Instance optimality in phase retrieval

TL;DR

This paper establishes that stable, instance-optimal recovery of approximately sparse signals is possible from a near-linear number of phaseless measurements, paralleling classical compressed sensing results.

Contribution

It proves that $(2,1)$ and $(1,1)$-instance optimality can be achieved with $O(k \, ext{log}(n/k))$ measurements in phase retrieval, extending compressed sensing theory.

Findings

Achieves instance optimality with $O(k \, ext{log}(n/k))$ measurements

Establishes parallels between phase retrieval and compressed sensing

Provides a non-uniform probabilistic version of instance optimality

Abstract

Compressed sensing has demonstrated that a general signal $\boldsymbol{x} \in \mathbb{F}^n$ ($\mathbb{F}\in \{\mathbb{R},\mathbb{C}\}$) can be estimated from few linear measurements with an error {proportional to} the best $k$-term approximation error, a property known as instance optimality. In this paper, we investigate instance optimality in the context of phaseless measurements using the $\ell_p$-minimization decoder, where $p \in (0, 1]$, for both real and complex cases. More specifically, we prove that $(2,1)$ and $(1,1)$-instance optimality of order $k$ can be achieved with $m =O(k \log(n/k))$ phaseless measurements, paralleling results from linear measurements. These results imply that one can stably recover approximately $k$-sparse signals from $m = O(k \log(n/k))$ phaseless measurements. Our approach leverages the phaseless bi-Lipschitz condition. Additionally, we present a non-uniform version of $(2,2)$-instance optimality result in probability applicable to any fixed vector $\boldsymbol{x} \in \mathbb{F}^n$. These findings reveal striking parallels between compressive phase retrieval and classical compressed sensing, enhancing our understanding of both phase retrieval and instance optimality.