Riemannian Optimization for Convex and Non-Convex Signal Processing and Machine Learning Applications
The performance of most algorithms for signal processing and machine learning applications highly depends on the underlying optimization algorithms. Multiple techniques have been proposed for solving convex and non-convex problems such as interior-point methods and semidefinite programming. However,...
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https://thesis.library.caltech.edu/13758/15/Ahmed%20Douik%20Thesis%20Final%20Version.pdfDouik, Ahmed (2020) Riemannian Optimization for Convex and Non-Convex Signal Processing and Machine Learning Applications. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/jt3c-0m30. https://resolver.caltech.edu/CaltechTHESIS:06012020-120425051 <https://resolver.caltech.edu/CaltechTHESIS:06012020-120425051>