# Ptychographic phase-retrieval by proximal algorithms

arXiv: https://arxiv.org/abs/1909.06482 · HTML: https://mlsanity.com/paper/1909.06482/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Learning neural representations for X-ray ptychography reconstruction with unknown probes](https://mlsanity.com/paper/2509.04402/) (2025)

## Abstract (arXiv)

We derive a set of ptychography phase-retrieval iterative engines based on proximal algorithms originally developed in convex optimization theory, and discuss their connections with existing ones. The use of proximal operator creates a simple frame work that allows us to incorporate the effect of noise from a maximum-likelihood principle. We focus on three particular algorithms, namely proximal minimization, alternating direction method of multiplier and accelerated proximal gradient, and benckmark their performance with numerical simulations and experimental x-ray data. Among them, accelerated proximal gradient shows superior performance in terms of both accuracy and convergence rate for a noisy dataset.