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Recursive Gradient Retraction Methods for Stochastic Nonconvex Optimization on Stiefel Manifolds
Nonconvex optimization on Stiefel manifolds underlies many problems in machine learning and deep learning. However, existing stochastic Riemannian retraction methods often rely on stepsizes that require prior knowledge of the gradient Lipschitz constant, a prescribed iteration horizon, or restrictiv
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