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An alternative extrapolation scheme of PDHGM for saddle point problem with nonlinear function

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Abstract

Primal-dual hybrid gradient (PDHG) method is a canonical and popular prototype for solving saddle point problem (SPP). However, the nonlinear coupling term in SPP excludes the application of PDHG on far-reaching real-world problems. In this paper, following the seminal work by Valkonen (Inverse Problems 30, 2014), we devise a variant iterative scheme for solving SPP with nonlinear function by exerting an alternative extrapolation procedure. The novel iterative scheme falls exactly into the proximal point algorithmic framework without any residuals, which indicates that the associated inclusion problem is “nearer” to the KKT mapping induced by SPP. Under the metrically regular assumption on KKT mapping, we simplify the local convergence of the proposed method on contractive perspective. Numerical simulations on a PDE-constrained nonlinear inverse problem demonstrate the compelling performance of the proposed method.

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Data availability statement

All data generated or analyzed during this study are available from the corresponding author.

Notes

  1. By the acronym in [18, 43], we shall term the recursion (2) with \(\theta =0\) (resp. \(\theta \ne 0\)) as primal-dual hybrid gradient method (resp. modified primal-dual hybrid gradient).

  2. The critical point of SPP varies in different literature. We herein follow the definition in [13].

  3. Herein, for ease of description, we present the nonlinear inverse problem in continuum context as in [13, 15].

  4. Codes of accelerated-NL-PDHGM are available at https://github.com/clason/nlpdegm.

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Acknowledgements

The authors would like to thank the anonymous referees for their valuable comments, which helped improving the manuscript substantially. This work is support by NSFC grant (11971003), and partially by Science and Technology Department of Sichuan Province (2021YFG0125) and ZYGX2019J090.

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Gao, Y., Zhang, W. An alternative extrapolation scheme of PDHGM for saddle point problem with nonlinear function. Comput Optim Appl 85, 263–291 (2023). https://doi.org/10.1007/s10589-023-00453-8

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