Nov. 9, 2022, 2:12 a.m. | Xilin Li

cs.LG updates on arXiv.org arxiv.org

A matrix free and a low rank approximation preconditioner are proposed to
accelerate the convergence of stochastic gradient descent (SGD) by exploiting
curvature information sampled from Hessian-vector products or finite
differences of parameters and gradients similar to the BFGS algorithm. Both
preconditioners are fitted with an online updating manner minimizing a
criterion that is free of line search and robust to stochastic gradient noise,
and further constrained to be on certain connected Lie groups to preserve their
corresponding symmetry or …

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