For input @math , let @math denote the set of outputs that are the "legal" answers for a computational problem @math . Suppose @math and members of @math are so large that there is not time to read them in their entirety. We propose a model of local computation algorithms which for a given input @math , support queries by a user to values of specified locations @math in a legal output @math . When more than one legal output @math exists for a given @math , the local computation algorithm should output in a way that is consistent with at least one such @math . Local computation algorithms are intended to distill the common features of several concepts that have appeared in various algorithmic subfields, including local distributed computation, local algorithms, locally decodable codes, and local reconstruction. We develop a technique, based on known constructions of small sample spaces of @math -wise independent random variables and Beck's analysis in his algorithmic approach to the Lov ' a sz Local Lemma, which under certain conditions can be applied to construct local computation algorithms that run in polylogarithmic time and space. We apply this technique to maximal independent set computations, scheduling radio network broadcasts, hypergraph coloring and satisfying @math -SAT formulas.
Though most of the previous examples are for sparse graphs or other problems which have some sort of sparsity, local computation algorithms have also been provided for problems on dense graphs. The property testing algorithms of @cite_36 use a small sample of the vertices (a type of a core-set) to define a good graph coloring or partition of a dense graph. This approach yields local computation algorithms for finding a large partition of the graph and a coloring of the vertices which has relatively few edge violations.
@cite_36
2253610808 1970630090 2046654297 2294368603
We provide a thorough study of distributed property testing – producing algorithms for the approximation problems of property testing in the CONGEST model. In particular, for the so-called dense graph testing model we emulate sequential tests for nearly all graph properties having 1-sided tests, while in the general and sparse models we obtain faster tests for triangle-freeness, cycle-freeness and bipartiteness, respectively. In addition, we show a logarithmic lower bound for testing bipartiteness and cycle-freeness, which holds even in the LOCAL model. In this paper, we consider the question of determining whether a function f has property P or is e-far from any function with property P. A property testing algorithm is given a sample of the value of f on instances drawn according to some distribution. In some cases, it is also allowed to query f on instances of its choice. We study this question for different properties and establish some connections to problems in learning theory and approximation. In particular, we focus our attention on testing graph properties. Given access to a graph G in the form of being able to query whether an edge exists or not between a pair of vertices, we devise algorithms to test whether the underlying graph has properties such as being bipartite, k -Colorable, or having a p -Clique (clique of density p with respect to the vertex set). Our graph property testing algorithms are probabilistic and make assertions that are correct with high probability, while making a number of queries that is independent of the size of the graph. Moreover, the property testing algorithms can be used to efficiently (i.e., in time linear in the number of vertices) construct partitions of the graph that correspond to the property being tested, if it holds for the input graph. We consider distribution-free property-testing of graph connectivity. In this setting of property testing, the distance between functions is measured with respect to a fixed but unknown distribution D on the domain, and the testing algorithm has an oracle access to random sampling from the domain according to this distribution D. This notion of distribution-free testing was previously defined, and testers were shown for very few properties. However, no distribution-free property testing algorithm was known for any graph property. We present the first distribution-free testing algorithms for one of the central properties in this area—graph connectivity (specifically, the problem is mainly interesting in the case of sparse graphs). We introduce three testing models for sparse graphs: A model for bounded-degree graphs, A model for graphs with a bound on the total number of edges (both models were already considered in the context of uniform distribution testing), and A model which is a combination of the two previous testing models; i.e., bounded-degree graphs with a bound on the total number of edges. We prove that connectivity can be tested in each of these testing models, in a distribution-free manner, using a number of queries that is independent of the size of the graph. This is done by providing a new analysis to previously known connectivity testers (from “standard”, uniform distribution property-testing) and by introducing some new testers. Graph coloring is a central problem in distributed computing. Both vertex- and edge-coloring problems have been extensively studied in this context. In this paper we show that a (2Δ − 1)-edge-coloring can be computed in time smaller than loge n for any e > 0, specifically, in eO([EQUATION]log log n) rounds. This establishes a separation between the (2Δ − 1)-edge-coloring and Maximal Matching problems, as the latter is known to require Ω([EQUATION]log n) time [15]. No such separation is currently known between the (Δ + 1)-vertex-coloring and Maximal Independent Set problems. We devise a (1 + e)Δ-edge-coloring algorithm for an arbitrarily small constant e > 0. This result applies whenever Δ ≥ Δe, for some constant Δe which depends on e. The running time of this algorithm is O(log* Δ + [EQUATION]). A much earlier logarithmic-time algorithm by Dubhashi, Grable and Panconesi [11] assumed Δ ≥ (log n)1+Ω(1). For Δ = (log n)1+Ω(1) the running time of our algorithm is only O(log* n). This constitutes a drastic improvement of the previous logarithmic bound [11, 9]. Our results for (2Δ − 1)-edge-coloring also follows from our more general results concerning (1 − e)-locally sparse graphs. Specifically, we devise a (Δ + 1)-vertex coloring algorithm for (1 − e)-locally sparse graphs that runs in O(log* Δ + log(1 e)) rounds for any e > 0, provided that eΔ = (log n)1+Ω(1). We conclude that the (Δ + 1)-vertex coloring problem for (1 − e)-locally sparse graphs can be solved in O(log(1 e)) + eO([EQUATION]log log n) time. This imply our result about (2Δ − 1)-edge-coloring, because (2Δ − 1)-edge-coloring reduces to (Δ + 1)-vertex-coloring of the line graph of the original graph, and because line graphs are (1 2 + o(1))-locally sparse.