Non-asymptotic approach in random matrix theory

Friday, November 7, 2014 - 4:00pm
MSB 111
Mark Rudelson (University of Michigan)

Random matrix theory studies the asymptotics of the spectral distributions of families of random matrices, as the sizes of the matrices tend to infinity. Derivation such asymptotics frequently requires analyzing the spectral properties of random matrices of a large fixed size, especially of their singular values. We will discuss several recent results in this area concerning matrices with independent entries, as well as random unitary and orthogonal perturbations of a fixed matrix. We will also show an application of the non-asymptotic random matrix theory to estimating the number of perfect matchings in a deterministic graph. The talk is intended for a broad mathematical audience, and no previous knowledge of the random matrix theory is assumed.

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