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Volume 2, Issue 1, 30 April 2020, Pages 1-17
Abstract. We present a parallel computing proximal method for solving the problem of minimizing the sum of convex functions over the intersection of fixed point sets of quasi-nonexpansive mappings in a real Hilbert space. We also provide a convergence analysis of the method for constant and diminishing step sizes under certain assumptions as well as a convergence-rate analysis for a diminishing step size. Numerical comparisons show that the performance of the algorithm is comparable with existing subgradient methods.
How to Cite this Article:
Kengo Shimizu, Kazuhiro Hishinuma, Hideaki Iiduka, Parallel computing proximal method for nonsmooth convex optimization with fixed point constraints of quasi-nonexpansive mappings, Appl. Set-Valued Anal. Optim. 2 (2020), 1-17.