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On the Efficient Implementation of the Matrix Exponentiated Gradient Algorithm for Low-Rank Matrix Optimization. (arXiv:2012.10469v2 [math.OC] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Convex optimization over the spectrahedron, i.e., the set of all real
$n\times n$ positive semidefinite matrices with unit trace, has important
applications in machine learning, signal processing and statistics, mainly as a
convex relaxation for optimization problems with low-rank matrices. It is also
one of the most prominent examples in the theory of first-order methods for
convex optimization in which non-Euclidean methods can be significantly
preferable to their Euclidean counterparts. In particular, the desirable choice
is the Matrix Exponentiated Gradient …
algorithm arxiv gradient implementation low math matrix optimization the matrix