Least-squares orthogonalization using semidefinite programming

DOI10.1016/j.laa.2005.07.010zbMath1081.65034OpenAlexW2015800490WikidataQ114851470 ScholiaQ114851470MaRDI QIDQ2576234

Yonina C. Eldar

Publication date: 27 December 2005

Published in: Linear Algebra and its Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.laa.2005.07.010

zbMATH Keywords

semidefinite programmingnumerical examplesHilbert spaceorthogonalizationleast-squaresGram-Schmidt methodquantum detectiongeometric uniformity

Mathematics Subject Classification ID

Numerical solutions to overdetermined systems, pseudoinverses (65F20) Orthogonalization in numerical linear algebra (65F25)

Uses Software

• SeDuMi

Cites Work

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• Orthogonal and projected orthogonal matched filter detection
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• Geometrically uniform codes
• An optimal whitening approach to linear multiuser detection
• Optimal Detection of Symmetric Mixed Quantum States
• Matrix Analysis
• Ten Lectures on Wavelets
• Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones
• On quantum detection and the square-root measurement
• Optimal tight frames and quantum measurement
• Geometrically uniform frames
• Designing optimal quantum detectors via semidefinite programming
• Semidefinite Programming
• Orthogonal multiuser detection

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