Spectral methods are widely used in nonconvex optimization approaches to signal estimation. Examples include phase retrieval, blind deconvolution, and low-rank matrix/tensor recovery. In this talk, I will present our work on precise asymptotic characterizations of the performance of the spectral methods and on their connections to the landscapes of high-dimensional inference problems.
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