  We consider the iterative shrinkage/thresholding algorithm (ISTA) applied to
a cost function composed of a data fidelity term and a penalty term. The
penalty is non-convex but the concavity of the penalty is accounted for by the
data fidelity term so that the overall cost function is convex. We provide a
generalization of the convergence result for ISTA viewed as a forward-backward
splitting algorithm. We also demonstrate experimentally that for the current
setup, using large stepsizes in ISTA can accelerate convergence more than
existing schemes proposed for the convex case, like TwIST or FISTA.
