  In this paper, we address strongly convex programming for princi- pal
component pursuit with reduced linear measurements, which decomposes a
superposition of a low-rank matrix and a sparse matrix from a small set of
linear measurements. We first provide sufficient conditions under which the
strongly convex models lead to the exact low-rank and sparse matrix recov- ery;
Second, we also give suggestions on how to choose suitable parameters in
practical algorithms.
