Abstract
We use inexact subgradient projection algorithms for solving convex feasibility problems. We show that almost all iterates, generated by a perturbed subgradient projection algorithm in a Hilbert space, are approximate solutions. Moreover, we obtain an estimate of the number of iterates which are not approximate solutions.
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Zaslavski, A.J. (2018). Convex Feasibility Problems. In: Algorithms for Solving Common Fixed Point Problems. Springer Optimization and Its Applications, vol 132. Springer, Cham. https://doi.org/10.1007/978-3-319-77437-4_8
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DOI: https://doi.org/10.1007/978-3-319-77437-4_8
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