# Sparse Phase Retrieval via Truncated Amplitude Flow

arXiv: https://arxiv.org/abs/1611.07641 · HTML: https://mlsanity.com/paper/1611.07641/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2016.

## Built on by (most cited first, 8 of 8)

- [Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors](https://mlsanity.com/paper/2106.15358/) (2021)
- [Misspecified Phase Retrieval with Generative Priors](https://mlsanity.com/paper/2210.05571/) (2022)
- [Solving Quadratic Systems with Full-Rank Matrices Using Sparse or Generative Priors](https://mlsanity.com/paper/2309.09032/) (2023)
- [Optimal sparse phase retrieval via a quasi-Bayesian approach](https://mlsanity.com/paper/2504.09509/) (2025)
- [Total Least Squares Phase Retrieval](https://mlsanity.com/paper/2102.00927/) (2021)
- [Acceleration and Implicit Regularization in Gaussian Phase Retrieval](https://mlsanity.com/paper/2311.12888/) (2023)
- [Unfolding-Aided Bootstrapped Phase Retrieval in Optical Imaging](https://mlsanity.com/paper/2203.01695/) (2022)
- [Tunable Domain Adaptation Using Unfolding](https://mlsanity.com/paper/2603.26931/) (2026)

## Abstract (arXiv)

This paper develops a novel algorithm, termed SPARse Truncated Amplitude flow (SPARTA), to reconstruct a sparse signal from a small number of magnitude-only measurements. It deals with what is also known as sparse phase retrieval (PR), which is NP-hard in general and emerges in many science and engineering applications. Upon formulating sparse PR as an amplitude-based nonconvex optimization task, SPARTA works iteratively in two stages: In stage one, the support of the underlying sparse signal is recovered using an analytically well-justified rule, and subsequently, a sparse orthogonality-promoting initialization is obtained via power iterations restricted on the support; and, in the second stage, the initialization is successively refined by means of hard thresholding based gradient-type iterations. SPARTA is a simple yet effective, scalable, and fast sparse PR solver. On the theoretical side, for any n-dimensional k-sparse (kll n) signal bmx with minimum (in modulus) nonzero entries on the order of (1/√k)|bmx|2, SPARTA recovers the signal exactly (up to a global unimodular constant) from about k2log n random Gaussian measurements with high probability. Furthermore, SPARTA incurs computational complexity on the order of k2nlog n with total runtime proportional to the time required to read the data, which improves upon the state-of-the-art by at least a factor of k. Finally, SPARTA is robust against additive noise of bounded support. Extensive numerical tests corroborate markedly improved recovery performance and speedups of SPARTA relative to existing alternatives.