𝓁_p-Spread and Restricted Isometry Properties of Sparse Random Matrices

Authors Venkatesan Guruswami, Peter Manohar, Jonathan Mosheiff

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Venkatesan Guruswami
  • University of California, Berkeley, CA, USA
Peter Manohar
  • Carnegie Mellon University, Pittsburgh, PA, USA
Jonathan Mosheiff
  • Carnegie Mellon University, Pittsburgh, PA, USA


We thank Sidhanth Mohanty for helpful discussions about the works of [Brito et al., 2018; Bordenave, 2019; Bordenave and Collins, 2019; Sidhanth Mohanty et al., 2020; Sidhanth Mohanty et al., 2020; Ryan O'Donnell and Xinyu Wu, 2020], and Yuval Peled for helpful discussions about convergence theorems for graph spectra. We thank Amir Shpilka for bringing the work of [Zohar Shay Karnin, 2011] to our attention, and Ioana Dumitriu for bringing the works of [Brito et al., 2018; Yizhe Zhu, 2020] to our attention.

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Venkatesan Guruswami, Peter Manohar, and Jonathan Mosheiff. 𝓁_p-Spread and Restricted Isometry Properties of Sparse Random Matrices. In 37th Computational Complexity Conference (CCC 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 234, pp. 7:1-7:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


Random subspaces X of ℝⁿ of dimension proportional to n are, with high probability, well-spread with respect to the 𝓁₂-norm. Namely, every nonzero x ∈ X is "robustly non-sparse" in the following sense: x is ε ‖x‖₂-far in 𝓁₂-distance from all δ n-sparse vectors, for positive constants ε, δ bounded away from 0. This "𝓁₂-spread" property is the natural counterpart, for subspaces over the reals, of the minimum distance of linear codes over finite fields, and corresponds to X being a Euclidean section of the 𝓁₁ unit ball. Explicit 𝓁₂-spread subspaces of dimension Ω(n), however, are unknown, and the best known explicit constructions (which achieve weaker spread properties), are analogs of low density parity check (LDPC) codes over the reals, i.e., they are kernels of certain sparse matrices. Motivated by this, we study the spread properties of the kernels of sparse random matrices. We prove that with high probability such subspaces contain vectors x that are o(1)⋅‖x‖₂-close to o(n)-sparse with respect to the 𝓁₂-norm, and in particular are not 𝓁₂-spread. This is strikingly different from the case of random LDPC codes, whose distance is asymptotically almost as good as that of (dense) random linear codes. On the other hand, for p < 2 we prove that such subspaces are 𝓁_p-spread with high probability. The spread property of sparse random matrices thus exhibits a threshold behavior at p = 2. Our proof for p < 2 moreover shows that a random sparse matrix has the stronger restricted isometry property (RIP) with respect to the 𝓁_p norm, and in fact this follows solely from the unique expansion of a random biregular graph, yielding a somewhat unexpected generalization of a similar result for the 𝓁₁ norm [Berinde et al., 2008]. Instantiating this with suitable explicit expanders, we obtain the first explicit constructions of 𝓁_p-RIP matrices for 1 ≤ p < p₀, where 1 < p₀ < 2 is an absolute constant.

Subject Classification

ACM Subject Classification
  • Theory of computation → Pseudorandomness and derandomization
  • Theory of computation → Random projections and metric embeddings
  • Spread Subspaces
  • Euclidean Sections
  • Restricted Isometry Property
  • Sparse Matrices


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