  Alternating Direction Method of Multipliers (ADMM) algorithm has been widely
adopted for solving the distributed optimization problem (DOP).
  In this paper, a new distributed parallel ADMM algorithm is proposed, which
allows the agents to update their local states and dual variables in a
completely distributed and parallel manner by modifying the existing
distributed sequential ADMM.
  Moreover, the updating rules and storage method for variables are
illustrated.
  It is shown that all the agents can reach a consensus by asymptotically
converging to the optimal solution.
  Besides, the global cost function will converge to the optimal value at a
rate of O(1/k).
  Simulation results on a numerical example are given to show the effectiveness
of the proposed algorithm.
