  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 the 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 (MPC) optimization problems.
The experimental results show that our algorithm can outperform the
state-of-the-art optimization software MOSEK and ECOS for the small size MPC
problems.
