Nathaniel Johnston
人物简介:
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.
Advanced Linear and Matrix Algebra书籍相关信息
- ISBN:9783030528140
- 作者:Nathaniel Johnston
- 出版社:Springer
- 出版时间:2021-5-20
- 页数:494
- 价格:暂无价格
- 纸张:暂无纸张
- 装帧:暂无装帧
- 开本:暂无开本
- 语言:暂无语言
- 适合人群:Mathematics Students, Engineering Students, Data Scientists, Computer Scientists, Physicists, Economists, anyone interested in advanced mathematics and its applications
- TAG:Data Science / linear algebra / matrix theory / Abstract Algebra / Engineering Mathematics / Mathematical Methods
- 豆瓣评分:暂无豆瓣评分
- 更新时间:2025-05-18 15:26:40
内容简介:
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.
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