Abstract
The Douglas–Rachford method is a popular splitting technique for finding a zero of the sum of two subdifferential operators of proper, closed, and convex functions and, more generally, two maximally monotone operators. Recent results concerned with linear rates of convergence of the method require additional properties of the underlying monotone operators, such as strong monotonicity and cocoercivity. In this paper, we study the case, when one operator is Lipschitz continuous but not necessarily a subdifferential operator and the other operator is strongly monotone. This situation arises in optimization methods based on primal–dual approaches. We provide new linear convergence results in this setting.
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Notes
This property also means that \(R_A\) is hypomonotone; see [11, Example 12.28].
This is also known as negative averagedness of the operator \(T_2T_1\) [20].
A closed function is also known as lower semicontinuous.
Here and elsewhere, we use \(f^{*}\) to denote the convex conjugate (this is also known as the Fenchel or Legendre conjugate) of f defined at \(u\in X\) as \(f^*(u) =\sup _{x\in X}\left\{ \langle u,x\rangle -f(x)\right\} \).
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Acknowledgements
This work was done while the authors were visiting the Simons Institute for the Theory of Computing. It was partially supported by the DIMACS/Simons Collaboration on Bridging Continuous and Discrete Optimization through NSF Grant # CCF-1740425.
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Communicated by Jalal Fadili.
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Moursi, W.M., Vandenberghe, L. Douglas–Rachford Splitting for the Sum of a Lipschitz Continuous and a Strongly Monotone Operator. J Optim Theory Appl 183, 179–198 (2019). https://doi.org/10.1007/s10957-019-01517-8
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DOI: https://doi.org/10.1007/s10957-019-01517-8
Keywords
- Douglas–Rachford algorithm
- Linear convergence
- Lipschitz continuous mapping
- Skew-symmetric operator
- Strongly convex function
- Strongly monotone operator