The Estimation Performance of Nonlinear Least Squares for Phase Retrieval
文献类型：期刊论文
| 作者 | Huang, Meng1,2; Xu, Zhiqiang1,2 |
| 刊名 | IEEE TRANSACTIONS ON INFORMATION THEORY
 |
| 出版日期 | 2020-12-01 |
| 卷号 | 66期号:12页码:7967-7977 |
| 关键词 | Phase retrieval estimation performance nonlinear least squares nonlinear Lasso |
| ISSN号 | 0018-9448 |
| DOI | 10.1109/TIT.2020.2983562 |
| 英文摘要 | Suppose that y = vertical bar Ax(0)vertical bar + eta where x(0) is an element of R-d is the target signal and eta is an element of R-m is a noise vector. The aim of phase retrieval is to estimate x(0) from y. A popular model for estimating x(0) is the nonlinear least squares (x) over cap := argmin(x) parallel to vertical bar Ax vertical bar - y parallel to 2. One has already developed many efficient algorithms for solving the model, such as the seminal error reduction algorithm. In this paper, we present the estimation performance of the model with proving that parallel to(x)over cap> - x(0)parallel to less than or similar to parallel to eta parallel to 2/root m under the assumption of A being a Gaussian random matrix. We also prove the reconstruction error parallel to eta parallel to(2)/root m is sharp. For the case where x(0) is sparse, we study the estimation performance of both the nonlinear Lasso of phase retrieval and its unconstrained version. Our results are non-asymptotic, and we do not assume any distribution on the noise eta. To the best of our knowledge, our results represent the first theoretical guarantee for the nonlinear least squares and for the nonlinear Lasso of phase retrieval. |
| 资助项目 | NSFC[91630203] ; NSFC[11688101] ; Beijing Natural Science Foundation[Z180002] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000594905600043 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/57843]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Xu, Zhiqiang |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Math Sci, Inst Computat Math, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Meng,Xu, Zhiqiang. The Estimation Performance of Nonlinear Least Squares for Phase Retrieval[J]. IEEE TRANSACTIONS ON INFORMATION THEORY,2020,66(12):7967-7977. |
| APA | Huang, Meng,&Xu, Zhiqiang.(2020).The Estimation Performance of Nonlinear Least Squares for Phase Retrieval.IEEE TRANSACTIONS ON INFORMATION THEORY,66(12),7967-7977. |
| MLA | Huang, Meng,et al."The Estimation Performance of Nonlinear Least Squares for Phase Retrieval".IEEE TRANSACTIONS ON INFORMATION THEORY 66.12(2020):7967-7977. |
入库方式： OAI收割
来源：数学与系统科学研究院
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