  We recently proposed the use of consensus optimization as a viable and
effective way to improve the quality of calibration of radio interferometric
data. We showed that it is possible to obtain far more accurate calibration
solutions and also to distribute the compute load across a network of computers
by using this technique. A crucial aspect in any consensus optimization problem
is the selection of the penalty parameter used in the alternating direction
method of multipliers (ADMM) iterations. This affects the convergence speed as
well as the accuracy. In this paper, we use the Hessian of the cost function
used in calibration to appropriately select this penalty. We extend our results
to a multi-directional calibration setting, where we propose to use a penalty
scaled by the squared intensity of each direction.
