  Calibration of a typical radio interferometric array yields thousands of
parameters as solutions. These solutions contain valuable information about the
systematic errors in the data (ionosphere and beam shape). This information
could be reused in calibration to improve the accuracy and also can be fed into
imaging to improve the fidelity. We propose a distributed optimization strategy
to construct models for the systematic errors in the data using the calibration
solutions. We formulate this as an elastic net regularized distributed
optimization problem which we solve using the alternating direction method of
multipliers (ADMM) algorithm. We give simulation results to show the
feasibility of the proposed distributed model construction scheme.
