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arXiv · 2106.08502

Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent

Jason M. Altschuler·Sinho Chewi·Patrik Gerber·Austin J. Stromme
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Abstract

We study first-order optimization algorithms for computing the barycenter of Gaussian distributions with respect to the optimal transport metric. Although the objective is geodesically non-convex, Riemannian GD empirically converges rapidly, in fact faster than off-the-shelf methods such as Euclidean GD and SDP solvers. This stands in stark contrast to the best-known theoretical results for Riemannian GD, which depend exponentially on the dimension. In this work, we prove new geodesic convexity results which provide stronger control of the iterates, yielding a dimension-free convergence rate. Our techniques also enable the analysis of two related notions of averaging, the entropically-regularized barycenter and the geometric median, providing the first convergence guarantees for Riemannian GD for these problems.

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Jason M. Altschuler, Sinho Chewi, Patrik Gerber, Austin J. Stromme. 2023-10-31. Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent. https://arxiv.org/abs/2106.08502

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