title: The Case for Full-Matrix Adaptive Regularization
abstract: Adaptive regularization methods pre-multiply a descent direction by a preconditioning matrix. Due to the large number of parameters of machine learning problems, full-matrix preconditioning methods are prohibitively expensive. We show how to modify full-matrix adaptive regularization in order to make it practical and effective. We also provide novel theoretical analysis
  for adaptive regularization in non-convex optimization settings. The core of our algorithm, termed GGT, consists of efficient inverse computation of square roots of low-rank matrices. Our preliminary experiments underscore improved convergence rate of GGT across a variety of synthetic tasks and standard deep learning benchmarks.
authors: Naman Agarwal Brian Bullins Xinyi Chen Elad Hazan Karan Singh Cyril Zhang Yi Zhang
hIndex: Elad Hazan 56