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Backtracking strategies for accelerated descent methods with smooth composite objectives

Abstract : We present and analyse a backtracking strategy for a general Fast Iterative Shrink-age/Thresholding Algorithm which has been recently proposed in [11] for strongly convex objective functions. Differently from classical Armijo-type line searching, our backtracking rule allows for local increase and decrease of the Lipschitz constant estimate along the iterations. For such strategy accelerated convergence rates are proved and numerical results are shown for some exemplar imaging problems.
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Submitted on : Wednesday, September 27, 2017 - 11:28:05 AM
Last modification on : Sunday, November 28, 2021 - 4:38:03 PM
Long-term archiving on: : Thursday, December 28, 2017 - 1:15:36 PM

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Luca Calatroni, Antonin Chambolle. Backtracking strategies for accelerated descent methods with smooth composite objectives. SIAM Journal on Optimization, Society for Industrial and Applied Mathematics, 2019, 29 (3), pp.1772--1798. ⟨hal-01596103⟩

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