A direct-sum theorem for read-once branching programs

Anup Rao, Makrand Sinha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study a direct-sum question for read-once branching programs. If M(f) denotes the minimum average memory required to compute a function f(x1, x2, . . . , xn) how much memory is required to compute f on k independent inputs that arrive in parallel? We show that when the inputs are sampled independently from some domain X and M(f) = Ω(n), then computing the value of f on k streams requires average memory at least Ω k M(f) n . Our results are obtained by defining new ways to measure the information complexity of read-once branching programs. We define two such measures: The transitional and cumulative information content. We prove that any read-once branching program with transitional information content I can be simulated using average memory O(n(I + 1)). On the other hand, if every read-once branching program with cumulative information content I can be simulated with average memory O(I + 1), then computing f on k inputs requires average memory at least (k (M(f) - 1)).

Original languageEnglish (US)
Title of host publicationApproximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques - 19th International Workshop, APPROX 2016 and 20th International Workshop, RANDOM 2016
EditorsKlaus Jansen, Claire Mathieu, Jose D. P. Rolim, Chris Umans
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959770187
DOIs
StatePublished - Sep 1 2016
Externally publishedYes
Event19th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2016 and the 20th International Workshop on Randomization and Computation, RANDOM 2016 - Paris, France
Duration: Sep 7 2016Sep 9 2016

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume60
ISSN (Print)1868-8969

Conference

Conference19th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2016 and the 20th International Workshop on Randomization and Computation, RANDOM 2016
Country/TerritoryFrance
CityParis
Period9/7/169/9/16

Keywords

  • Direct-sum
  • Information complexity
  • Streaming Algorithms

ASJC Scopus subject areas

  • Software

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