- Series
- Pearson
- Author
- David,Lay
- Publisher
- Pearson
- Cover
- Softcover
- Edition
- 6
- Language
- English
- Total pages
- 672
- Pub.-date
- July 2021
- ISBN13
- 9781292351216
- ISBN
- 1292351217

ISBN | Product | Product | Price CHF | Available | |
---|---|---|---|---|---|

Linear Algebra and Its Applications, Global Edition |
9781292351216 Linear Algebra and Its Applications, Global Edition |
90.30 |

Support your students’ learning with this comprehensive textbook.

Linear Algebra and Its Applications, 6th edition offers a strong introduction with a variety of resources to support your teaching of elementary level concepts and principles, as well as aid in instilling confidence in students.

Also available with MyLab®Math.

- Starting with easier material and
**gradually developing**complex concepts, the book**ensures students do not hit a brick**wall later in their learning. - The text continually
**returns to difficult topics**, so students have more time to absorb and to review these critical concepts.

**Visualisation of Concepts**throughout the chapters help students to grasp major points with the use of geometric interpretation.- Carefully selected
**Practice Problems**before each exercise with complete set of solutions, focus on potential trouble spots in the exercise set or provide a “warm-up” for the exercises.

**New topics and applications**in this edition now prepare students with foundations for machine learning, artificial intelligence, data analysis, and digital signal processing.**A newly added Chapter**9, previously only available online, offers learning material on optimisation.**Reorganised chapters**updated to reflect the importance of certain topics show how they relate to one another, such as moving Markov Chains in chapter 4 to the section on Eigenvalues and Eigenvectors in chapter 5.

**End of chapter projects**added to this edition encourage students to drill deeper into their exploration of various topics, encouraging them to use creativity in problem solving.**'Reasonable Answers'**sections are now included, offering advice and helping students analyse whether their answers are consistent with data in the given questions.

**Introductory Example:**Linear Models in Economics and Engineering- 1.1 Systems of Linear Equations
- 1.2 Row Reduction and Echelon Forms
- 1.3 Vector Equations
- 1.4 The Matrix Equation
*A***x= b** - 1.5 Solution Sets of Linear Systems
- 1.6 Applications of Linear Systems
- 1.7 Linear Independence
- 1.8 Introduction to Linear Transformations
- 1.9 The Matrix of a Linear Transformation
- 1.10 Linear Models in Business,Science, and Engineering
- Projects
- Supplementary Exercises

**Introductory Example:**Computer Models in Aircraft Design- 2.1 Matrix Operations
- 2.2 The Inverse of a Matrix
- 2.3 Characterizations of Invertible Matrices
- 2.4 Partitioned Matrices
- 2.5 Matrix Factorizations
- 2.6 The Leontief Input—Output Model
- 2.7 Applications to Computer Graphics
- 2.8 Subspaces of R
^{n} - 2.9 Dimension and Rank
- Projects
- Supplementary Exercises

**Introductory Example:**Random Paths and Distortion- 3.1 Introduction to Determinants
- 3.2 Properties of Determinants
- 3.3 Cramer's Rule, Volume, and Linear Transformations
- Projects
- Supplementary Exercises

**Introductory Example:**Space Flightand Control Systems- 4.1 Vector Spaces and Subspaces
- 4.2 Null Spaces, Column Spaces,and Linear Transformations
- 4.3 Linearly Independent Sets; Bases
- 4.4 Coordinate Systems
- 4.5 The Dimension of a Vector Space
- 4.6 Change of Basis
- 4.7 Digital Signal Processing
- 4.8 Applications to Difference Equations
- Projects
- Supplementary Exercises

**Introductory Example:**Dynamical Systems and Spotted Owls- 5.1 Eigenvectors and Eigenvalues
- 5.2 The Characteristic Equation
- 5.3 Diagonalization
- 5.4 Eigenvectors and Linear Transformations
- 5.5 Complex Eigenvalues
- 5.6 Discrete Dynamical Systems
- 5.7 Applications to Differential Equations
- 5.8 Iterative Estimates for Eigenvalues
- 5.9 Markov Chains
- Projects
- Supplementary Exercises

**Introductory Example:**Artificial Intelligence and Machine Learning- 6.1 Inner Product, Length, and Orthogonality
- 6.2 Orthogonal Sets
- 6.3 Orthogonal Projections
- 6.4 The Gram—Schmidt Process
- 6.5 Least-Squares Problems
- 6.6 Machine Learning and LinearModels
- 6.7 Inner Product Spaces
- 6.8 Applications of Inner Product Spaces
- Projects
- Supplementary Exercises

**Introductory Example:**Multichannel Image Processing- 7.1 Diagonalization of Symmetric Matrices
- 7.2 Quadratic Forms
- 7.3 Constrained Optimization
- 7.4 The Singular Value Decomposition
- 7.5 Applications to ImageProcessing and Statistics
- Projects
- Supplementary Exercises

**Introductory Example:**The Platonic Solids- 8.1 Affine Combinations
- 8.2 Affine Independence
- 8.3 Convex Combinations
- 8.4 Hyperplanes
- 8.5 Polytopes
- 8.6 Curves and Surfaces
- Projects
- Supplementary Exercises

**Introductory Example:**The Berlin Airlift- 9.1 Matrix Games
- 9.2 Linear Programming–Geometric Method
- 9.3 Linear Programming–Simplex Method
- 9.4 Duality
- Projects
- Supplementary Exercises

**Introductory Example:**Googling Markov Chains- 10.1 Introduction and Examples
- 10.2 The Steady-State Vector andGoogle's PageRank
- 10.3 Communication Classes
- 10.4 Classification of States andPeriodicity
- 10.5 The Fundamental Matrix
- 10.6 Markov Chains and BaseballStatistics

- Uniqueness of the Reduced Echelon Form
- Complex Numbers

**David C. Lay**, University of Maryland–College Park

**Steven R. Lay**, Lee University

**Judi J. McDonald**, Washington State University