Solving Systems of Quadratic Equations via Exponential-Type Gradient Descent Algorithm

Solving Systems of Quadratic Equations via Exponential-Type Gradient Descent Algorithm

Year:    2020

Author:    Meng Huang, Zhiqiang Xu

Journal of Computational Mathematics, Vol. 38 (2020), Iss. 4 : pp. 638–660

Abstract

We consider the rank minimization problem from quadratic measurements, i.e., recovering a rank $r$ matrix $X \in \mathbb{R}^{n×r}$ from $m$ scalar measurements $y_i=a_i^T XX^T a_i,\;a_i\in \mathbb{R}^n,\;i=1,\ldots,m$. Such problem arises in a variety of applications such as quadratic regression and quantum state tomography. We present a novel algorithm, which is termed $exponential-type$ $gradient$ $descent$ $algorithm$, to minimize a non-convex objective function $f(U)=\frac{1}{4m}\sum_{i=1}^m(y_i-a_i^T UU^T a_i)^2$. This  algorithm  starts with a careful initialization, and then refines this initial guess by iteratively applying exponential-type gradient descent. Particularly,  we can obtain a good initial guess of $X$ as long as the number of Gaussian random measurements is  $O(nr)$, and  our iteration algorithm can converge linearly to the true $X$ (up to an orthogonal matrix) with $m=O\left(nr\log (cr)\right)$ Gaussian random measurements.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jcm.1902-m2018-0109

Journal of Computational Mathematics, Vol. 38 (2020), Iss. 4 : pp. 638–660

Published online:    2020-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    23

Keywords:    Low-rank matrix recovery Non-convex optimization Phase retrieval.

Author Details

Meng Huang

Zhiqiang Xu

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