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DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization
Rixon Crane · Fred Roosta

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #196

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.

Author Information

Rixon Crane (The University of Queensland)
Fred Roosta (University of Queensland)

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