摘要: We use subgradient projection algorithms for solving convex feasibility problems. show that almost all iterates, generated by a algorithm in Hilbert space, are approximate solutions. Moreover, we obtain an estimate of the number iterates which not In finite-dimensional case, study behavior presence computational errors. Provided errors bounded, prove our generates good solution after certain iterates.
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