# Elementary Linear Algebra, Canadian 2nd edition

Stewart Venit, Wayne Bishop, and Jason Brown

## eBook

Access is contingent on use of this textbook in the instructor's classroom.

• Chapter 1: Geometry of R2 and R3
• 1.1: Vectors in R2 and R3 (10)
• 1.2: Dot and Cross Products (10)
• 1.3: Lines and Planes (10)
• 1.4: Application—Vectors and Interactive Multimedia
• 1: Self-Test
• 1: Review Exercises

• Chapter 2: Euclidean m-Space and Linear Equations
• 2.1: Euclidean m-Space (10)
• 2.2: Systems of Linear Equations (6)
• 2.3: Row Reduction of Linear Systems (14)
• 2.4: Application—Electric Circuits and Pipe Networks (3)
• 2.5: Application—Interpolating Polynomials (4)
• 2: Self-Test
• 2: Review Exercises

• Chapter 3: Matrices
• 3.1: Operations on Matrices (13)
• 3.2: Matrix Equations and Inverses (13)
• 3.3: Theory of Linear Systems (8)
• 3.4: LU Decomposition (7)
• 3.5: Elementary Matrices and Linear Systems (6)
• 3.6: Application—Least Square Polynomials (4)
• 3.7: Application—Social Networks
• 3.8: Application—Input-Output Analysis: Leontief Models (3)
• 3: Self-Test
• 3: Review Exercises

• Chapter 4: Determinants
• 4.1: Definition of a Determinant (12)
• 4.2: Properties of Determinants (10)
• 4.3: Cramer's Rule (7)
• 4.4: An Introduction to Eigenvalues (8)
• 4.5: Application—Markov Chains I: Theory (9)
• 4.6: Application—Markov Chains II: Google PageRank
• 4: Self-Test
• 4: Review Exercises

• Chapter 5: Independence and Basis in Rm
• 5.1: Linear Independence and Dependence (10)
• 5.2: Subspaces of Rm (8)
• 5.3: Basis and Dimension (12)
• 5.4: Rank of a Matrix (10)
• 5.5: Application—Signal Compression (2)
• 5: Self-Test
• 5: Review Exercises

• Chapter 6: Vector Spaces
• 6.1: Vector Spaces and Subspaces (9)
• 6.2: Linear Independence, Basis, and Dimension (12)
• 6.3: Coordinate Vectors (10)
• 6.4: Inner Product Spaces (10)
• 6.5: Vector Spaces over Other Fields (2)
• 6.6: Application—Approximation of Continuous Functions; Fourier Series (6)
• 6.7: Application—Music, Fourier Transforms, and A Hard Day's Night
• 6.8: Application—Fractals I: Self-Similarity, Projections, and Seeds
• 6: Self-Test
• 6: Review Exercises

• Chapter 7: Linear Transformations
• 7.1: Definition of a Linear Transformation (6)
• 7.2: Algebra of Linear Transformations (10)
• 7.3: Kernel and Image (10)
• 7.4: Matrix of a General Linear Transformation (9)
• 7.5: Change of Basis (7)
• 7.6: Application—Linear Differential Equations (10)
• 7.7: Application—Fractals II: Contractive Affine Maps and IFSs
• 7: Self-Test
• 7: Review Exercises

• Chapter 8: Eigenvalues and Eigenvectors
• 8.1: Definitions and Examples (9)
• 8.2: Diagonalization (10)
• 8.3: The QR Method for Approximating Eigenvalues (6)
• 8.4: Complex Eigenvalues and Eigenvectors (4)
• 8.5: Application—Powers of Matrices and Recurrence Relations
• 8.7: Application—Fractals III: When Is an Affine Map Contractive?
• 8: Self-Test
• 8: Review Exercises

• Chapter 9: Linear Programming
• 9.1: Introduction and Terminology (7)
• 9.2: The Simplex Algorithm (5)
• 9.3: Nonstandard LP Problems (2)
• 9.4: Theory of Linear Programming
• 9.5: Application—Supply and Demand
• 9: Self-Test
• 9: Review Exercises

## Questions Available within WebAssign

Most questions from this textbook are available in WebAssign. The online questions are identical to the textbook questions except for minor wording changes necessary for Web use. Whenever possible, variables, numbers, or words have been randomized so that each student receives a unique version of the question. This list is updated nightly.

##### Question Availability Color Key
BLACK questions are available now
GRAY questions are under development

Group Quantity Questions
Chapter 1: Geometry of R2 and R3
1.1 10 002 006 010 014 018 020 022 024 026 028
1.2 10 002 004 008 010 012 014 016 018 020 038
1.3 10 002 004 006 008 010 012 014 020 025 028
Chapter 2: Euclidean m-Space and Linear Equations
2.1 10 002 004 006 010 012 013 016 030 032 039
2.2 6 002 003 004 006 016 018
2.3 14 002 004 006 010 012 016 018 019 022 026 038 040 041 042
2.4 3 002 004 008
2.5 4 002 004 006 008
Chapter 3: Matrices
3.1 13 002 004 008 010 012 014 016 018 020 022 024 026 028
3.2 13 002 004 010 012 014 016 018 020 022 024 026 028 030
3.3 8 002 004 006 010 012 014 016 018
3.4 7 007 008 010 012 014 016 018
3.5 6 001 002 003 004 005 006
3.6 4 002 004 006 008
3.8 3 001 002 006
Chapter 4: Determinants
4.1 12 002 004 006 008 010 012 016 018 020 022 027 028
4.2 10 001 002 003 004 007 008 009 014 016 017
4.3 7 002 004 006 008 010 012 014
4.4 8 002 003 006 009 010 012 014 016
4.5 9 002 003 004 006 008 010 012 013 014
Chapter 5: Independence and Basis in Rm
5.1 10 006 010 012 014 016 018 020 022 024 026
5.2 8 013 014 015 016 019 021 023 024
5.3 12 002 004 006 008 010 012 013 014 022 024 026 028
5.4 10 002 003 004 006 008 010 012 014 016 018
5.5 2 003 004
Chapter 6: Vector Spaces
6.1 9 002 003 004 010 011 014 016 017 019
6.2 12 002 006 010 016 018 020 026 028 034 037 038 042
6.3 10 001 002 004 006 008 010 014 016 018 020
6.4 10 002 004 006 008 010 012 014 018 020 023
6.5 2 001 002
6.6 6 002 004 006 010 012 016
Chapter 7: Linear Transformations
7.1 6 010 012 014 016 022 024
7.2 10 002 004 006 008 010 012 014 016 018 020
7.3 10 002 004 006 008 010 016 020 030 032 034
7.4 9 001 002 004 005 006 009 010 013 016
7.5 7 002 004 006 008 010 012 014
7.6 10 002 006 008 012 014 018 020 022 026 028
Chapter 8: Eigenvalues and Eigenvectors
8.1 9 002 004 006 009 011 014 017 018 020
8.2 10 002 004 006 008 010 012 014 016 018 022
8.3 6 002 004 005 008 010 011
8.4 4 014 020 022 028
Chapter 9: Linear Programming
9.1 7 001 002 003 005 007 012 014
9.2 5 002 004 006 012 014
9.3 2 002 004
Total 353