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Introduction to Linear Algebra for Science and Engineering 3rd Edition by Dan Wolczuk, Daniel Norman ISBN 9780135278581 0135278589

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Product details:

ISBN 10: 0135278589
ISBN 13: 9780135278581
Author:  Dan Wolczuk, Daniel Norman

Note: You are purchasing a standalone product; MyLab Mathematics does not come packaged with this content. Students, if interested in purchasing this title with MyLab Mathematics, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information.

 

Norman/Wolczuk’s An Introduction to Linear Algebra for Science and Engineering has been widely respected for its unique approach, which helps students understand and apply theory and concepts by combining theory with computations and slowly bringing students to the difficult abstract concepts. This approach includes an early treatment of vector spaces and complex topics in a simpler, geometric context. An Introduction to Linear Algebra for Science and Engineering promotes advanced thinking and understanding by encouraging students to make connections between previously learned and new concepts and demonstrates the importance of each topic through applications.

Table of contents:

Chapter 1: Euclidean Vector Spaces

1.1. Vectors in R2 and R3

1.2. Spanning and Linear Independence in R2 and R3

1.3. Length and Angles in R2 and R3

1.4. Vectors in Rn

1.5. Dot Products and Projections in Rn

Chapter 2: Systems of Linear Equations

2.1. Systems of Linear Equations and Elimination

2.2. Reduced Row Echelon Form, Rank, and Homogeneous Systems

2.3. Application to Spanning and Linear Independence

2.4. Applications of Systems of Linear Equations

Chapter 3: Matrices, LinearMappings, and Inverses

3.1. Operations onMatrices

3.2. MatrixMappings and LinearMappings

3.3. Geometrical Transformations

3.4. Special Subspaces

3.5. InverseMatrices and InverseMappings

3.6. ElementaryMatrices

3.7. LU-Decomposition

Chapter 4: Vector Spaces

4.1. Spaces of Polynomials

4.2. Vector Spaces

4.3. Bases and Dimensions

4.4. Coordinates

4.5. General LinearMappings

4.6. Matrix of a LinearMapping

4.7. Isomorphisms of Vector Spaces

Chapter 5: Determinants

5.1. Determinants in Terms of Cofactors

5.2. Properties of the Determinant

5.3. Inverse by Cofactors, Cramer’s Rule

5.4. Area, Volume, and the Determinant

Chapter 6: Eigenvectors and Diagonalization

6.1. Eigenvalues and Eigenvectors

6.2. Diagonalization

6.3. Applications of Diagonalization

Chapter 7: Inner Products and Projections

7.1. Orthogonal Bases in Rn

7.2. Projections and the Gram-Schmidt Procedure

7.3. Method of Least Squares

7.4. Inner Product Spaces

7.5. Fourier Series

Chapter 8: SymmetricMatrices and Quadratic Forms

8.1. Diagonalization of SymmetricMatrices

8.2. Quadratic Forms

8.3. Graphs of Quadratic Forms

8.4. Applications of Quadratic Forms

8.5. Singular Value Decomposition

Chapter 9: Complex Vector Spaces

9.1. Complex Numbers

9.2. Systems with Complex Numbers

9.3. Complex Vector Spaces

9.4. Complex Diagonalization

9.5. Unitary Diagonalization

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