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Inertial proximal alternating minimization for nonconvex and nonsmooth problems [PDF]
Journal of Inequalities and Applications, 2017 In this paper, we study the minimization problem of the type L ( x , y ) = f ( x ) + R ( x , y ) + g ( y ) $L(x,y)=f(x)+R(x,y)+g(y)$ , where f and g are both nonconvex nonsmooth functions, and R is a smooth function we can choose.
Yaxuan Zhang, Songnian He
exaly +7 more sources
EURO Journal on Computational Optimization, 2016 We analyze the proximal alternating linearized minimization algorithm (PALM) for solving non-smooth convex minimization problems where the objective function is a sum of a smooth convex function and block separable non-smooth extended real-valued convex ...
Marc Teboulle, Ron Shefi
exaly +6 more sources
Graph regularized non-negative matrix factorization with $$L_{2,1}$$ L 2 , 1 norm regularization terms for drug–target interactions prediction [PDF]
BMC Bioinformatics, 2023 Background Identifying drug–target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are costly and time consuming. Effective computational methods to predict DTIs are useful to speed up the process of
Junjun Zhang, Minzhu Xie
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Sparse Non-negative Matrix Factorization Algorithm Based on Proximal Alternating Linearized Minimization [PDF]
Jisuanji gongcheng, 2019 This paper combinessparsity constraint and Proximal Alternating Linearized Minimization(PALM),proposes a Sparse Non-negative Matrix Factorization(SNMF) algorithm,called SNMF_PALM.The non-convex Smoothly Clipped Absolute Deviation(SCAD) function is used ...
WANG Jing,YANG Dan
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Quaternion Matrix Factorization for Low-Rank Quaternion Matrix Completion
Mathematics, 2023 The main aim of this paper is to study quaternion matrix factorization for low-rank quaternion matrix completion and its applications in color image processing.
Jiang-Feng Chen +3 more
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Constrained Total Generalized p-Variation Minimization for Few-View X-Ray Computed Tomography Image Reconstruction. [PDF]
PLoS ONE, 2016 Total generalized variation (TGV)-based computed tomography (CT) image reconstruction, which utilizes high-order image derivatives, is superior to total variation-based methods in terms of the preservation of edge information and the suppression of ...
Hanming Zhang +5 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 This article aims to solve the problem of the hyperspectral imagery (HSI) demosaicing under a novel subsampling hyperspectral sensing strategy. The existing method utilizes the periodic structure of subsampling to estimate a fixed subspace in matrix form
Shan-Shan Xu +3 more
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On the Convergence of Bregman ADMM With Variational Inequality
IEEE Access, 2020 The alternating direction method of multipliers (ADMM) is one of most foundational algorithms for linear constrained composite minimization problems. For different specific problems, variations of ADMM (like linearized ADMM, proximal ADMM) are developed.
Peibing Du, Hao Jiang
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Ptychographic phase retrieval by proximal algorithms
New Journal of Physics, 2020 We derive a set of ptychography phase-retrieval iterative engines based on proximal algorithms originally developed in convex optimization theory, and discuss their connections with existing ones.
Hanfei Yan
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Sparse Blind Deconvolution with Nonconvex Optimization for Ultrasonic NDT Application
Sensors, 2020 In the field of ultrasonic nondestructive testing (NDT), robust and accurate detection of defects is a challenging task because of the attenuation and noising of the ultrasonic wave from the structure.
Xuyang Gao +4 more
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