Subgradient projection algorithms for convex feasibility problems in the presence of computational errors

In the present paper we study convergence of subgradient projection algorithms for solving convex feasibility problems in a Hilbert space. Our goal is to obtain an approximate solution of the problem in the presence of computational errors. We show that our subgradient projection algorithm generates a good approximate solution, if the sequence of computational errors is bounded from above by a constant.