Algorithm 358, singular value decomposition of complex matrix

Projection matrices, generalized inverse

📁 Projection matrices, generalized inverse matrices, and singular value decomposition

✍ Haruo Yanai, Kei Takeuchi, Yoshio Takane (auth.) 📂 Library 📅 2011 🏛 Springer-Verlag New York 🌐 English

Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic mechanism of multivariate analysis. The former underlies the least squares estimation in regression analysis, which is essentially a projection

Projection Matrices, Generalized Inverse

📁 Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition

✍ Haruo Yanai, Kei Takeuchi, Yoshio Takane (auth.) 📂 Library 📅 2011 🏛 Springer-Verlag New York 🌐 English

Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic mechanism of multivariate analysis. The former underlies the least squares estimation in regression analysis, which is essentially a projection

Matrix Computations and Semiseparable Ma

📁 Matrix Computations and Semiseparable Matrices - Eigenvalue and Singular Value Methods

✍ Raf Vandebril, Marc van Van Barel, Nicola Mastronardi 📂 Library 📅 2008 🏛 JHU 🌐 English

The general properties and mathematical structures of semiseparable matrices were presented in volume 1 of Matrix Computations and Semiseparable Matrices. In volume 2, Raf Vandebril, Marc Van Barel, and Nicola Mastronardi discuss the theory of structured eigenvalue and singular value computations

Matrix Computations and Semiseparable Ma

📁 Matrix Computations and Semiseparable Matrices: Eigenvalue and Singular Value Methods

✍ Raf Vandebril, Marc Van Barel, Nicola Mastronardi 📂 Library 📅 2008 🏛 Johns Hopkins University Press 🌐 English

The general properties and mathematical structures of semiseparable matrices were presented in volume 1 of Matrix Computations and Semiseparable Matrices. In volume 2, Raf Vandebril, Marc Van Barel, and Nicola Mastronardi discuss the theory of structured eigenvalue and singular value comp

Matrix Computations and Semiseparable Ma

📁 Matrix Computations and Semiseparable Matrices: Eigenvalue and Singular Value Methods (Volume 2)

✍ Raf Vandebril, Marc van Van Barel, Nicola Mastronardi 📂 Library 📅 2008 🏛 The Johns Hopkins University Press 🌐 English

The general properties and mathematical structures of semiseparable matrices were presented in volume 1 of Matrix Computations and Semiseparable Matrices. In volume 2, Raf Vandebril, Marc Van Barel, and Nicola Mastronardi discuss the theory of structured eigenvalue and singular value computations

Matrix algorithms, - Basic decomposition

📁 Matrix algorithms, - Basic decompositions

✍ G. W. Stewart 📂 Library 📅 1998 🏛 SIAM: Society for Industrial and Applied Mathemati 🌐 English

This thorough, concise, and superbly written volume is the first in a self-contained five-volume series devoted to matrix algorithms. It focuses on the computation of matrix decompositions - the factorization of matrices into products of similar ones. The first two chapters provide the required back