This work aims to accelerate the convergence of proximal gradient methods used to solve regularized linear inverse problems. This is achieved by designing a polynomial-based preconditioner that targets the eigenvalue spectrum of the normal operator derived from the linear operator. The preconditioner does not assume any explicit structure on the linear function and thus can be deployed in diverse applications of interest. The efficacy of the preconditioner is validated on three different Magnetic Resonance Imaging applications, where it is seen to achieve faster iterative convergence while achieving similar reconstruction quality.
@article{arxiv.2204.10252,
title = {Polynomial Preconditioners for Regularized Linear Inverse Problems},
author = {Siddharth Srinivasan Iyer and Frank Ong and Xiaozhi Cao and Congyu Liao and Luca Daniel and Jonathan I. Tamir and Kawin Setsompop},
journal= {arXiv preprint arXiv:2204.10252},
year = {2022}
}