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Random Structures and Algorithms
Article . 2016 . Peer-reviewed
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A combinatorial characterization of smooth LTCs and applications

Authors: Eli Ben-Sasson; Michael Viderman;

A combinatorial characterization of smooth LTCs and applications

Abstract

Summary: The study of locally testable codes (LTCs) has benefited from a number of nontrivial constructions discovered in recent years. Yet, we still lack a good understanding of what makes a linear error correcting code locally testable and as a result we do not know what is the rate-limit of LTCs and whether asymptotically good linear LTCs with constant query complexity exist. In this paper, we provide a combinatorial characterization of smooth locally testable codes, which are locally testable codes whose associated tester queries every bit of the tested word with equal probability. Our main contribution is a combinatorial property defined on the Tanner graph associated with the code tester (``well-structured tester''). We show that a family of codes is smoothly locally testable if and only if it has a well-structured tester. As a case study we show that the standard tester for the Hadamard code is ``well-structured,'' giving an alternative proof of the local testability of the Hadamard code, originally proved by \textit{M. Blum} et al. [J. Comput. Syst. Sci. 47, No. 3, 549--595 (1993; Zbl 0795.68131)] (STOC 1990). Additional connections to the works of \textit{E. Ben-Sasson} et al. [SIAM J. Comput. 35, No. 1, 1--21 (2005; Zbl 1086.68045)] (SICOMP 2005) and of \textit{O. Lachish} et al. [Comput. Complexity 17, No. 1, 70--93 (2008; Zbl 1149.68032)] are also discussed.

Keywords

linearity testing, locally testable codes, Linear codes (general theory)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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