Suchergebnisse - "Proximal majorization-minimization algorithm"

An inexact proximal majorization-minimization algorithm for remote sensing image stripe noise removal

Autoren: Wang, Chengjing Zhao, Xile Wang, Qingsong et al.

Quelle: Numerical Algorithms. 97:1119-1139

Schlagwörter: 65K05, 90C26, 90C90, Image and Video Processing (eess.IV), FOS: Mathematics, FOS: Electrical engineering, electronic engineering, information engineering, Mathematics - Numerical Analysis, Numerical Analysis (math.NA), Electrical Engineering and Systems Science - Image and Video Processing

Zugangs-URL: http://arxiv.org/abs/2308.08866

A Proximal-Proximal Majorization-Minimization Algorithm for Nonconvex Rank Regression Problems

Autoren: Peipei Tang Chengjing Wang Bo Jiang

Quelle: IEEE Transactions on Signal Processing. 71:3502-3517

An inexact proximal majorization-minimization Algorithm for remote sensing image stripe noise removal

Autoren: Wang, Chengjing Zhao, Xile Wang, Qingsong et al.

Index Begriffe: Mathematics - Numerical Analysis, Electrical Engineering and Systems Science - Image and Video Processing, 65K05, 90C26, 90C90, text

URL: http://arxiv.org/abs/2308.08866

A proximal-proximal majorization-minimization algorithm for nonconvex tuning-free robust regression problems

Autoren: Tang, Peipei Wang, Chengjing Jiang, Bo

Index Begriffe: Mathematics - Optimization and Control, Computer Science - Machine Learning, Mathematics - Numerical Analysis, Statistics - Computation, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/2106.13683

A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems

Autoren: Tang, P Wang, C Sun, D et al.

Weitere Verfasser: Tang, P Wang, C Sun, D et al.

Schlagwörter: Semismooth Newton method, FOS: Computer and information sciences, Computer Science - Machine Learning, 0211 other engineering and technologies, Machine Learning (stat.ML), 02 engineering and technology, Numerical Analysis (math.NA), Statistics - Computation, 01 natural sciences, Machine Learning (cs.LG), Nonconvex square-root regression problems, Statistics - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, Proximal majorization-minimization, Mathematics - Numerical Analysis, 0101 mathematics, Mathematics - Optimization and Control, Computation (stat.CO)

Zugangs-URL: http://arxiv.org/abs/1903.11460

A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems

Autoren: Tang, Peipei Wang, Chengjing Sun, Defeng et al.

Index Begriffe: Mathematics - Optimization and Control, Computer Science - Machine Learning, Mathematics - Numerical Analysis, Statistics - Computation, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1903.11460

A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems

Autoren: Tang, P Wang, C Sun, D et al.

Weitere Verfasser: Tang, P Wang, C Sun, D et al.

Schlagwörter: Nonconvex square-root regression problems, Proximal majorization-minimization, Semismooth Newton method

Relation: http://hdl.handle.net/10397/98518; 38; 21; 226

Verfügbarkeit: http://hdl.handle.net/10397/98518

A Sparse Semismooth Newton Based Proximal Majorization-Minimization Algorithm for Nonconvex Square-Root-Loss Regression Problems.

Autoren: Peipei Tang Chengjing Wang Defeng Sun et al.

Quelle: Journal of Machine Learning Research. 2020, Vol. 20, p1-38. 38p.

Schlagwörter: *PROBLEM solving, *ALGORITHMS, *NEWTON-Raphson method

Nonconvex truncated conditional value at risk-based sparse linear regression.

Autoren: Xie, Boyi Wu, Zhongming Li, Min

Quelle: European Journal of Operational Research. Jan2026, Vol. 328 Issue 1, p246-257. 12p.

Schlagwörter: *VALUE at risk, *REGRESSION analysis, *ALGORITHMS, NEWTON-Raphson method, NP-hard problems

Adaptive sieving: a dimension reduction technique for sparse optimization problems.

Autoren: Yuan, Yancheng Lin, Meixia Sun, Defeng et al.

Quelle: Mathematical Programming Computation; Sep2025, Vol. 17 Issue 3, p585-616, 32p

Riemannian preconditioned algorithms for tensor completion via tensor ring decomposition.

