# Solving Random Systems of Quadratic Equations with Tanh Wirtinger Flow

## TL;DR

This paper introduces a novel Wirtinger flow algorithm with spectral initialization for phase retrieval, demonstrating improved efficiency and effectiveness in low measurement regimes through theoretical analysis and numerical experiments.

## Contribution

A new form of Wirtinger flow and spectral initialization method for phase retrieval, with proven linear sample and computational complexities and enhanced performance in low data scenarios.

## Key findings

- Achieves high probability of success with fewer measurements than the information-theoretic limit.
- Proven linear sample and computational complexities for the new algorithm.
- Demonstrates effectiveness in numerical tests with low measurement-to-parameter ratios.

## Abstract

Solving quadratic systems of equations in n variables and m measurements of the form $y_i = |a^T_i x|^2$ , $i = 1, ..., m$ and $x \in R^n$ , which is also known as phase retrieval, is a hard nonconvex problem. In the case of standard Gaussian measurement vectors, the wirtinger flow algorithm Chen and Candes (2015) is an efficient solution. In this paper, we proposed a new form of wirtinger flow and a new spectral initialization method based on this new algorithm. We proved that the new wirtinger flow and initialization method achieve linear sample and computational complexities. We further extended the new phasing algorithm by combining it with other existing methods. Finally, we demonstrated the effectiveness of our new method in the low data to parameter ratio settings where the number of measurements which is less than information-theoretic limit, namely, $m < 2n$, via numerical tests. For instance, our method can solve the quadratic systems of equations with gaussian measurement vector with probability $\ge 97\%$ when $m/n = 1.7$ and $n = 1000$, and with probability $\approx 60\%$ when $m/n = 1.5$ and $n = 1000$.