A relaxed version of Bregman's method for convex programming
A new type of relaxation for Bregman's method, an iterative primal-dual algorithm for linearly constrained convex programming, is presented. It is shown that the new relaxation procedure generalizes the usual concept of relaxation and preserves the convergence properties of Bregman's algorithm for a suitable choice of the relaxation parameters. For convergence, Bregman's method requires that the objective function satisfy certain conditions. A sufficient and easily checkable condition for these requirements to hold is also given.
scientific article; zbMATH DE number 3930732