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Titlebook: Linear Algebra; Jin Ho Kwak,Sungpyo Hong Textbook 2004Latest edition Springer Science+Business Media New York 2004 Eigenvalue.Eigenvector.

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书目名称Linear Algebra
编辑Jin Ho Kwak,Sungpyo Hong
视频video
概述Presents the basic concepts of linear algebra as a coherent part of mathematics.Differs from competitors in its emphasis and integration of computational skills and mathematical abstractions.Variety o
图书封面Titlebook: Linear Algebra;  Jin Ho Kwak,Sungpyo Hong Textbook 2004Latest edition Springer Science+Business Media New York 2004 Eigenvalue.Eigenvector.
描述.A cornerstone of undergraduate mathematics, science, and engineering, this clear and rigorous presentation of the fundamentals of linear algebra is unique in its emphasis and integration of computational skills and mathematical abstractions. The power and utility of this beautiful subject is demonstrated, in particular, in its focus on linear recurrence, difference and differential equations that affect applications in physics, computer science, and economics....Key topics and features include:...* Linear equations, matrices, determinants, vector spaces, complex vector spaces, inner products, Jordan canonical forms, and quadratic forms..* Rich selection of examples and explanations, as well as a wide range of exercises at the end of every section..* Selected answers and hints..* Excellent index...This second edition includes substantial revisions, new material on minimal polynomials and diagonalization, as well as a variety of new applications. The text will serve theoretical and applied courses and is ideal for self-study. With its important approach to linear algebra as a coherent part of mathematics and as a vital component of the natural and social sciences, Linear Algebra, Se
出版日期Textbook 2004Latest edition
关键词Eigenvalue; Eigenvector; Matrix; Transformation; algebra; computer; computer science; linear algebra; matrix
版次2
doihttps://doi.org/10.1007/978-0-8176-8194-4
isbn_softcover978-0-8176-4294-5
isbn_ebook978-0-8176-8194-4
copyrightSpringer Science+Business Media New York 2004
The information of publication is updating

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Jordan Canonical Forms,s true in computing the power ., in solving a linear difference equation . = . or a linear differential equation .′(.) = .(.). In this chapter, we discuss how to solve the same problems for a non-diagonalizable matrix A by introducing the Jordan canonical form of a square matrix.
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https://doi.org/10.1007/978-0-8176-8194-4Eigenvalue; Eigenvector; Matrix; Transformation; algebra; computer; computer science; linear algebra; matrix
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Inner Product Spaces,To study a geometry ofa vector space, we go back to the case ofthe 3-space ℝ..
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