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A Full Adagrad algorithm with O(Nd) operations
May 6, 2024, 4:46 a.m. | Antoine Godichon-Baggioni (LPSM), Wei Lu (LMI), Bruno Portier (LMI)
stat.ML updates on arXiv.org arxiv.org
Abstract: A novel approach is given to overcome the computational challenges of the full-matrix Adaptive Gradient algorithm (Full AdaGrad) in stochastic optimization. By developing a recursive method that estimates the inverse of the square root of the covariance of the gradient, alongside a streaming variant for parameter updates, the study offers efficient and practical algorithms for large-scale applications. This innovative strategy significantly reduces the complexity and resource demands typically associated with full-matrix methods, enabling more effective …
abstract algorithm arxiv challenges computational covariance gradient math.st matrix novel operations optimization recursive square stat.ml stat.th stochastic streaming type updates
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