An inertial parallel algorithm for a finite family of $ G $-nonexpansive mappings applied to signal recovery - Jun-on, Nipa; Suparatulatorn, Raweerote; Gamal, Mohamed; Cholamjiak, Watcharaporn (AIMS mathematics, 2022)
This study investigates the weak convergence of the sequences generated by the inertial technique combining the parallel monotone hybrid method for finding a common fixed point of a finite family of $ G $-nonexpansive mappings under suitable conditions in Hilbert spaces endowed with graphs. Some numerical examples are also presented, providing applications to signal recovery under situations without knowing the type of noises. Besides, numerical experiments of the proposed algorithms, defined by different types of blurred matrices and noises on the algorithm, are able to show the efficiency and the implementation for LASSO problem in signal recovery.