Another Look at the Fast Iterative Shrinkage/Thresholding Algorithm (FISTA)
本文重新推导了广泛用于最小化含非光滑项（如L1正则化）的复合凸函数的FISTA算法，证明其对应加速近端梯度法的最优形式，并提出一种基于复合梯度映射最坏情况界的新算法。
This paper provides a new way of developing the fast iterative shrinkage/thresh-olding algorithm (FISTA) [A. Beck and M. Teboulle, SIAM J. Imaging Sci., 2 (2009), pp. 183--202] that is widely used for minimizing composite convex functions with a nonsmooth term such as the $\ell_1$ regularizer. In particular, this paper shows that FISTA corresponds to an optimized approach to accelerating the proximal gradient method with respect to a worst-case bound of the cost function. This paper then proposes a new algorithm that is derived by instead optimizing the step coefficients of the proximal gradient method with respect to a worst-case bound of the composite gradient mapping. The proof is based on the worst-case analysis called the performance estimation problem in [Y. Drori and M. Teboulle, Math. Program., 145 (2014), pp. 451--482].