On The Behavior of Subgradient Projections Methods for Convex Feasibility Problems in Euclidean Spaces

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