Academic Journal

LOWER BOUNDS FOR LOCAL MONOTONICITY RECONSTRUCTION FROM TRANSITIVE-CLOSURE SPANNERS.

Bibliographic Details
Title: LOWER BOUNDS FOR LOCAL MONOTONICITY RECONSTRUCTION FROM TRANSITIVE-CLOSURE SPANNERS.
Authors: Bhattacharyya, Arnab, Grigorescu, Elena, Jha, Madhav, Jungh, Kyomin, Raskhodnikova, Sofya, Woodruff, David P.
Source: SIAM Journal on Discrete Mathematics; 2012, Vol. 26 Issue 2, p618-646, 29p, 8 Diagrams, 2 Charts
Subject Terms: Mathematical functions, Hypercubes, Directed graphs, Integers, Data structures, Algorithms
Abstract: Given a directed graph G = (V, E) and an integer k ≥ 1, a k-transitive-closure-spanner (k- TC-spanner) of G is a directed graph H = (V, EH that has (1) the same transitive-closure as G and (2) diameter at most k. Transitive-closure spanners are used in access control, property testing and data structures. We show a connection between 2-TC-spanners and local monotonicity filters. A local monotonicity filter, introduced by Saks and Seshadhri [SIAM J. Comput., pp. 2897--2926], is a randomized algorithm that, given access to an oracle for an almost monotone function ƒ: {1,2,…,m}d → ℝ, can quickly evaluate a related function g: {1,2,…,m}d → ℝ which is guaranteed to be monotone. Furthermore, the filter can be implemented in a distributed manner. We show that an efficient local monotonicity filter implies a sparse 2-TC-spanner of the directed hypergrid, providing a new technique for proving lower bounds for local monotonicity filters. Our connection is, in fact, more general: an efficient local monotonicity filter for functions on any partially ordered set (poset) implies a sparse 2-TC-spanner of the directed acyclic graph corresponding to the poset. We present nearly tight upper and lower bounds on the size of the sparsest 2-TC-spanners of the directed hypercube and hypergrid. These bounds imply stronger lower bounds for local monotonicity filters that nearly match the upper bounds of Saks and Seshadhri. [ABSTRACT FROM AUTHOR]
Copyright of SIAM Journal on Discrete Mathematics is the property of Society for Industrial & Applied Mathematics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
ISSN:08954801
DOI:10.1137/100808186