Modular-proximal gradient algorithms in variable exponent Lebesgue spaces
Marta Lazzaretti, Luca Calatroni, Claudio Estatico

TL;DR
This paper develops and analyzes proximal gradient algorithms tailored for optimization problems in variable exponent Lebesgue spaces, enabling efficient solutions for ill-posed inverse problems with space-variant properties.
Contribution
It introduces primal and dual proximal gradient algorithms in $L_{p(ullet)}(\,Omega)$ spaces using modular functions for efficient computation, with convergence proofs and numerical validation.
Findings
Algorithms converge in function values with problem-dependent rates.
Numerical tests demonstrate flexibility in deconvolution and noise removal.
Proposed methods outperform standard $L_p$ algorithms in convergence speed and costs.
Abstract
We consider structured optimisation problems defined in terms of the sum of a smooth and convex function, and a proper, l.s.c., convex (typically non-smooth) one in reflexive variable exponent Lebesgue spaces . Due to their intrinsic space-variant properties, such spaces can be naturally used as solution space and combined with space-variant functionals for the solution of ill-posed inverse problems. For this purpose, we propose and analyse two instances (primal and dual) of proximal gradient algorithms in , where the proximal step, rather than depending on the natural (non-separable) norm, is defined in terms of its modular function, which, thanks to its separability, allows for the efficient computation of algorithmic iterates. Convergence in function values is proved for both algorithms, with convergence rates…
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