  This chapter develops a theoretical analysis of the convex programming method
for recovering a structured signal from independent random linear measurements.
This technique delivers bounds for the sampling complexity that are similar
with recent results for standard Gaussian measurements, but the argument
applies to a much wider class of measurement ensembles. To demonstrate the
power of this approach, the paper presents a short analysis of phase retrieval
by trace-norm minimization. The key technical tool is a framework, due to
Mendelson and coauthors, for bounding a nonnegative empirical process.
