Douglas-Rachford Splitting: Complexity Estimates and Accelerated Variants
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Abstract (arXiv)
We propose a new approach for analyzing convergence of the Douglas-Rachford splitting method for solving convex composite optimization problems. The approach is based on a continuously differentiable function, the Douglas-Rachford Envelope (DRE), whose stationary points correspond to the solutions of the original (possibly nonsmooth) problem. By proving the equivalence between the Douglas-Rachford splitting method and a scaled gradient method applied to the DRE, results from smooth unconstrained optimization are employed to analyze convergence properties of DRS, to tune the method and to derive an accelerated version of it.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jul 2014.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
4 papers.
- 2021Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusionscited by 20
- 2022PEPit: computer-assisted worst-case analyses of first-order optimization methods in Pythoncited by 10
- 2024Accelerated forward-backward and Douglas-Rachford splitting dynamicscited by 2
- 2025Robust Non-negative Proximal Gradient Algorithm for Inverse Problemscited by 0
- 2025Robust Non-negative Proximal Gradient Algorithm for Inverse Problems27 Oct 2025
- 2024Accelerated forward-backward and Douglas-Rachford splitting dynamics30 Jul 2024
- 2022PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python11 Jan 2022
- 2021Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusions15 Oct 2021