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Polynomial Preconditioning for Accelerated Convergence of Proximal Algorithms including FISTA
Siddharth Iyer, Frank Ong, Kawin Setsompop
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scienti
DOI: 10.58530/2022/3483 (Computer Science)
Licence : non indiquée (tous droits réservés)
Résumé : This work aims to accelerate the convergence of proximal gradient algorithms (like FISTA) by designing a preconditioner using polynomials that targets the eigenvalue spectrum of the forward linear model to enable faster convergence. The preconditioner does not assume any explicit structure and can be applied to various imaging applications. The efficacy of the preconditioner is validated on four varied imaging applications, where it seen to achieve 2-4x faster convergence.
Publié le 03 Aoû. 2023