  In this paper, a multi-parameterized proximal point algorithm combining with
a relaxation step is developed for solving convex minimization problem subject
to linear constraints. We show its global convergence and sublinear convergence
rate from the prospective of variational inequality. Preliminary numerical
experiments on testing a sparse minimization problem from signal processing
indicate that the proposed algorithm performs better than some well-established
methods
