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We introduce two equations expressing the inverse determinant of a full rank matrix $mathbf{A} in mathbb{R}^{n times n}$ in terms of expectations over matrix-vector products. The first relationship is $|mathrm{det} (mathbf{A})|^{-1} = mathbb{E}_{mathbf{s} sim mathcal{S}^{n-1}}bigl[, Vert mathbf{As}Vert^{-n} bigr]$, where expectations are over vectors drawn uniformly on the surface of an $n$-dimensional radius one hypersphere. The second relationship is $|mathrm{det}(mathbf{A})|^{-1} = mathbb{E}_{mathbf{x} sim q}[,p(mathbf{Ax}) /, q(mathbf{x})]$, where $p$ and $q$ are smooth distributions, and $q$ has full support.
Algorithms involving Gaussian processes or determinantal point processes typically require computing the determinant of a kernel matrix. Frequently, the latter is computed from the Cholesky decomposition, an algorithm of cubic complexity in the size
We study quantum algorithms that learn properties of a matrix using queries that return its action on an input vector. We show that for various problems, including computing the trace, determinant, or rank of a matrix or solving a linear system that
We evaluate the determinant of a matrix whose entries are elliptic hypergeometric terms and whose form is reminiscent of Sylvester matrices. A hypergeometric determinant evaluation of a matrix of this type has appeared in the context of approximation
We gather several results on the eigenvalues of the spatial sign covariance matrix of an elliptical distribution. It is shown that the eigenvalues are a one-to-one function of the eigenvalues of the shape matrix and that they are closer together than
We present a quantum algorithm that verifies a product of two n*n matrices over any field with bounded error in worst-case time n^{5/3} and expected time n^{5/3} / min(w,sqrt(n))^{1/3}, where w is the number of wrong entries. This improves the previo