Convergence Rate Comparison of Proximal Algorithms for Non-Smooth Convex Optimization With an Application to Texture Segmentation

نویسندگان

چکیده

In this paper we provide a theoretical and numerical comparison of convergence rates forward-backward, Douglas-Rachford, Peaceman-Rachford algorithms for minimizing the sum convex proper lower semicontinuous function strongly differentiable with Lipschitz continuous gradient. Our results extend made in [1], when both functions are smooth, to context where only one is assumed differentiable. Optimal step-sizes three compared theoretically numerically texture segmentation problem, obtaining very sharp estimations illustrating high efficiency splitting.

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ژورنال

عنوان ژورنال: IEEE Signal Processing Letters

سال: 2022

ISSN: ['1558-2361', '1070-9908']

DOI: https://doi.org/10.1109/lsp.2022.3179169