Eigenvector Phase Retrieval: Recovering eigenvectors from the absolute value of their entries

Abstract

We consider the eigenvalue problem $Ax=\lambda x$ where $A\in {\mathbb{R}}^{n\times n}$ and the eigenvalue is also real $\lambda \in \mathbb{R}$. If we are given $A$, $\lambda$ and, additionally, the absolute value of the entries of $x$ (the vector $(|x_i|{)}_{i=1}^n$), is there a fast way to recover $x$? In particular, can this be done quicker than computing $x$ from scratch? This may be understood as a special case of the phase retrieval problem. We present a randomized algorithm which provably converges in expectation whenever $\lambda$ is a simple eigenvalue. The problem should become easier when $|\lambda |$ is large and we discuss another algorithm for that case as well.