Monotonicity and restart in fast gradient methods

Fast gradient methods are known to be nonmonotone algorithms, and oscillations typically occur around the solution. To avoid this behavior, we propose in this paper a fast gradient method with restart, and analyze its convergence rate. The proposed algorithm bears similarities to other algorithms in the literature, but differs in a key point that enables theoretical convergence rate results. The efficiency of the proposed method is demonstrated by two numerical examples.