2019
DOI: 10.48550/arxiv.1912.01593
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Tight Coefficients of Averaged Operators via Scaled Relative Graph

Abstract: Many iterative methods in optimization are fixed-point iterations with averaged operators. As such methods converge at an O(1/k) rate with the constant determined by the averagedness coefficient, establishing small averagedness coefficients for operators is of broad interest. In this paper, we show that the averagedness coefficients of the composition of averaged operators by Ogura

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Cited by 3 publications
(3 citation statements)
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“…Prior work [12,9] focused on the SRG of nonlinear multi-valued operators. For linear operators, Ryu, Hannah, and Yin [12] established G(A) includes Λ(A), as stated in Fact 1, but did not characterize when and how G(A) enlarges Λ(A).…”
Section: Contributionsmentioning
confidence: 99%
“…Prior work [12,9] focused on the SRG of nonlinear multi-valued operators. For linear operators, Ryu, Hannah, and Yin [12] established G(A) includes Λ(A), as stated in Fact 1, but did not characterize when and how G(A) enlarges Λ(A).…”
Section: Contributionsmentioning
confidence: 99%
“…This paper proposes the Scaled Relative Graph (SRG) as a tool for quantifying the errors introduced by an approximation. The SRG has recently been introduced in the theory of convex optimization [19], and allows simple, graphical proofs of algorithm convergence, and the derivation of tight convergence bounds [20]. The SRG gives a graphical representation of the incremental behavior of a nonlinear operator, and generalizes the Nyquist diagram of an LTI transfer function [21].…”
Section: Introductionmentioning
confidence: 99%
“…This tool allows simple, intuitive and rigorous proofs of the convergence of many algorithms in convex optimization. Furthermore, the graphical method is particularly suitable for proving tightness of convergence bounds, with several novel tightness results being proved [1], [2].…”
Section: Introductionmentioning
confidence: 99%