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Convergence analysis on a modified generalized alternating direction method of multipliers
Journal of Inequalities and Applications, 2018 The alternating direction method of multipliers (ADMM) is one of the most powerful and successful methods for solving convex composite minimization problem.
Sha Lu, Zengxin Wei
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IET Intelligent Transport Systems, 2023 Spatiotemporal traffic data usually suffers from missing entries in the data acquisition and transmission process. Existing imputation methods only consider the global/local structure of spatiotemporal traffic data, resulting in insufficient estimation ...
Peng‐Ling Wu +2 more
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Journal of Inequalities and Applications, 2017 The proximal alternating direction method of multipliers (P-ADMM) is an efficient first-order method for solving the separable convex minimization problems. Recently, He et al.
Min Sun, Jing Liu
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Overcomplete Transform Learning With the
IEEE Access, 2018 Transform learning has been proposed as a new and effective formulation for analysis dictionary learning, where the ℓ0 norm or the ℓ1 norm are generally used as sparsity constraint.
Zhenni Li +3 more
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Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization [PDF]
International Journal of Computer Vision, 2014 Published at ...
Yu-Xiang Wang 0003 +3 more
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Alternating Minimization, Proximal Minimization and Optimization Transfer Are Equivalent
, 2015 We show that proximal minimization algorithms (PMA), majorization minimization (MM), and alternating minimization (AM) are equivalent. Each type of algorithm leads to a decreasing sequence of objective function. New conditions on PMA are given (the limit of the decreasing sequence of objective function is indeed the infimum of the objective function ...
Byrne, Charles L., Lee, Jong Soo
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Remote Sensing, 2023 Hyperspectral image (HSI) super-resolution is a vital technique that generates high spatial-resolution HSI (HR-HSI) by integrating information from low spatial-resolution HSI with high spatial-resolution multispectral image (MSI).
Yidong Peng +3 more
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A Regular k-Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise
Applied Computational Intelligence and Soft Computing, 2017 The removal of mixed Gaussian-impulse noise plays an important role in many areas, such as remote sensing. However, traditional methods may be unaware of promoting the degree of the sparsity adaptively after decomposing into low rank component and sparse
Han Pan +3 more
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Research on Hyperspectral Image Super-resolution Methods Based on Tensor Ring SubspaceSmoothing and Graph Regularization [PDF]
Jisuanji kexueRegarding existing classical matrix decomposition models,they may lead to the loss of three-dimensional data structure information,especially when affected by noise pollution,resulting in a significant decrease in the quality of reconstructed images,this
YANG Feixia, LI Zheng, MA Fei
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Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
Applied SciencesRecovering incomplete high-dimensional data to create complete and valuable datasets is the main focus of tensor completion research, which lies at the intersection of mathematics and information science.
Yifan Mei +3 more
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