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Phase Retrieval using Lipschitz Continuous Maps

Functional Analysis 2014-03-11 v1 Information Theory math.IT Machine Learning

Abstract

In this note we prove that reconstruction from magnitudes of frame coefficients (the so called "phase retrieval problem") can be performed using Lipschitz continuous maps. Specifically we show that when the nonlinear analysis map α:HRm\alpha:{\mathcal H}\rightarrow\mathbb{R}^m is injective, with (α(x))k=<x,fk>2(\alpha(x))_k=|<x,f_k>|^2, where {f1,,fm}\{f_1,\ldots,f_m\} is a frame for the Hilbert space H{\mathcal H}, then there exists a left inverse map ω:RmH\omega:\mathbb{R}^m\rightarrow {\mathcal H} that is Lipschitz continuous. Additionally we obtain the Lipschitz constant of this inverse map in terms of the lower Lipschitz constant of α\alpha. Surprisingly the increase in Lipschitz constant is independent of the space dimension or frame redundancy.

Keywords

Cite

@article{arxiv.1403.2301,
  title  = {Phase Retrieval using Lipschitz Continuous Maps},
  author = {Radu Balan and Dongmian Zou},
  journal= {arXiv preprint arXiv:1403.2301},
  year   = {2014}
}

Comments

12 pages, 1 figure

R2 v1 2026-06-22T03:23:38.989Z