Graph splitting methods: Fixed points and strong convergence for linear subspaces
Francisco J. Aragón-Artacho, Heinz H. Bauschke, Rubén Campoy, César López-Pastor
TL;DR
This work addresses the fixed-point structure and convergence of graph-splitting methods for solving sums of maximally monotone operators. It adopts a graph-based, preconditioned proximal-point framework to derive explicit fixed-point sets and projection formulas, unifying several known schemes (e.g., Douglas--Rachford, Ryu, Malitsky--Tam) and enabling the design of new variants. The main contributions include explicit expressions for the fixed points when the operators are normal cones of closed linear subspaces, characterization of limit points, and strong convergence results in the linear-subspace setting. The results provide a systematic, graph-driven approach that extends prior analyses and offers practical tools for producing and analyzing new splitting algorithms in feasibility problems.
Abstract
In this paper, we develop a general analysis for the fixed points of the operators defining the graph splitting methods from [SIAM J. Optim., 34 (2024), pp. 1569-1594] by Bredies, Chenchene and Naldi. We particularize it to the case where the maximally monotone operators are normal cones of closed linear subspaces and provide an explicit formula for the limit points of the graph splitting schemes. We exemplify these results on some particular algorithms, unifying in this way some results previously derived as well as obtaining new ones.
