1 research outputs found
Convergence Revisit on Generalized Symmetric ADMM
In this note, we show a sublinear nonergodic convergence rate for the
algorithm developed in [Bai, et al. Generalized symmetric ADMM for separable
convex optimization. Comput. Optim. Appl. 70, 129-170 (2018)], as well as its
linear convergence under assumptions that the sub-differential of each
component objective function is piecewise linear and all the constraint sets
are polyhedra. These remaining convergence results are established for the
stepsize parameters of dual variables belonging to a special isosceles triangle
region, which aims to strengthen our understanding for convergence of the
generalized symmetric ADMM.Comment: 16 page