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A. Afham

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#machine learning Preprint Sep 2026

Projected Riemannian Gradient Descent for the Bures-Wasserstein Barycenter: Dimension-Independent Linear Convergence at Unit Step Size

This work proposes a Projected RGD algorithm that achieves dimension-independent linear convergence at unit step size and identifies as unit-step RGD on a totally geodesic submanifold, thereby extending the dimension-independent guarantee to that setting verbatim.

A. Afham · 0 citations