Relaxed Centralized Circumcentered-Reflection Methods for Convex Feasibility: Convergence and Finite Termination
Projection methods are among the most widely used techniques for solving convex feasibility problems due to their simplicity and robustness. However, classical projection-based algorithms and existing circumcentered-reflection methods generally provide only asymptotic convergence. In this paper, we introduce a relaxed version of the centralized circumcentered-reflection method (cCRM) obtained through an extrapolation of the circumcenter step. We first analyze the two-set case and then extend the proposed approach to the successive framework for multiple convex sets, including under- and over-relaxation and a backtracking strategy. The convergence analysis establishes Fejér monotonicity and global convergence under suitable conditions, while local error-bound assumptions yield linear convergence and standard regularity assumptions imply superlinear convergence. Motivated by finitely convergent projection methods based on perturbed feasible sets, we further investigate a perturbed relaxed formulation designed to achieve finite termination. Numerical experiments show good practical performance, comparable to and competitive with the finitely convergent PACA method. The results also provide clear numerical evidence of finite termination in the tested instances.