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Tolerant Linearity Testing and Locally Testable Codes

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5687))

Abstract

We study tolerant linearity testing under general distributions. Given groups G and H, a distribution μ on G, and oracle access to a function f:G → H, we consider the task of approximating the smallest μ-distance of f to a homomorphism h:G → H, where the μ-distance between f and h is the probability that f(x) ≠ h(x) when x is drawn according to the distribution μ. This question is intimately connected to local testability of linear codes.

In this work, we give a general sufficient condition on the distribution μ for linearity to be tolerantly testable with a constant number of queries. Using this condition we show that linearity is tolerantly testable for several natural classes of distributions including low bias, symmetric and product distributions. This gives a new and simple proof of a result of Kaufman and Sudan which shows that sparse, unbiased linear codes over \(\mathbb{Z}_2^n\) are locally testable.

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Kopparty, S., Saraf, S. (2009). Tolerant Linearity Testing and Locally Testable Codes. In: Dinur, I., Jansen, K., Naor, J., Rolim, J. (eds) Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. APPROX RANDOM 2009 2009. Lecture Notes in Computer Science, vol 5687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03685-9_45

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  • DOI: https://doi.org/10.1007/978-3-642-03685-9_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03684-2

  • Online ISBN: 978-3-642-03685-9

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