  We propose a novel approach for estimating the location of block boundaries
(change-points) in a random matrix consisting of a block wise constant matrix
observed in white noise. Our method consists in rephrasing this task as a
variable selection issue. We use a penalized least-squares criterion with an
$\ell_1$-type penalty for dealing with this issue. We first provide some
theoretical results ensuring the consistency of our change-point estimators.
Then, we explain how to implement our method in a very efficient way. Finally,
we provide some empirical evidence to support our claims and apply our approach
to HiC data which are used in molecular biology for better understanding the
influence of the chromosomal conformation on the cells functioning.
