Computationally Efficient Decompositions of Oblique Projection Matrices | Zendy

Johannes J. Brust | Zendy; Roummel F. Marcia | Zendy; Cosmin G. Petra | Zendy

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Computationally Efficient Decompositions of Oblique Projection Matrices

Author(s) -

Johannes J. Brust,

Roummel F. Marcia,

Cosmin G. Petra

Publication year - 2020

Publication title -

siam journal on matrix analysis and applications

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 1.268

H-Index - 101

eISSN - 1095-7162

pISSN - 0895-4798

DOI - 10.1137/19m1288115

Subject(s) - singular value decomposition , oblique projection , mathematics , algorithm , eigendecomposition of a matrix , projection (relational algebra) , rank (graph theory) , oblique case , qr decomposition , computation , singular value , least squares function approximation , low rank approximation , eigenvalues and eigenvectors , linear least squares , signal processing , matrix decomposition , mathematical optimization , computer science , orthographic projection , combinatorics , mathematical analysis , geometry , telecommunications , linguistics , physics , statistics , philosophy , radar , quantum mechanics , estimator , hankel matrix

Oblique projection matrices arise in problems in weighted least squares, signal processing, and optimization. While these matrices can be potentially very large, their low-rank structure can be exp...

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