  The main objective of this work is to accelerate the Maximum-Likelihood (ML)
estimation procedure in radio interferometric calibration. We introduce the
OS-LS and the OS-SAGE radio interferometric calibration methods, as a
combination of the Ordered-Subsets (OS) method with the Least-Squares (LS) and
Space Alternating Generalized Expectation maximization (SAGE) calibration
techniques, respectively. The OS algorithm speeds up the ML estimation and
achieves nearly the same level of accuracy of solutions as the one obtained by
the non-OS methods. We apply the OS-LS and OS-SAGE calibration methods to
simulated observations and show that these methods have a much higher
convergence rate relative to the conventional LS and SAGE techniques. Moreover,
the obtained results show that the OS-SAGE calibration technique has a superior
performance compared to the OS-LS calibration method in the sense of achieving
more accurate results while having significantly less computational cost.
