On the behavior of subgradient projections methods for convex feasibility problems in euclidean spaces

We study some methods of subgradient projections for solving a convex feasibility problem with general (not necessarily hyperplanes or half-spaces) convex sets in the inconsistent case and propose a strategy that controls the relaxation parameters in a specific self-adapting manner. This strategy leaves enough user flexibility but gives a mathematical guarantee for the algorithm's behavior in the inconsistent case. We present the numerical results of computational experiments that illustrate the computational advantage of the new method.