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相关论文: An Elementary Proof of Convex Phase Retrieval in t…

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A recently proposed convex formulation of the phase retrieval problem estimates the unknown signal by solving a simple linear program. This new scheme, known as PhaseMax, is computationally efficient compared to standard convex relaxation…

信息论 · 计算机科学 2017-10-17 Oussama Dhifallah , Christos Thrampoulidis , Yue M. Lu

We consider the recovery of a (real- or complex-valued) signal from magnitude-only measurements, known as phase retrieval. We formulate phase retrieval as a convex optimization problem, which we call PhaseMax. Unlike other convex methods…

信息论 · 计算机科学 2018-01-31 Tom Goldstein , Christoph Studer

We study the problem of recovering the phase from magnitude measurements; specifically, we wish to reconstruct a complex-valued signal x of C^n about which we have phaseless samples of the form y_r = |< a_r,x >|^2, r = 1,2,...,m (knowledge…

信息论 · 计算机科学 2016-11-17 Emmanuel Candes , Xiaodong Li , Mahdi Soltanolkotabi

Recovering an unknown complex signal from the magnitude of linear combinations of the signal is referred to as phase retrieval. We present an exact performance analysis of a recently proposed convex-optimization-formulation for this…

信息论 · 计算机科学 2018-01-23 Fariborz Salehi , Ehsan Abbasi , Babak Hassibi

We consider a recently proposed convex formulation, known as the PhaseMax method, for solving the phase retrieval problem. Using the replica method from statistical mechanics, we analyze the performance of PhaseMax in the high-dimensional…

信息论 · 计算机科学 2017-08-14 Oussama Dhifallah , Yue M. Lu

We study algorithms for solving quadratic systems of equations based on optimization methods over polytopes. Our work is inspired by a recently proposed convex formulation of the phase retrieval problem, which estimates the unknown signal…

信息论 · 计算机科学 2018-05-25 Oussama Dhifallah , Christos Thrampoulidis , Yue M. Lu

Phase retrieval deals with the recovery of complex- or real-valued signals from magnitude measurements. As shown recently, the method PhaseMax enables phase retrieval via convex optimization and without lifting the problem to a higher…

信息论 · 计算机科学 2018-02-02 Ramina Ghods , Andrew S. Lan , Tom Goldstein , Christoph Studer

We propose a flexible convex relaxation for the phase retrieval problem that operates in the natural domain of the signal. Therefore, we avoid the prohibitive computational cost associated with "lifting" and semidefinite programming (SDP)…

信息论 · 计算机科学 2017-03-17 Sohail Bahmani , Justin Romberg

Suppose we wish to recover a signal x in C^n from m intensity measurements of the form |<x,z_i>|^2, i = 1, 2,..., m; that is, from data in which phase information is missing. We prove that if the vectors z_i are sampled independently and…

信息论 · 计算机科学 2011-09-22 Emmanuel J. Candes , Thomas Strohmer , Vladislav Voroninski

Consider the task of recovering an unknown $n$-vector from phaseless linear measurements. This task is the phase retrieval problem. Through the technique of lifting, this nonconvex problem may be convexified into a semidefinite rank-one…

最优化与控制 · 数学 2015-02-17 Paul Hand

This paper considers phase retrieval from the magnitude of 1D over-sampled Fourier measurements, a classical problem that has challenged researchers in various fields of science and engineering. We show that an optimal vector in a…

最优化与控制 · 数学 2016-11-03 Kejun Huang , Yonina C. Eldar , Nicholas D. Sidiropoulos

In the phase retrieval problem, an unknown vector is to be recovered given quadratic measurements. This problem has received considerable attention in recent times. In this paper, we present an algorithm to solve a nonconvex formulation of…

信息论 · 计算机科学 2016-06-13 Ritesh Kolte , Ayfer Özgür

The PhaseLift algorithm is an effective convex method for solving the phase retrieval problem from Fourier measurements with coded diffraction patterns (CDP). While exact reconstruction guarantees are well-established in the noiseless case,…

数值分析 · 数学 2025-10-14 Meng Huang , Jinming Wen , Ran Zhang

This paper considers the question of recovering the phase of an object from intensity-only measurements, a problem which naturally appears in X-ray crystallography and related disciplines. We study a physically realistic setup where one can…

信息论 · 计算机科学 2013-11-08 Emmanuel Candes , Xiaodong Li , Mahdi Soltanolkotabi

Phase retrieval seeks to recover a signal x from the amplitude |Ax| of linear measurements. We cast the phase retrieval problem as a non-convex quadratic program over a complex phase vector and formulate a tractable relaxation (called…

最优化与控制 · 数学 2013-07-23 Irène Waldspurger , Alexandre d'Aspremont , Stéphane Mallat

In this work we analyze the problem of phase retrieval from Fourier measurements with random diffraction patterns. To this end, we consider the recently introduced PhaseLift algorithm, which expresses the problem in the language of convex…

信息论 · 计算机科学 2017-01-10 David Gross , Felix Krahmer , Richard Kueng

This paper develops a novel framework for phase retrieval, a problem which arises in X-ray crystallography, diffraction imaging, astronomical imaging and many other applications. Our approach combines multiple structured illuminations…

信息论 · 计算机科学 2011-09-21 Emmanuel J. Candes , Yonina Eldar , Thomas Strohmer , Vlad Voroninski

Phase retrieval aims to recover a signal $x \in \mathbb{C}^{n}$ from its amplitude measurements $|<x, a_i > |^2$, $i=1,2,...,m$, where $a_i$'s are over-complete basis vectors, with $m$ at least $3n -2$ to ensure a unique solution up to a…

最优化与控制 · 数学 2014-10-09 Penghang Yin , Jack Xin

We develop procedures, based on minimization of the composition $f(x) = h(c(x))$ of a convex function $h$ and smooth function $c$, for solving random collections of quadratic equalities, applying our methodology to phase retrieval problems.…

统计理论 · 数学 2018-04-24 John C. Duchi , Feng Ruan

In this paper, we propose a new non-convex algorithm for solving the phase retrieval problem, i.e., the reconstruction of a signal $ \vx\in\H^n $ ($\H=\R$ or $\C$) from phaseless samples $ b_j=\abs{\langle \va_j, \vx\rangle } $, $…

数值分析 · 数学 2020-10-15 Bing Gao , Xinwei Sun , Yang Wang , Zhiqiang Xu
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