NS-RGS uses Newton-Schulz iterations to avoid costly matrix decompositions in Riemannian optimization for orthogonal group synchronization, proving linear convergence with spectral initialization and showing 2x practical speedup.
Local geometry determines global landscape in low-rank factorization for synchronization
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NS-RGS: Newton-Schulz based Riemannian gradient method for orthogonal group synchronization
NS-RGS uses Newton-Schulz iterations to avoid costly matrix decompositions in Riemannian optimization for orthogonal group synchronization, proving linear convergence with spectral initialization and showing 2x practical speedup.