  Graphical models with change-points are computationally challenging to fit,
particularly in cases where the number of observation points and the number of
nodes in the graph are large. Focusing on Gaussian graphical models, we
introduce an approximate majorize-minimize (MM) algorithm that can be useful
for computing change-points in large graphical models. The proposed algorithm
is an order of magnitude faster than a brute force search. Under some
regularity conditions on the data generating process, we show that with high
probability, the algorithm converges to a value that is within statistical
error of the true change-point. A fast implementation of the algorithm using
Markov Chain Monte Carlo is also introduced. The performances of the proposed
algorithms are evaluated on synthetic data sets and the algorithm is also used
to analyze structural changes in the S&P 500 over the period 2000-2016.
