On convergence of greedy block nonlinear Kaczmarz methods with momentum
Naiyu Jiang, Wendi Bao, Lili Xing, Weiguo Li
TL;DR
The paper tackles solving nonlinear systems $f(x)=0$ by introducing two momentum-augmented, pseudoinverse-free greedy block nonlinear Kaczmarz methods, RBWNK-m and MRWNK-m, built from RBWNK/MRWNK and the heavy-ball momentum. It develops two greedy block index rules and proves convergence under a local tangential cone condition without assuming full column rank of the Jacobian, providing explicit rate bounds dependent on momentum and selection parameters. The theoretical results are complemented by numerical experiments on several nonlinear test problems, where the momentum-augmented schemes consistently reduce iterations and CPU time compared to their non-momentum counterparts. The work demonstrates that incorporating momentum yields meaningful improvements in convergence speed for nonlinear Kaczmarz-type methods with greedy block strategies.
Abstract
In this paper, for solving nonlinear systems we propose two pseudoinverse-free greedy block methods with momentum by combining the residual-based weighted nonlinear Kaczmarz and heavy ball methods. Without the full column rank assumptions on Jacobi matrices of nonlinear systems, we provide a thorough convergence analysis, and derive upper bounds for the convergence rates of the new methods. Numerical experiments demonstrate that the proposed methods with momentum are much more effective than the existing ones.
