Web: http://arxiv.org/abs/2206.11426

June 24, 2022, 1:10 a.m. | Suvrit Sra, Melanie Weber

cs.LG updates on arXiv.org arxiv.org

We study geodesically convex (g-convex) problems that can be written as a
difference of Euclidean convex functions. This structure arises in several
optimization problems in statistics and machine learning, e.g., for matrix
scaling, M-estimators for covariances, and Brascamp-Lieb inequalities. Our work
offers efficient algorithms that on the one hand exploit g-convexity to ensure
global optimality along with guarantees on iteration complexity. On the other
hand, the split structure permits us to develop Euclidean
Majorization-Minorization algorithms that help us bypass the …

arxiv math on optimization

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