mlsanityData to 14 Oct 2026

Symmetrizing Bregman Divergence on the Cone of Positive Definite Matrices: Which Mean to Use and Why

arXiv 2603.28917 ↗First posted 30 Mar 2026math.OC cs.LG cs.SYcited by 0

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Abstract (arXiv)

This work uncovers variational principles behind symmetrizing the Bregman divergences induced by generic mirror maps over the cone of positive definite matrices. We show that computing the canonical means for this symmetrization can be posed as minimizing the desired symmetrized divergences over a set of mean functionals defined axiomatically to satisfy certain properties. For the forward symmetrization, we prove that the arithmetic mean over the primal space is canonical for any mirror map over the positive definite cone. For the reverse symmetrization, we show that the canonical mean is the arithmetic mean over the dual space, pulled back to the primal space. Applying this result to three common mirror maps used in practice, we show that the canonical means for reverse symmetrization, in those cases, turn out to be the arithmetic, log-Euclidean and harmonic means. Our results improve understanding of existing symmetrization practices in the literature, and can be seen as a navigational chart to help decide which mean to use when.

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References · 8

  1. 2014Convex Optimization: Algorithms and Complexitycited by 575
  2. 2019On a generalization of the Jensen-Shannon divergence and the JS-symmetrization of distances relying on abstract meanscited by 61
  3. 2013Bregman Alternating Direction Method of Multiplierscited by 19
  4. 2014Bregman Divergences for Infinite Dimensional Covariance Matricescited by 12
  5. 2017Gradient Flows in Filtering and Fisher-Rao Geometrycited by 12
  6. 2019Hopfield Neural Network Flow: A Geometric Viewpointcited by 5
  7. 2018DeGroot-Friedkin Map in Opinion Dynamics is Mirror Descentcited by 1
  8. 2025Beyond Quadratic Costs in LQR: Bregman Divergence Controlcited by 1

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