Sparse Signal Recovery from Phaseless Measurements via Hard Thresholding Pursuit

TL;DR

This paper introduces a second-order algorithm inspired by hard thresholding pursuit for sparse phase retrieval, achieving finite-step exact recovery with high efficiency and outperforming existing methods in speed.

Contribution

The paper proposes a novel second-order algorithm for sparse phase retrieval with finite-step guarantees, improving speed and accuracy over first-order methods.

Findings

Finite-step exact recovery under Gaussian measurements.

Algorithm is several times faster than existing methods.

Guaranteed recovery with O(s^2 log n) samples.

Abstract

In this paper, we consider the sparse phase retrieval problem, recovering an $s$-sparse signal $\bm{x}^{\natural}\in\mathbb{R}^n$ from $m$ phaseless samples $y_i=|\langle\bm{x}^{\natural},\bm{a}_i\rangle|$ for $i=1,\ldots,m$. Existing sparse phase retrieval algorithms are usually first-order and hence converge at most linearly. Inspired by the hard thresholding pursuit (HTP) algorithm in compressed sensing, we propose an efficient second-order algorithm for sparse phase retrieval. Our proposed algorithm is theoretically guaranteed to give an exact sparse signal recovery in finite (in particular, at most $O(\log m + \log(\|\bm{x}^{\natural}\|_2/|x_{\min}^{\natural}|))$) steps, when $\{\bm{a}_i\}_{i=1}^{m}$ are i.i.d. standard Gaussian random vector with $m\sim O(s\log(n/s))$ and the initialization is in a neighborhood of the underlying sparse signal. Together with a spectral initialization, our algorithm is guaranteed to have an exact recovery from $O(s^2\log n)$ samples. Since the computational cost per iteration of our proposed algorithm is the same order as popular first-order algorithms, our algorithm is extremely efficient. Experimental results show that our algorithm can be several times faster than existing sparse phase retrieval algorithms.