  In this paper, we develop a parameterized proximal point algorithm (P-PPA)
for solving a class of separable convex programming problems subject to linear
and convex constraints. The proposed algorithm is provable to be globally
convergent with a worst-case O(1/t) convergence rate, wheret denotes the
iteration number. By properly choosing the algorithm parameters, numerical
experiments on solving a sparse optimization problem arising from statistical
learning show that our P-PPA could perform significantly better than other
state-of-the-art methods, such as the alternating direction method of
multipliers and the relaxed proximal point algorithm.
