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Poster
Phase Retrieval using Alternating Minimization
Praneeth Netrapalli · Prateek Jain · Sujay Sanghavi

Fri Dec 06 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None
Phase retrieval problems involve solving linear equations, but with missing sign (or phase, for complex numbers) information. Over the last two decades, a popular generic empirical approach to the many variants of this problem has been one of alternating minimization; i.e. alternating between estimating the missing phase information, and the candidate solution. In this paper, we show that a simple alternating minimization algorithm geometrically converges to the solution of one such problem -- finding a vector $x$ from $y,A$, where $y = |A'x|$ and $|z|$ denotes a vector of element-wise magnitudes of $z$ -- under the assumption that $A$ is Gaussian. Empirically, our algorithm performs similar to recently proposed convex techniques for this variant (which are based on "lifting" to a convex matrix problem) in sample complexity and robustness to noise. However, our algorithm is much more efficient and can scale to large problems. Analytically, we show geometric convergence to the solution, and sample complexity that is off by log factors from obvious lower bounds. We also establish close to optimal scaling for the case when the unknown vector is sparse. Our work represents the only known proof of alternating minimization for any variant of phase retrieval problems in the non-convex setting.

Author Information

Praneeth Netrapalli (Microsoft Research)
Prateek Jain (Microsoft Research)
Sujay Sanghavi (UT-Austin)

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