Author

Bamdev Mishra

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#machine learning Preprint Sep 2026

Optimization over covariance matrices with a parameterized metric

The choice of Riemannian metric can strongly influence the convergence of gradient-based optimization over covariance matrices. Euclidean, Bures-Wasserstein and affine-invariant metrics are common choices, but their relative effectiveness depends on the objective. We introduce a two-parameter family defined by $X^{p}LX...

Yi-Bang Li, Bamdev Mishra, P. Jawanpuria et al. · 0 citations

#machine learning May 2026

Riemannian Optimization for Hadamard Products of Low-Rank Matrices

This work proposes a novel block-diagonal Riemannian metric derived from the pullback of the Frobenius inner product and develops a Riemannian gradient descent algorithm that uses a tuning-free Gaussian step size and scales linearly in the number of observed entries per iteration.

Pratik Jawanpuria, Ankish Chandresh, Bamdev Mishra · arXiv.org · 0 citations