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Principles of Linear Algebra With Maple [Hardback]

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An accessible introduction to the theoretical and computational aspects of linear algebra using MapleTM

Many topics in linear algebra can be computationally intensive, and software programs often serve as important tools for understanding challenging concepts and visualizing the geometric aspects of the subject. Principles of Linear Algebra with Maple uniquely addresses the quickly growing intersection between subject theory and numerical computation, providing all of the commands required to solve complex and computationally challenging linear algebra problems using Maple. The authors supply an informal, accessible, and easy-to-follow treatment of key topics often found in a first course in linear algebra.

Requiring no prior knowledge of the software, the book begins with an introduction to the commands and programming guidelines for working with Maple. Next, the book explores linear systems of equations and matrices, applications of linear systems and matrices, determinants, inverses, and Cramer's rule. Basic linear algebra topics such as vectors, dot product, cross product, and vector projection are explained, as well as the more advanced topics of rotations in space, rolling a circle along a curve, and the TNB Frame. Subsequent chapters feature coverage of linear transformations from Rn to Rm, the geometry of linear and affine transformations, least squares fits and pseudoinverses, and eigenvalues and eigenvectors.

The authors explore several topics that are not often found in introductory linear algebra books, including sensitivity to error and the effects of linear and affine maps on the geometry of objects. The Maple software highlights the topic's visual nature, as the book is complete with numerous graphics in two and three dimensions, animations, symbolic manipulations, numerical computations, and programming. In addition, a related Web site features supplemental material, including Maple code for each chapter's problems, solutions, and color versions of the book's figures.

Extensively class-tested to ensure an accessible presentation, Principles of Linear Algebra with Maple is an excellent book for courses on linear algebra at the undergraduate level. It is also an ideal reference for students and professionals who would like to gain a further understanding of the use of Maple to solve linear algebra problems.

Recenzijas

Preface ix
Conventions and Notations xiv
1 An Introduction To Maple™
1(14)
1.1 The Commands
2(9)
1.2 Programming
11(4)
2 Linear Systems of Equations and Matrices
15(36)
2.1 Linear Systems of Equations
15(13)
2.2 Augmented Matrix of a Linear System and Row Operations
28(11)
2.3 Some Matrix Arithmetic
39(12)
3 Gauss-Jordan Elimination and Reduced Row Echelon Form
51(38)
3.1 Gauss-Jordan Elimination and rref
51(14)
3.2 Elementary Matrices
65(9)
3.3 Sensitivity of Solutions to Error in the Linear System
74(15)
4 Applications of Linear Systems and Matrices
89(40)
4.1 Applications of Linear Systems to Geometry
89(10)
4.2 Applications of Linear Systems to Curve Fitting
99(8)
4.3 Applications of Linear Systems to Economics
107(5)
4.4 Applications of Matrix Multiplication to Geometry
112(8)
4.5 An Application of Matrix Multiplication to Economics
120(9)
5 Determinants, Inverses, and Cramer's Rule
129(66)
5.1 Determinants and Inverses from the Adjoint Formula
129(18)
5.2 Determinants by Expanding Along Any Row or Column
147(12)
5.3 Determinants Found by Triangularizing Matrices
159(12)
5.4 LU Factorization
171(8)
5.5 Inverses from rref
179(5)
5.6 Cramer's Rule
184(11)
6 Basic Linear Algebra Topics
195(50)
6.1 Vectors
195(15)
6.2 Dot Product
210(13)
6.3 Cross Product
223(9)
6.4 Vector Projection
232(13)
7 A Few Advanced Linear Algebra Topics
245(26)
7.1 Rotations in Space
245(10)
7.2 "Rolling" a Circle Along a Curve
255(10)
7.3 The TNB Frame
265(6)
8 Independence, Basis, and Dimension for Subspaces of Rn
271(62)
8.1 Subspaces of Rn
271(18)
8.2 Independent and Dependent Sets of Vectors in Rn
289(13)
8.3 Basis and Dimension for Subspaces of Rn
302(9)
8.4 Vector Projection onto a Subspace of Rn
311(11)
8.5 The Gram-Schmidt Orthonormalization Process
322(11)
9 Linear Maps from Rn to Rm
333(42)
9.1 Basics About Linear Maps
333(12)
9.2 The Kernel and Image Subspaces of a Linear Map
345(9)
9.3 Composites of Two Linear Maps and Inverses
354(7)
9.4 Change of Bases for the Matrix Representation of a Linear Map
361(14)
10 The Geometry of Linear and Affine Maps
375(60)
10.1 The Effect of a Linear Map on Area and Arclength in Two Dimensions
375(18)
10.2 The Decomposition of Linear Maps into Rotations, Reflections, and Rescalings in R2
393(8)
10.3 The Effect of Linear Maps on Volume, Area, and Arclength in R3
401(11)
10.4 Rotations, Reflections, and Rescalings in Three Dimensions
412(11)
10.5 Affine Maps
423(12)
11 Least-Squares Fits and Pseudoinverses
435(38)
11.1 Pseudoinverse to a Nonsquare Matrix and Almost Solving an Overdetermined Linear System
435(11)
11.2 Fits and Pseudoinverses
446(16)
11.3 Least-Squares Fits and Pseudoinverses
462(11)
12 Eigenvalues and Eigenvectors
473(116)
12.1 What Are Eigenvalues and Eigenvectors, and Why Do We Need Them?
473(15)
12.2 Summary of Definitions and Methods for Computing Eigenvalues and Eigenvectors as well as the Exponential of a Matrix
488(4)
12.3 Applications of the Diagonalizability of Square Matrices
492(17)
12.4 Solving a Square First-Order Linear System of Differential Equations
509(42)
12.5 Basic Facts About Eigenvalues, Eigenvectors, and Diagonalizability
551(14)
12.6 The Geometry of the Ellipse Using Eigenvalues and Eigenvectors
565(20)
12.7 A Maple Eigen-Procedure
585(4)
Suggested Reading
589(2)
Indices
591
Keyword Index
591(4)
Index of Maple Commands and Packages
595
Kenneth Shiskowski, PhD, is Professor of Mathematics at Eastern Michigan University. His areas of research interest include numerical analysis, the history of mathematics, the integration of technology into mathematics, differential geometry, and dynamical systems.

Karl H. Frinkle, PhD, is Associate Professor of Mathematics at Southeastern Oklahoma State University. He has extensive academic experience teaching in the areas of algebra, trigonometry, and calculus. Dr. Frinkle currently focuses his research on Bose-Einstein condensates, nonlinear optics, dynamical systems, and the integration of technology into mathematics.