Dynamic String-Averaging Subgradient Projection Algorithm

Abstract

In this chapter we study convergence of dynamic string-averaging subgradient projection algorithms for solving convex feasibility problems in a general 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. Moreover, for a known computational error, we find out what an approximate solution can be obtained and how many iterates one needs for this.