Given $\mathbf A \in \mathbb{R}^{n \times n}$ with entries bounded in magnitude by $1$, it is well-known that if $S \subset [n] \times [n]$ is a uniformly random subset of $\tilde{O} (n/\epsilon^2)$ entries, and if ${\mathbf A}_S$ equals $\mathbf A$ on the entries in $S$ and is zero elsewhere, then $\|\mathbf A - \frac{n^2}{s} \cdot {\mathbf A}_S\|_2 \le \epsilon n$ with high probability, where $\|\cdot\|_2$ is the spectral norm. We show that for positive semidefinite (PSD) matrices, no randomness is needed at all in this statement. Namely, there exists a fixed subset $S$ of $\tilde{O} (n/\epsilon^2)$ entries that acts as a universal sparsifier: the above error bound holds simultaneously for every bounded entry PSD matrix $\mathbf A \in \mathbb{R}^{n \times n}$. One can view this result as a significant extension of a Ramanujan expander graph, which sparsifies any bounded entry PSD matrix, not just the all ones matrix. We leverage the existence of such universal sparsifiers to give the first deterministic algorithms for several central problems related to singular value computation that run in faster than matrix multiplication time. We also prove universal sparsification bounds for non-PSD matrices, showing that $\tilde{O} (n/\epsilon^4)$ entries suffices to achieve error $\epsilon \cdot \max(n,\|\mathbf A\|_1)$, where $\|\mathbf A\|_1$ is the trace norm. We prove that this is optimal up to an $\tilde{O} (1/\epsilon^2)$ factor. Finally, we give an improved deterministic spectral approximation algorithm for PSD $\mathbf A$ with entries lying in $\{-1,0,1\}$, which we show is nearly information-theoretically optimal.

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