A triple-Bregman balanced primal-dual algorithm for saddle point problems achieves O(1/N) ergodic convergence, allows larger step sizes than PDHG in a Euclidean setting, and has accelerated variants under strong convexity.
Beck, First-order methods in optimization , SIAM, Philadelphia, 2017
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A Triple-Bregman Balanced Primal-Dual Algorithm for Saddle Point Problems
A triple-Bregman balanced primal-dual algorithm for saddle point problems achieves O(1/N) ergodic convergence, allows larger step sizes than PDHG in a Euclidean setting, and has accelerated variants under strong convexity.