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Low-rank spectral optimization

Michael P. Friedlander (mpfriedlander***at***ucdavis.edu)
Ives MacÍdo (ijamj***at***cs.ubc.ca)

Abstract: 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 solving a certain constrained eigenvalue optimization problem that corresponds to the gauge dual. Numerical examples on a range of problems illustrate the effectiveness of the approach.

Keywords:

Category 1: Convex and Nonsmooth Optimization (Convex Optimization )

Citation:

Download: [PDF]

Entry Submitted: 08/05/2015
Entry Accepted: 08/05/2015
Entry Last Modified: 08/12/2015

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