributed models of computation. In particular, in the vertex-arrival streams, we get a randomized $O(\frac{\sqrt{n}}{\epsilon^2}\log n)$ space algorithm for approximating the matching size within $(3.5+\epsilon)$ factor in a planar graph on $n$ vertices. Similarly, we get a simultaneous protocol in the vertex-partition model for approximating the matching size within $(3.5+\epsilon)$ factor using $O(\frac{n^{2/3}}{\epsilon^2}\log n)$ communication from each player. In comparison with the previous estimators, the estimator in this paper does not need to know the arboricity of the input graph and improves the approximation factor for the case of planar graphs

Forbidden Extension Queries

Document retrieval is one of the most fundamental problem in information retrieval. The objective is to retrieve all documents from a document collection that are relevant to an input pattern. Several variations of this problem such as ranked document retrieval, document listing with two patterns and forbidden patterns have been studied. We introduce the problem of document retrieval with forbidden extensions. Let D={T_1,T_2,...,T_D} be a collection of D string documents of n characters in total, and P^+ and P^- be two query patterns, where P^+ is a proper prefix of P^-. We call P^- as the forbidden extension of the included pattern P^+. A forbidden extension query asks to report all occ documents in D that contains P^+ as a substring, but does not contain P^- as one. A top-k forbidden extension query asks to report those k documents among the occ documents that are most relevant to P^+. We present a linear index (in words) with an O(|P^-| + occ) query time for the document listing problem. For the top-k version of the problem, we achieve the following results, when the relevance of a document is based on PageRank: - an O(n) space (in words) index with O(|P^-|log sigma+ k) query time, where sigma is the size of the alphabet from which characters in D are chosen. For constant alphabets, this yields an optimal query time of O(|P^-|+ k). - for any constant epsilon > 0, a |CSA| + |CSA^*| + Dlog frac{n}{D} + O(n) bits index with O(search(P)+ k cdot tsa cdot log ^{2+epsilon} n) query time, where search(P) is the time to find the suffix range of a pattern P, tsa is the time to find suffix (or inverse suffix) array value, and |CSA^*| denotes the maximum of the space needed to store the compressed suffix array CSA of the concatenated text of all documents, or the total space needed to store the individual CSA of each document

Planar Matching in Streams Revisited

We present data stream algorithms for estimating the size or weight of the maximum matching in low arboricity graphs. A large body of work has focused on improving the constant approximation factor for general graphs when the data stream algorithm is permitted O(n polylog n) space where n is the number of nodes. This space is necessary if the algorithm must return the matching. Recently, Esfandiari et al. (SODA 2015) showed that it was possible to estimate the maximum cardinality of a matching in a planar graph up to a factor of 24+epsilon using O(epsilon^{-2} n^{2/3} polylog n) space. We first present an algorithm (with a simple analysis) that improves this to a factor 5+epsilon using the same space. We also improve upon the previous results for other graphs with bounded arboricity. We then present a factor 12.5 approximation for matching in planar graphs that can be implemented using O(log n) space in the adjacency list data stream model where the stream is a concatenation of the adjacency lists of the graph. The main idea behind our results is finding "local" fractional matchings, i.e., fractional matchings where the value of any edge e is solely determined by the edges sharing an endpoint with e. Our work also improves upon the results for the dynamic data stream model where the stream consists of a sequence of edges being inserted and deleted from the graph. We also extend our results to weighted graphs, improving over the bounds given by Bury and Schwiegelshohn (ESA 2015), via a reduction to the unweighted problem that increases the approximation by at most a factor of two