Minimum rank Hermitian solution to the matrix approximation problem in the spectral norm and its application

DOI10.1016/j.aml.2019.06.012zbMath1420.15003OpenAlexW2953297372WikidataQ127682531 ScholiaQ127682531MaRDI QIDQ2275184

Xifu Liu

Publication date: 2 October 2019

Published in: Applied Mathematics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.aml.2019.06.012

zbMATH Keywords

minimum rankHermitian solutionmatrix approximation

Mathematics Subject Classification ID

Related Items (2)

Minimum rank positive semidefinite solution to the matrix approximation problem in the spectral norm ⋮ Norm-preserving dilation theorems for a block positive semidefinite (definite) matrix

Uses Software

• SLRA

Cites Work

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• Rank constrained matrix best approximation problem with respect to (skew) Hermitian matrices
• Low rank approximation. Algorithms, implementation, applications
• Rank constrained matrix best approximation problem
• A generalization of the Eckart-Young-Mirsky matrix approximation theorem
• Software for weighted structured low-rank approximation
• Minimum rank (skew) Hermitian solutions to the matrix approximation problem in the spectral norm
• On generalized matrix approximation problem in the spectral norm
• On least squares solutions subject to a rank restriction
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• On rank-constrained Hermitian nonnegative-definite least squares solutions to the matrix equationAXA^H=B
• The Smallest Perturbation of a Submatrix which Lowers the Rank and Constrained Total Least Squares Problems
• Minimum Rank Solutions to the Matrix Approximation Problems in the Spectral Norm
• Generalized Rank-Constrained Matrix Approximations

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