Description
Advanced Linear and Matrix Algebra, 1st ed. 2021
Author: Johnston Nathaniel
Language: English
Subject for Advanced Linear and Matrix Algebra:
Keywords
Second course in linear algebra textbook; Linear algebra textbook; Matrix algebra textbook; Matrix algebra vs linear algebra; Vector spaces; Linear transformation matrix; Isomorphism linear algebra; Projections linear algebra; Tensor products textbook; Matrix decomposition; Singular value decomposition; Jordan decomposition; Schur triangularization; Spectral decomposition; Kronecker product; Multilinear transformations; QR decomposition; Cholesky decomposition; Multilinearity; Multilinear transformations
Approximative price 58.01 €
In Print (Delivery period: 15 days).
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the book of Johnston Nathaniel
Publication date: 05-2022
494 p. · 17.8x25.4 cm · Paperback
Publication date: 05-2021
494 p. · 17.8x25.4 cm · Hardback
Description
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This textbook emphasizes the interplay between algebra and geometry to motivate the study of advanced linear algebra techniques. Matrices and linear transformations are presented as two sides of the same coin, with their connection motivating inquiry throughout the book. Building on a first course in linear algebra, this book offers readers a deeper understanding of abstract structures, matrix decompositions, multilinearity, and tensors. Concepts draw on concrete examples throughout, offering accessible pathways to advanced techniques.
Beginning with a study of vector spaces that includes coordinates, isomorphisms, orthogonality, and projections, the book goes on to focus on matrix decompositions. Numerous decompositions are explored, including the Shur, spectral, singular value, and Jordan decompositions. In each case, the author ties the new technique back to familiar ones, to create a coherent set of tools. Tensors and multilinearity complete the book, with a study of the Kronecker product, multilinear transformations, and tensor products. Throughout, ?Extra Topic? sections augment the core content with a wide range of ideas and applications, from the QR and Cholesky decompositions, to matrix-valued linear maps and semidefinite programming. Exercises of all levels accompany each section.Advanced Linear and Matrix Algebra offers students of mathematics, data analysis, and beyond the essential tools and concepts needed for further study. The engaging color presentation and frequent marginal notes showcase the author?s visual approach. A first course in proof-based linear algebra is assumed. An ideal preparation can be found in the author?s companion volume, Introduction to Linear and Matrix Algebra.
Nathaniel Johnston is an Associate Professor of Mathematics at Mount Allison University in New Brunswick, Canada. His research makes use of linear algebra, matrix analysis, and convex optimization to tackle questions related to the theory of quantum entanglement. His companion volume, Introduction to Linear and Matrix Algebra, is also published by Springer.
Motivates a deeper understanding of the abstract structures needed to tackle questions in mathematics, data analysis, and quantum information theory
Engages readers with a visual approach that uses color to enhance both content and learning
Features a wide selection of theoretical and applied topics to complement the core material
Incorporates exercises of all levels
Includes supplementary material: sn.pub/extras
