scholarly journals Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization

2012 ◽  
Vol 29 (2) ◽  
pp. 239-263 ◽  
Author(s):  
Y. Shen ◽  
Z. Wen ◽  
Y. Zhang
Keyword(s):  
Augmented Lagrangian ◽  
Alternating Direction Method ◽  
Low Rank ◽  
Matrix Separation ◽  
Alternating Direction ◽  
Rank Factorization

An Alternating Direction Method with Continuation for Nonconvex Low Rank Minimization

2015 ◽  
Vol 66 (2) ◽  
pp. 849-869 ◽  
Author(s):  
Zheng-Fen Jin ◽  
Zhongping Wan ◽  
Yuling Jiao ◽  
Xiliang Lu
Keyword(s):  
Alternating Direction Method ◽  
Low Rank ◽  
Rank Minimization ◽  
Alternating Direction

scholarly journals A New Algorithm for Positive Semidefinite Matrix Completion

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Fangfang Xu ◽  
Peng Pan
Keyword(s):  
Matrix Completion ◽  
Positive Semidefinite ◽  
Alternating Direction Method ◽  
Low Rank ◽  
System Control ◽  
Positive Semidefinite Matrix ◽  
Linear Least Squares ◽  
Alternating Direction ◽  
Semidefinite Matrix Completion ◽  
Semidefinite Matrix

Positive semidefinite matrix completion (PSDMC) aims to recover positive semidefinite and low-rank matrices from a subset of entries of a matrix. It is widely applicable in many fields, such as statistic analysis and system control. This task can be conducted by solving the nuclear norm regularized linear least squares model with positive semidefinite constraints. We apply the widely used alternating direction method of multipliers to solve the model and get a novel algorithm. The applicability and efficiency of the new algorithm are demonstrated in numerical experiments. Recovery results show that our algorithm is helpful.

Efficient completion for corrupted low-rank images via alternating direction method

2014 ◽  
Vol 23 (3) ◽  
pp. 033018 ◽  
Author(s):  
Wei Li ◽  
Lei Zhao ◽  
Duanqing Xu ◽  
Dongming Lu
Keyword(s):  
Alternating Direction Method ◽  
Low Rank ◽  
Alternating Direction

scholarly journals Millimeter-Wave Channel Estimation using Alternating Direction Method of Multipliers

2021 ◽  
Vol 10 (4) ◽  
pp. 239-242
Author(s):  
Aarab Mohamed Nassim ◽  
Chakkor Otman
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Mobile Networks ◽  
Mimo Systems ◽  
Matrix Completion ◽  
Mobile Network ◽  
Alternating Direction Method ◽  
Low Rank ◽  
Wave Channel ◽  
Alternating Direction

With the explosive growth in demand for mobile data traffic, the contradiction between capacity requirements and spectrum scarcity becomes more and more prominent. The bandwidth is becoming a key issue in 5G mobile networks. However, with the huge bandwidth from 30 GHz to 300 GHz, mmWave communications considered an important part of the 5G mobile network providing multi communication services, where channel state information considers a challenging task for millimeter wave MIMO systems due to the huge number of antennas. Therefore, this paper discusses the channel and signal models of the mmWave, with a novel formulation for mmWave channel estimation inclusive low rank features, that we improved using a developed theory of matrix completion with Alternating Direction Method.

scholarly journals Low rank alternating direction method of multipliers reconstruction for MR fingerprinting

2017 ◽  
Vol 79 (1) ◽  
pp. 83-96 ◽  
Author(s):  
Jakob Assländer ◽  
Martijn A. Cloos ◽  
Florian Knoll ◽  
Daniel K. Sodickson ◽  
Jürgen Hennig ◽  
...  
Keyword(s):  
Alternating Direction Method ◽  
Low Rank ◽  
Method Of Multipliers ◽  
Alternating Direction

scholarly journals Robust Subspace Discovery via Relaxed Rank Minimization

2014 ◽  
Vol 26 (3) ◽  
pp. 611-635 ◽  
Author(s):  
Xinggang Wang ◽  
Zhengdong Zhang ◽  
Yi Ma ◽  
Xiang Bai ◽  
Wenyu Liu ◽  
...  
Keyword(s):  
Input Data ◽  
Alternating Direction Method ◽  
Low Rank ◽  
High Dimensional ◽  
Effective Algorithm ◽  
Selection Problems ◽  
Alternating Direction ◽  
Label Information ◽  
The Face ◽  
New Formulation

This letter examines the problem of robust subspace discovery from input data samples (instances) in the presence of overwhelming outliers and corruptions. A typical example is the case where we are given a set of images; each image contains, for example, a face at an unknown location of an unknown size; our goal is to identify or detect the face in the image and simultaneously learn its model. We employ a simple generative subspace model and propose a new formulation to simultaneously infer the label information and learn the model using low-rank optimization. Solving this problem enables us to simultaneously identify the ownership of instances to the subspace and learn the corresponding subspace model. We give an efficient and effective algorithm based on the alternating direction method of multipliers and provide extensive simulations and experiments to verify the effectiveness of our method. The proposed scheme can also be used to tackle many related high-dimensional combinatorial selection problems.

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