Preconditioned ADMM with nonlinear operator constraint
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Abstract (arXiv)
We are presenting a modification of the well-known Alternating Direction Method of Multipliers (ADMM) algorithm with additional preconditioning that aims at solving convex optimisation problems with nonlinear operator constraints. Connections to the recently developed Nonlinear Primal-Dual Hybrid Gradient Method (NL-PDHGM) are presented, and the algorithm is demonstrated to handle the nonlinear inverse problem of parallel Magnetic Resonance Imaging (MRI).
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Nov 2015.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1.
3 papers.
- 2021Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Priorcited by 24
- 2023Coefficient Shape Alignment in Multiple Functional Linear Regressioncited by 0
- 2024ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising applicationcited by 0
- 2024ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application11 Jul 2024
- 2023Coefficient Shape Alignment in Multiple Functional Linear Regression4 Dec 2023
- 2021Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Prior2 Mar 2021