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Book
Regression and the Moore-Penrose pseudoinverse
Author:
ISBN: 1282290126 9786612290121 0080956033 9780080956039 9781282290129 0120484501 9780120484508 Year: 1972 Volume: 94 Publisher: New York (N.Y.): Academic press


Book
Dynamics of one-dimensional quantum systems
Authors: ---
ISBN: 9780511596827 9780521815987 9781107424722 9780511596421 0511596421 0521815983 0511596820 1107424720 1107195020 1282303155 9786612303159 0511596022 0511593651 0511592728 0511595581 Year: 2009 Publisher: Cambridge New York Cambridge University Press

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Abstract

One-dimensional quantum systems show fascinating properties beyond the scope of the mean-field approximation. However, the complicated mathematics involved is a high barrier to non-specialists. Written for graduate students and researchers new to the field, this book is a self-contained account of how to derive the exotic quasi-particle picture from the exact solution of models with inverse-square interparticle interactions. The book provides readers with an intuitive understanding of exact dynamical properties in terms of exotic quasi-particles which are neither bosons nor fermions. Powerful concepts, such as the Yangian symmetry in the Sutherland model and its lattice versions, are explained. A self-contained account of non-symmetric and symmetric Jack polynomials is also given. Derivations of dynamics are made easier, and are more concise than in the original papers, so readers can learn the physics of one-dimensional quantum systems through the simplest model.


Book
Projection matrices, generalized inverse matrices, and singular value decomposition
Authors: --- ---
ISBN: 1441998861 144199887X Year: 2011 Publisher: New York : Springer,

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Abstract

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 of one subspace onto another, and the latter underlies principal component analysis, which seeks to find a subspace that captures the largest variability in the original space. This book is about projections and SVD. A thorough discussion of generalized inverse (g-inverse) matrices is also given because it is closely related to the former. The book provides systematic and in-depth accounts of these concepts from a unified viewpoint of linear transformations finite dimensional vector spaces. More specially, it shows that projection matrices (projectors) and g-inverse matrices can be defined in various ways so that a vector space is decomposed into a direct-sum of (disjoint) subspaces. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition will be useful for researchers, practitioners, and students in applied mathematics, statistics, engineering, behaviormetrics, and other fields.

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