## Convergence rate analysis of several splitting schemesDamek Davis and Wotao Yin Published in a Springer book on splitting methods as a chapter This technical report is Part 1 of a two-part series. Part 2 gives faster convergence rates under regularity assumptions.
## OverviewSplitting schemes are a class of powerful algorithms that solve complicated monotone inclusions and convex optimization problems that are built from many simpler pieces. They give rise to algorithms in which the simple pieces of the decomposition are processed individually. This leads to easily implementable and highly parallelizable algorithms, which often obtain nearly state-of-the-art performance. In the ﬁrst part of this paper, we analyze the convergence rates of several general splitting algorithms and provide examples to prove the tightness of our results. The most general rates are proved for the ﬁxed-point residual (FPR) of the Krasnosel’skii-Mann (KM) iteration of nonexpansive operators, where we improve the known big-O rate to little-o. We show the tightness of this result and improve it in several special cases. In the second part of this paper, we use the convergence rates derived for the KM iteration to analyze the objective error convergence rates for the Douglas-Rachford (DRS), Peaceman-Rachford (PRS), and ADMM splitting algorithms under general convexity assumptions. We show, by way of example, that the rates obtained for these algorithms are tight in all cases and obtain the surprising statement: The DRS algorithm is nearly as fast as the proximal point algorithm (PPA) in the ergodic sense and nearly as slow as the subgradient method in the nonergodic sense. Finally, we provide several applications of our result to feasibility problems, model ﬁtting, and distributed optimization. Our analysis is self-contained, and most results are deduced from a basic lemma that derives convergence rates for summable sequences, a simple diagram that decomposes each relaxed PRS iteration, and fundamental inequalities that relate the FPR to objective error. ## Citation
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