🤖 AI Summary
This work addresses the theoretical limitation of the Chambolle–Pock algorithm (CPA), whose convergence analysis traditionally relies on monotonicity assumptions, thereby restricting its applicability to non-monotone or weakly monotone optimization problems.
Method: We introduce the novel “skew-weak Minty condition” to quantify the degree of non-monotonicity of primal–dual operators. Leveraging variational inequality theory, operator theory, and singular value analysis of linear mappings, we derive tight sufficient conditions on step sizes and relaxation parameters—imposing additional constraints in the non-monotone regime while allowing the relaxation parameter to exceed the classical upper bound of 2 under strong monotonicity.
Contribution/Results: The framework unifies submonotone, cosubmonotone, and semimonotone operator classes. Constructive counterexamples verify the tightness of the derived bounds. Our results substantially broaden the theoretical foundation and practical scope of CPA, enabling rigorous convergence guarantees beyond standard monotonicity assumptions.
📝 Abstract
The Chambolle-Pock algorithm (CPA), also known as the primal-dual hybrid gradient method, has gained popularity over the last decade due to its success in solving large-scale convex structured problems. This work extends its convergence analysis for problems with varying degrees of (non)monotonicity, quantified through a so-called oblique weak Minty condition on the associated primal-dual operator. Our results reveal novel stepsize and relaxation parameter ranges which do not only depend on the norm of the linear mapping, but also on its other singular values. In particular, in nonmonotone settings, in addition to the classical stepsize conditions, extra bounds on the stepsizes and relaxation parameters are required. On the other hand, in the strongly monotone setting, the relaxation parameter is allowed to exceed the classical upper bound of two. Moreover, we build upon the recently introduced class of semimonotone operators, providing sufficient convergence conditions for CPA when the individual operators are semimonotone. Since this class of operators encompasses traditional operator classes including (hypo)- and co(hypo)-monotone operators, this analysis recovers and extends existing results for CPA. Tightness of the proposed stepsize ranges is demonstrated through several examples.
