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Elliot Paquette
Associate Academic Member
Associate Professor, McGill University, Department of Mathematics and Statistics
We analyze the dynamics of large batch stochastic gradient descent with momentum (SGD+M) on the least squares problem when both the number o… (see more)f samples and dimensions are large. In this setting, we show that the dynamics of SGD+M converge to a deterministic discrete Volterra equation as dimension increases, which we analyze. We identify a stability measurement, the implicit conditioning ratio (ICR), which regulates the ability of SGD+M to accelerate the algorithm. When the batch size exceeds this ICR, SGD+M converges linearly at a rate of
2021-12-31
Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (published)