For a saddle point problem , one update ordering isThe proximal operators treat both nonsmooth convex terms, while the extrapolation couples the primal and dual variables. The primal objective is , with the convex conjugate. A dual-first ordering is obtained by exchanging roles; consistent indexing is needed when comparing the formulas.
For proper lower semicontinuous convex functions on finite-dimensional Hilbert spaces, a nonempty saddle point set, and constant positive steps satisfying the displayed inequality, the primal-dual hybrid gradient method with extrapolation parameter one converges to a saddle point. The strict condition controls the bilinear coupling by the operator norm. It does not remove the need for existence of a saddle point. The fixed-step theorem and assumptions are recalled in Malitsky and Pock, A first-order primal-dual algorithm with linesearch, Section 1.
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The Chambolle-Pock algorithm is a powerful method for solving optimization problems that involve a combination of convex functions and Bregman distances. It is particularly useful for problems that can be framed as finding a minimizer of a convex function subject to certain constraints.