Asymptotic Analysis of Conditioned Stochastic Gradient Descent

Rémi Leluc; François Portier

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arXiv · 2006.02745

Asymptotic Analysis of Conditioned Stochastic Gradient Descent

Rémi Leluc·François Portier

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Abstract

In this paper, we investigate a general class of stochastic gradient descent (SGD) algorithms, called Conditioned SGD, based on a preconditioning of the gradient direction. Using a discrete-time approach with martingale tools, we establish under mild assumptions the weak convergence of the rescaled sequence of iterates for a broad class of conditioning matrices including stochastic first-order and second-order methods. Almost sure convergence results, which may be of independent interest, are also presented. Interestingly, the asymptotic normality result consists in a stochastic equicontinuity property so when the conditioning matrix is an estimate of the inverse Hessian, the algorithm is asymptotically optimal.

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Rémi Leluc, François Portier. 2020-06-04. Asymptotic Analysis of Conditioned Stochastic Gradient Descent. https://arxiv.org/abs/2006.02745

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