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Low-rank spectral optimization | | Michael P. Friedlander
; Ives Macedo
; | | Date: |
3 Aug 2015 | | 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 small and large problems illustrate the effectiveness
of the approach. | | Source: | arXiv, 1508.0315 | | Services: | Forum | Review | PDF | Favorites | |
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