Autoren: Gao, Bin Peng, Renfeng Yuan, Ya-xiang

Quelle: Computational Optimization & Applications; Jun2024, Vol. 88 Issue 2, p443-468, 26p

Schlagwörter: COST functions, RIEMANNIAN metric, ALGORITHMS, TENSOR products, GEODESICS

A FAST AND EFFECTIVE ALGORITHM FOR SPARSE LINEAR REGRESSION WITH lp-NORM DATA FIDELITY AND ELASTIC NET REGULARIZATION.

Autoren: YUNHAI XIAO JIAN SHEN YANYUN DING et al.

Quelle: Journal of Nonlinear & Variational Analysis; 2024, Vol. 8 Issue 3, p433-449, 17p

Schlagwörter: MATHEMATICAL regularization, REGRESSION analysis, RANDOM noise theory, ACCURACY, MULTIPLIERS (Mathematical analysis)

Generalized Newton Method with Positive Definite Regularization for Nonsmooth Optimization Problems with Nonisolated Solutions.

Autoren: Shi, Zijian Chao, Miantao

Quelle: Journal of Optimization Theory & Applications; Apr2024, Vol. 201 Issue 1, p396-432, 37p

Schlagwörter: NEWTON-Raphson method, NONSMOOTH optimization, LIPSCHITZ continuity, QUADRATIC programming, SYMMETRIC matrices, MATHEMATICAL regularization

Collaborative Optimal Configuration of a Mobile Energy Storage System and a Stationary Energy Storage System to Cope with Regional Grid Blackouts in Extreme Scenarios.

Autoren: Zhou, Weicheng Zhao, Ping Lu, Yifei

Quelle: Energies (19961073); Dec2023, Vol. 16 Issue 23, p7903, 17p

Schlagwörter: ELECTRIC power failures, ENERGY storage, ELECTRIC power distribution grids, STRUCTURAL optimization, RELIABILITY in engineering

A dual-based stochastic inexact algorithm for a class of stochastic nonsmooth convex composite problems.

Autoren: Lin, Gui-Hua Yang, Zhen-Ping Yin, Hai-An et al.

Quelle: Computational Optimization & Applications; Nov2023, Vol. 86 Issue 2, p669-710, 42p

Schlagwörter: NONSMOOTH optimization, STOCHASTIC approximation, NEWTON-Raphson method, ALGORITHMS, POINT set theory, STOCHASTIC programming

Color Image Recovery Using Generalized Matrix Completion over Higher-Order Finite Dimensional Algebra.

Autoren: Liao, Liang Guo, Zhuang Gao, Qi et al.

Quelle: Axioms (2075-1680); Oct2023, Vol. 12 Issue 10, p954, 23p

Schlagwörter: ALGEBRA, MATRICES (Mathematics), T-matrix, LOW-rank matrices, COLOR, NEIGHBORHOODS

An inexact proximal majorization–minimization method for a class of image reconstruction models

Autoren: Bujin Li Shaohua Pan Tieyong Zeng

Quelle: Inverse Problems. 41:065004

Sparse precision matrix estimation with missing observations.

Autoren: Zhang, Ning Yang, Jin

Quelle: Computational Statistics; Sep2023, Vol. 38 Issue 3, p1337-1355, 19p

Schlagwörter: MISSING data (Statistics), SPARSE matrices, CALMNESS, GRAPHICAL modeling (Statistics)

Convex and Nonconvex Risk-Based Linear Regression at Scale.

Autoren: Wu, Can Cui, Ying Li, Donghui et al.

Quelle: INFORMS Journal on Computing; Jul/Aug2023, Vol. 35 Issue 4, p797-816, 20p

Schlagwörter: VALUE at risk, RESEARCH grants, NONCONVEX programming, SELF-tuning controllers

A global two-stage algorithm for non-convex penalized high-dimensional linear regression problems.

Autoren: Li, Peili Liu, Min Yu, Zhou

Quelle: Computational Statistics; Jun2023, Vol. 38 Issue 2, p871-898, 28p

Schlagwörter: IMAGE reconstruction, ALGORITHMS, CONVEX functions, SIGNAL processing, DATA analysis