Modular-Proximal Gradient Algorithms in Variable Exponent Lebesgue Spaces

نویسندگان

چکیده

We consider structured optimisation problems defined in terms of the sum a smooth and convex function, proper, l.s.c., (typically non-smooth) one reflexive variable exponent Lebesgue spaces $L_{p(\cdot)}(\Omega)$. Due to their intrinsic space-variant properties, such can be naturally used as solution space combined with functionals for ill-posed inverse problems. For this purpose, we propose analyse two instances (primal dual) proximal gradient algorithms $L_{p(\cdot)}(\Omega)$, where step, rather than depending on natural (non-separable) $L_{p(\cdot)}(\Omega)$ norm, is its modular which, thanks separability, allows efficient computation algorithmic iterates. Convergence function values proved both algorithms, convergence rates problem/space smoothness. To show effectiveness proposed modelling, some numerical tests highlighting flexibility are shown exemplar deconvolution mixed noise removal Finally, verification speed computational costs comparison analogous ones standard $L_{p}(\Omega)$ presented.

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ژورنال

عنوان ژورنال: SIAM Journal on Scientific Computing

سال: 2022

ISSN: ['1095-7197', '1064-8275']

DOI: https://doi.org/10.1137/21m1464336