Abstract
Distributed optimization algorithms are essential for training machine
learning models on very large-scale datasets. However, they often suffer from
communication bottlenecks. Confronting this issue, a communication-efficient
primal-dual coordinate ascent framework (CoCoA) and its improved variant CoCoA+
have been proposed, achieving a convergence rate of $O(1/t)$ for
solving empirical risk minimization problems with Lipschitz continuous losses.
In this paper, an accelerated variant of CoCoA+ is proposed and shown to
possess a convergence rate of $O(1/t^2)$ in terms of reducing
suboptimality. The analysis of this rate is also notable in that the
convergence rate bounds involve constants that, except in extreme cases, are
significantly reduced compared to those previously provided for CoCoA+. The
results of numerical experiments are provided to show that acceleration can
lead to significant performance gains.
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