NDLI: Randomized Composable Core-sets for Distributed Submodular Maximization

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Randomized Composable Core-sets for Distributed Submodular Maximization

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Randomized Rounding for the Largest Simplex Problem

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Randomized Composable Core-sets for Distributed Submodular Maximization

Content Provider	ACM Digital Library
Author	Zadimoghaddam, Morteza Mirrokni, Vahab
Abstract	An effective technique for solving optimization problems over massive data sets is to partition the data into smaller pieces, solve the problem on each piece and compute a representative solution from it, and finally obtain a solution inside the union of the representative solutions for all pieces. This technique can be captured via the concept of composable core-sets, and has been recently applied to solve diversity maximization problems as well as several clustering problems [7,15,8]. However, for coverage and submodular maximization problems, impossibility bounds are known for this technique [15]. In this paper, we focus on efficient construction of a randomized variant of composable core-sets where the above idea is applied on a random clustering of the data. We employ this technique for the coverage, monotone and non-monotone submodular maximization problems. Our results significantly improve upon the hardness results for non-randomized core-sets, and imply improved results for submodular maximization in a distributed and streaming settings. The effectiveness of this technique has been confirmed empirically for several machine learning applications [22], and our proof provides a theoretical foundation to this idea. In summary, we show that a simple greedy algorithm results in a 1/3-approximate randomized composable core-set for submodular maximization under a cardinality constraint. Our result also extends to non-monotone submodular functions, and leads to the first 2-round MapReduce-based constant-factor approximation algorithm with O(n) total communication complexity for either monotone or non-monotone functions. Finally, using an improved analysis technique and a new algorithm PseudoGreedy, we present an improved 0.545-approximation algorithm for monotone submodular maximization, which is in turn the first MapReduce-based algorithm beating factor 1/2 in a constant number of rounds.
Starting Page	153
Ending Page	162
Page Count	10
File Format	PDF
ISBN	9781450335362
DOI	10.1145/2746539.2746624
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Randomized composable core-sets Mapreduce algorithms Streaming algorithms core-sets Submodular maximization Distributed algorithms
Content Type	Text
Resource Type	Article

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