DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization

Published in Advances in Neural Information Processing Systems 32 (NIPS 2019), 2019


For optimization of a large sum of functions in a distributed computing environment, we present a novel communication efficient Newton-type algorithm that enjoys a variety of advantages over similar existing methods. Our algorithm, DINGO, is derived by optimization of the gradient’s norm as a surrogate function. DINGO does not impose any specific form on the underlying functions and its application range extends far beyond convexity and smoothness. The underlying sub-problems of DINGO are simple linear least-squares, for which a plethora of efficient algorithms exist. DINGO involves a few hyper-parameters that are easy to tune and we theoretically show that a strict reduction in the surrogate objective is guaranteed, regardless of the selected hyper-parameters.

Paper Poster Code arXiv