Stability of Over-Relaxations for the Forward-Backward Algorithm, Application to FISTA
研究了前向后向算法及其快速版本FISTA在计算存在误差时的收敛性，证明小误差下算法仍收敛且速度不变，并指出使用较低过松弛可容忍更大误差。
This paper is concerned with the convergence of over-relaxations of the forward-backward algorithm (FB) (in particular the fast iterative soft thresholding algorithm (FISTA)) in the case when proximal maps and/or gradients are computed with a possible error. We show that, provided these errors are small enough, the algorithm still converges to a minimizer of the functional, and with a speed of convergence (in terms of values of the functional) that remains the same as in the noise-free case. We also show that larger errors can be allowed, using a lower over-relaxation than FISTA. This still leads to the convergence of iterates and with ergodic convergence speed faster than the classical FB and FISTA.