  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.
