



LEADER 
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180604  eng 
020 


a 9783319910413

100 
1 

a Olver, Peter J.

245 
0 
0 
a Applied Linear Algebra
h Elektronische Ressource
c by Peter J. Olver, Chehrzad Shakiban

250 


a 2nd ed. 2018

260 


a Cham
b Springer International Publishing
c 2018, 2018

300 


a XXV, 679 p. 130 illus., 88 illus. in color
b online resource

505 
0 

a Preface  1. Linear Algebraic Systems  2. Vector Spaces and Bases  3. Inner Products and Norms  4. Minimization and Least Squares Approximation  5. Orthogonality  6. Equilibrium  7. Linearity  8. Eigenvalues  9. Linear Dynamical Systems  10. Iteration of Linear Systems  11. Boundary Value Problems in One Dimension  References  Index

653 


a Linear and Multilinear Algebras, Matrix Theory

653 


a Algebra

653 


a Mathematical physics

653 


a Matrix theory

653 


a Mathematical Applications in the Physical Sciences

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1 

a Shakiban, Chehrzad
e [author]

710 
2 

a SpringerLink (Online service)

041 
0 
7 
a eng
2 ISO 6392

989 


b Springer
a Springer eBooks 2005

490 
0 

a Undergraduate Texts in Mathematics

856 


u https://doi.org/10.1007/9783319910413?nosfx=y
x Verlag
3 Volltext

082 
0 

a 512.5

520 


a This textbook develops the essential tools of linear algebra, with the goal of imparting technique alongside contextual understanding. Applications go handinhand with theory, each reinforcing and explaining the other. This approach encourages students to develop not only the technical proficiency needed to go on to further study, but an appreciation for when, why, and how the tools of linear algebra can be used across modern applied mathematics. Providing an extensive treatment of essential topics such as Gaussian elimination, inner products and norms, and eigenvalues and singular values, this text can be used for an indepth first course, or an applicationdriven second course in linear algebra. In this second edition, applications have been updated and expanded to include numerical methods, dynamical systems, data analysis, and signal processing, while the pedagogical flow of the core material has been improved. Throughout, the text emphasizes the conceptual connections between each application and the underlying linear algebraic techniques, thereby enabling students not only to learn how to apply the mathematical tools in routine contexts, but also to understand what is required to adapt to unusual or emerging problems. No previous knowledge of linear algebra is needed to approach this text, with singlevariable calculus as the only formal prerequisite. However, the reader will need to draw upon some mathematical maturity to engage in the increasing abstraction inherent to the subject. Once equipped with the main tools and concepts from this book, students will be prepared for further study in differential equations, numerical analysis, data science and statistics, and a broad range of applications. The first author’s text, Introduction to Partial Differential Equations, is an ideal companion volume, forming a natural extension of the linear mathematical methods developed here
