Linear Algebra and Its Applications, Global Edition

Learn key concepts of linear algebra to equip yourself in your studies and future career. Linear Algebra and Its Applications 6th edition by Steven R. Lay, Judi J. McDonald and David C. Lay is an excellent introductory guide to the principles and foundations of practical linear algebra. With its learner-friendly approach, the textbook starts with easier material, building confidence by introducing typically challenging concepts early on and gradually developing them. The book revisits those concepts throughout, ensuring you do not become overwhelmed when abstract concepts are introduced, as you progress with your learning. The latest edition provides new and revised content, with a range of features, including: A broad range of introductory vignettes, application examples, and online resources New material and topics to consolidate and enhance your understanding of the subject New, modernised applications to prepare your learning of the most innovative topics, such as machine learning, Artificial Intelligence, and digital signal processing With an array of exercises and questions to support your learning, this textbook provides the tools you need to build on your understanding of linear algebra and succeed in your studies. Also available with MyLab® Math MyLab is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab Math personalises the learning experience and improves results for each student. If you would like to purchase both the physical text and MyLab® Math, search for: 9781292351353 Linear Algebra and Its Applications, Global Edition, 6th edition plus MyLab Math with Pearson eText. Package consists of: 9781292351216 Corporate Finance, Global Edition, 5th Edition 9781292351285 Corporate Finance, Global Edition, 5th Edition MyLab® Math with Pearson eText MyLab® Math is not included. Students, if MyLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. MyLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information. This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content, which is especially relevant to students outside the United States.

Table of Contents:
About the Authors Preface A Note to Students Chapter 1 Linear Equations in LinearAlgebra 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 Ax= 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 Chapter 2 Matrix Algebra 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 ℝn 2.9 Dimension and Rank Projects Supplementary Exercises Chapter 3 Determinants 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 Chapter 4 Vector Spaces 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 Chapter 5 Eigenvalues and Eigenvectors 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 Chapter 6 Orthogonality and Least Squares 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 Chapter 7 Symmetric Matrices and Quadratic Forms 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 Chapter 8 The Geometry of Vector Spaces 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 Chapter 9 Optimization 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 Chapter 10 Finite-State Markov Chains(Online Only) 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 Appendixes Uniqueness of the Reduced Echelon Form Complex Numbers Credits Glossary Answers to Odd-Numbered Exercises Index