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The rate of convergence of Nesterov's accelerated forward-backward method is actually $o(k^{-2})$

H Attouch (hedy.attouch***at***univ-montp2.fr)
J Peypouquet (juan.peypouquet***at***usm.cl)

Abstract: The {\it forward-backward algorithm} is a powerful tool for solving optimization problems with a {\it additively separable} and {\it smooth} + {\it nonsmooth} structure. In the convex setting, a simple but ingenious acceleration scheme developed by Nesterov has been proved useful to improve the theoretical rate of convergence for the function values from the standard $\mathcal O(k^{-1})$ down to $\mathcal O(k^{-2})$. In this short paper, we prove that the rate of convergence of a slight variant of Nesterov's accelerated forward-backward method, which produces {\it convergent} sequences, is actually $o(k^{-2})$, rather than $\mathcal O(k^{-2})$. Our arguments rely on the connection between this algorithm and a second-order differential inclusion with vanishing damping.

Keywords: Convex optimization, fast convergent methods, Nesterov method

Category 1: Convex and Nonsmooth Optimization (Convex Optimization )


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Entry Submitted: 10/29/2015
Entry Accepted: 10/29/2015
Entry Last Modified: 11/01/2015

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