Testing Fourier Dimensionality and Sparsity

  • Authors:
  • Parikshit Gopalan;Ryan O'Donnell;Rocco A. Servedio;Amir Shpilka;Karl Wimmer

  • Affiliations:
  • Microsoft Research, Silicon Valley,;Carnegie Mellon University,;Columbia University,;Technion,;Carnegie Mellon University,

  • Venue:
  • ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
  • Year:
  • 2009

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Abstract

We present a range of new results for testing properties of Boolean functions that are defined in terms of the Fourier spectrum. Broadly speaking, our results show that the property of a Boolean function having a concise Fourier representation is locally testable. We first give an efficient algorithm for testing whether the Fourier spectrum of a Boolean function is supported in a low-dimensional subspace of ${\mathbb F}_2^n$ (equivalently, for testing whether f is a junta over a small number of parities). We next give an efficient algorithm for testing whether a Boolean function has a sparse Fourier spectrum (small number of nonzero coefficients). In both cases we also prove lower bounds showing that any testing algorithm -- even an adaptive one -- must have query complexity within a polynomial factor of our algorithms, which are nonadaptive. Finally, we give an "implicit learning" algorithm that lets us test any sub-property of Fourier concision. Our technical contributions include new structural results about sparse Boolean functions and new analysis of the pairwise independent hashing of Fourier coefficients from [12].