  We address the computational challenge of finding the robust sequential
change-point detection procedures when the pre- and post-change distributions
are not completely specified. Earlier works [veeravalli 1994] and [Unnikrishnan
2011] establish the general conditions for robust procedures which include
finding a pair of least favorable distributions (LFDs). However, in the
multi-dimensional setting, it is hard to find such LFDs computationally. We
present a method based on convex optimization that addresses this issue when
the distributions are Gaussian with unknown parameters from pre-specified
uncertainty sets. We also establish theoretical properties of our robust
procedures, and numerical examples demonstrate their good performance.
