Theory of Computing Systems

, Volume 63, Issue 3, pp 418–449 | Cite as

Property Testing Lower Bounds via a Generalization of Randomized Parity Decision Trees

  • Roei TellEmail author


A few years ago, Blais, Brody, and Matulef: Comput. Complex. 21(2), 311–358 (2012) presented a methodology for proving lower bounds for property testing problems by reducing them from problems in communication complexity. Recently, Bhrushundi, Chakraborty, and Kulkarni (2014) showed that some reductions of this type can be deconstructed to two separate reductions, from communication complexity to randomized parity decision trees and from the latter to property testing. This work follows up on these ideas. We introduce a model called linear-access algorithms, which is a generalization of randomized parity decision trees, and show several methods to reduce communication complexity problems to problems for linear-access algorithms and problems for linear-access algorithms to property testing problems. This approach yields a new interpretation for several well-known reductions, since we present these reductions as a composition of two steps with fundamentally different functionalities. Furthermore, we demonstrate the potential of proving lower bounds on property testing problems by reducing them directly from problems for linear-access algorithms. In particular, we provide an alternative and simple proof for a known lower bound of Ω(k) queries on testing “k-linearity”; that is, the property of k-sparse linear functions over \(\mathbb {F}_{2}\). This alternative proof relies on a theorem by Linial and Samorodnitsky: Combinatorica 22(4), 497–522 (2002). We then extend this result to a new lower bound of Ω(s) queries for testing s-sparse degree-d polynomials over \(\mathbb {F}_{2}\), for any \(d\in \mathbb {N}\). In addition we provide a simple proof for the hardness of testing some families of linear subcodes.


Property testing Communication complexity Parity decision trees Linear-access algorithms Affine subspaces Linear codes 



The author thanks Tom Gur for suggesting the initial observation motivating the study and for several helpful discussions during the research process. The author is grateful to Avishay Tal for pointing him to the work of Linial and Samorodnitsky. The author also thanks his advisor, Oded Goldreich, for his guidance and support in the research and writing process. This research was partially supported by the Israel Science Foundation (grant No. 671/13).


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Authors and Affiliations

  1. 1.Department of Computer ScienceWeizmann Institute of ScienceRehovotIsrael

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