LAUSR

In this letter, an accelerated quadratic programming (QP) algorithm is proposed based on the proximal gradient method. The algorithm can achieve convergence rate $O{(}1/p^{\alpha }{)}$ , where $p$ is the iteration number and $\alpha $ is a given positive integer. The proposed algorithm improves the convergence rate of existing algorithms that achieve $O{(}1/p^{2}{)}$ . The key idea is that iterative parameters are selected from a group of specific high order polynomial equations. The performance of the proposed algorithm is assessed on the randomly generated model predictive control optimization problems. The effectiveness of our algorithm is verified by the numerical experiments.