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arxiv: 1808.05550 · v2 · pith:AKCOKL6Mnew · submitted 2018-08-10 · 🧮 math.ST · cs.IT· math.IT· math.PR· stat.TH

A generalized Lieb's theorem and its applications to spectrum estimates for a sum of random matrices

classification 🧮 math.ST cs.ITmath.ITmath.PRstat.TH
keywords matricesconcavityeigenvaluesestimatesfunctionsrandomtexttrace
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In this paper we prove the concavity of the $k$-trace functions, $A\mapsto (\text{Tr}_k[\exp(H+\ln A)])^{1/k}$, on the convex cone of all positive definite matrices. $\text{Tr}_k[A]$ denotes the $k_{\mathrm{th}}$ elementary symmetric polynomial of the eigenvalues of $A$. As an application, we use the concavity of these $k$-trace functions to derive tail bounds and expectation estimates on the sum of the $k$ largest (or smallest) eigenvalues of a sum of random matrices.

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