# Benchmark problems for phase retrieval

## TL;DR

This paper introduces a set of benchmark problems for phase retrieval in crystallography, providing data, evaluation criteria, and baseline results to facilitate the development of improved algorithms in this challenging area.

## Contribution

It presents a graded, publicly available benchmark suite for phase retrieval in crystallography, along with success criteria and baseline runtimes, to advance algorithm development.

## Key findings

- Baseline runtimes grow exponentially with signal autocorrelation sparsity.
- The benchmark problems are designed to evaluate algorithm performance.
- A simple success/failure criterion tailored to crystallography needs.

## Abstract

In recent years, the mathematical and algorithmic aspects of the phase retrieval problem have received considerable attention. Many papers in this area mention crystallography as a principal application. In crystallography, the signal to be recovered is periodic and comprised of atomic distributions arranged homogeneously in the unit cell of the crystal. The crystallographic problem is both the leading application and one of the hardest forms of phase retrieval. We have constructed a graded set of benchmark problems for evaluating algorithms that perform this type of phase retrieval. The data, publicly available online, is provided in an easily interpretable format. We also propose a simple and unambiguous success/failure criterion based on the actual needs in crystallography. Baseline runtimes were obtained with an iterative algorithm that is similar but more transparent than those used in crystallography. Empirically, the runtimes grow exponentially with respect to a new hardness parameter: the sparsity of the signal autocorrelation. We also review the algorithms used by the leading software packages. This set of benchmark problems, we hope, will encourage the development of new algorithms for the phase retrieval problem in general, and crystallography in particular.