A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization

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Frank Curtis ;
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:632-641, 2016.

Abstract

An algorithm for stochastic (convex or nonconvex) optimization is presented. The algorithm is variable-metric in the sense that, in each iteration, the step is computed through the product of a symmetric positive definite scaling matrix and a stochastic (mini-batch) gradient of the objective function, where the sequence of scaling matrices is updated dynamically by the algorithm. A key feature of the algorithm is that it does not overly restrict the manner in which the scaling matrices are updated. Rather, the algorithm exploits fundamental self-correcting properties of BFGS-type updating—properties that have been over-looked in other attempts to devise quasi-Newton methods for stochastic optimization. Numerical experiments illustrate that the method and a limited memory variant of it are stable and outperform (mini-batch) stochastic gradient and other quasi-Newton methods when employed to solve a few machine learning problems.

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