A Diagonal-Augmented quasi-Newton method with application to factorization machines

Abstract

We present a novel quasi-Newton method for convex optimization, in which the Hessian estimates are based not only on the gradients, but also on the diagonal part of the true Hessian matrix (which can often be obtained with reasonable complexity). The new algorithm is based on the well known Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm and has similar… (More)
DOI: 10.1109/ICASSP.2017.7952641

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