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DINO: Distributed Newton-Type Optimization Method
Rixon Crane · Fred Roosta

Wed Jul 15 03:00 PM -- 03:45 PM & Thu Jul 16 04:00 AM -- 04:45 AM (PDT) @ None #None

We present a novel communication-efficient Newton-type algorithm for finite-sum optimization over a distributed computing environment. Our method, named DINO, overcomes both theoretical and practical shortcomings of similar existing methods. Under minimal assumptions, we guarantee global sub-linear convergence of DINO to a first-order stationary point for general non-convex functions and arbitrary data distribution over the network. Furthermore, for functions satisfying Polyak-Lojasiewicz (PL) inequality, we show that DINO enjoys a linear convergence rate. Our proposed algorithm is practically parameter free, in that it will converge regardless of the selected hyper-parameters, which are easy to tune. Additionally, its sub-problems are simple linear least-squares, for which efficient solvers exist, and numerical simulations demonstrate the efficiency of DINO as compared with similar alternatives.

Author Information

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

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