Recent advances in convex optimization have led to new strides in the phase
retrieval problem over finite-dimensional vector spaces. However, certain
fundamental questions remain: What sorts of measurement vectors uniquely
determine every signal up to a global phase factor, and how many are needed to
do so? Furthermore, which measurement ensembles lend stability? This paper
presents several results that address each of these questions. We begin by
characterizing injectivity, and we identify that the complement property is
indeed a necessary condition in the complex case. We then pose a conjecture
that 4M-4 generic measurement vectors are both necessary and sufficient for
injectivity in M dimensions, and we prove this conjecture in the special cases
where M=2,3. Next, we shift our attention to stability, both in the worst and
average cases. Here, we characterize worst-case stability in the real case by
introducing a numerical version of the complement property. This new property
bears some resemblance to the restricted isometry property of compressed
sensing and can be used to derive a sharp lower Lipschitz bound on the
intensity measurement mapping. Localized frames are shown to lack this property
(suggesting instability), whereas Gaussian random measurements are shown to
satisfy this property with high probability. We conclude by presenting results
that use a stochastic noise model in both the real and complex cases, and we
leverage Cramer-Rao lower bounds to identify stability with stronger versions
of the injectivity characterizations.Comment: 22 page