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math.FA — Functional Analysis

The pp-Bures–Wasserstein distance via 3×33\times3 block matrices (with application to pp-Bures–Wasserstein barycenters)

Contributed by Suvrit Sra

We study the pp-Bures–Wasserstein distance Dp(A,B)=(tr Ap+tr Bp−2∥A1/2B1/2∥pp)1/2D_p(A,B)=(\mathrm{tr}\,A^p+\mathrm{tr}\,B^p-2\|A^{1/2}B^{1/2}\|_p^p)^{1/2} on positive semidefinite matrices; it equals the Bures–Wasserstein distance for p=1p=1, and the Hilbert–Schmidt distance for p=2p=2. We prove that DpD_p is a metric for every 1≤p≤21\le p\le2, by proving a stronger 2014 conjecture of Lin and van den Driessche on 3×33\times3 block matrices. Our main tool is a partition inequality for Schatten norms, proved by complex interpolation. We also characterize Fréchet means for DpD_p by a fixed-point equation and prove geometric convergence of the associated iteration.

A mix of human-written and AI-generated textHuman understanding: all partsBures-Wasserstein distanceLin-van den Driessche conjectureSchatten normbarycenterscomplex interpolationpartition inequalitypositive semidefinite block matrices

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