Under mild sparsity and overlap assumptions, almost every proportional-size column subset of a sparse matrix has smallest singular value o(1), so constant-sparsity SparseStack maps are not (Ω(k), Ω(1))-OSI.
Comparison theorems for the extreme eigenvalues of a random symmetric matrix
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abstract
This paper establishes a comparison theorem for the maximum eigenvalue of a sum of independent random symmetric matrices. The theorem states that the maximum eigenvalue of the matrix sum is dominated by the maximum eigenvalue of a Gaussian random matrix whose statistics match the sum, and it strengthens previous results of this type. Corollaries address the minimum eigenvalue and the spectral norm; the proof strategy also extends to matrix martingale sequences. The comparison methodology is powerful because of the vast arsenal of tools for treating Gaussian random matrices. As applications, the paper improves on existing eigenvalue bounds for random matrices arising in spectral graph theory, quantum information theory, high-dimensional statistics, and numerical linear algebra. In particular, these techniques deliver the first complete proof that a sparse random dimension reduction map has the injectivity properties conjectured by Nelson & Nguyen in 2013.
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Well-invertible column subsets of sparse matrices are rare
Under mild sparsity and overlap assumptions, almost every proportional-size column subset of a sparse matrix has smallest singular value o(1), so constant-sparsity SparseStack maps are not (Ω(k), Ω(1))-OSI.