Tuesday, August 18, 2015

Low-Rank Spectral Optimization - implementation -

Michael just let me know of his recent work and attendant implementation. Thanks Michael !.

Various applications in signal processing and machine learning give rise to highly structured spectral optimization problems characterized by low-rank solutions. Two important examples that motivate this work are optimization problems from phase retrieval and from blind deconvolution, which are designed to yield rank-1 solutions. An algorithm is described based on solving a certain constrained eigenvalue optimization problem that corresponds to the gauge dual. Numerical examples on a range of problems illustrate the e ffectiveness of the approach.
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