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Linear Algebra and Its Applications 5th Fifth Edition Hardcover By David Lay VG+
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- Nu kopenLinear Algebra and Its Applications 5th Fifth Edition Hardcover By David Lay VG+
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Specificaties
- Objectstaat
- Heel goed
- Opmerkingen van verkoper
- Release Year
- 2014
- Subject Area
- Mathematics
- Book Title
- Linear Algebra and Its Applications
- ISBN
- 9780321982384
- Publication Name
- Linear Algebra and Its Applications
- Item Length
- 0.4 in
- Publisher
- Pearson Education
- Subject
- Algebra / Linear, General
- Publication Year
- 2014
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 0.4 in
- Item Width
- 0.4 in
- Item Weight
- 43 Oz
- Number of Pages
- 576 Pages
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Product Information
NOTE: Before purchasing, check with your instructor to ensure you select the correct ISBN. Several versions of Pearson's MyLab & Mastering products exist for each title, and registrations are not transferable. To register for and use Pearson's MyLab & Mastering products, you may also need a Course ID, which your instructor will provide. Used books, rentals, and purchases made outside of Pearson If purchasing or renting from companies other than Pearson, the access codes for Pearson's MyLab & Mastering products may not be included, may be incorrect, or may be previously redeemed. Check with the seller before completing your purchase. Note: You are purchasing a standalone product; MyMathLab does not come packaged with this content. MyMathLab is not a self-paced technology and should only be purchased when required by an instructor. If you would like to purchase both the physical text and MyMathLab, search for: 9780134022697 / 0134022696 Linear Algebra and Its Applications plus New MyMathLab with Pearson eText -- Access Card Package, 5/e With traditional linear algebra texts, the course is relatively easy for students during the early stages as material is presented in a familiar, concrete setting. However, when abstract concepts are introduced, students often hit a wall. Instructors seem to agree that certain concepts (such as linear independence, spanning, subspace, vector space, and linear transformations) are not easily understood and require time to assimilate. These concepts are fundamental to the study of linear algebra, so students' understanding of them is vital to mastering the subject. This text makes these concepts more accessible by introducing them early in a familiar, concrete Rn setting, developing them gradually, and returning to them throughout the text so that when they are discussed in the abstract, students are readily able to understand.
Product Identifiers
Publisher
Pearson Education
ISBN-10
032198238x
ISBN-13
9780321982384
eBay Product ID (ePID)
12038782829
Product Key Features
Publication Name
Linear Algebra and Its Applications
Format
Hardcover
Language
English
Subject
Algebra / Linear, General
Publication Year
2014
Type
Textbook
Subject Area
Mathematics
Number of Pages
576 Pages
Dimensions
Item Length
0.4 in
Item Height
0.4 in
Item Width
0.4 in
Item Weight
43 Oz
Additional Product Features
Edition Number
5
LCCN
2014-011617
Lc Classification Number
Qa184.2.L39 2016
Table of Content
1. Linear Equations in Linear Algebra 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 Supplementary Exercises 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 Supplementary Exercises 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 Supplementary Exercises 4. Vector Spaces Introductory Example: Space Flight and 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 Rank 4.7 Change of Basis 4.8 Applications to Difference Equations 4.9 Applications to Markov Chains Supplementary Exercises 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 Supplementary Exercises 6. Orthogonality and Least Squares Introductory Example: The North American Datum and GPS Navigation 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 Applications to Linear Models 6.7 Inner Product Spaces 6.8 Applications of Inner Product Spaces Supplementary Exercises 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 Image Processing and Statistics Supplementary Exercises 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 9. Optimization (Online Only) Introductory Example: The Berlin Airlift 9.1 Matrix Games 9.2 Linear Programming--Geometric Method 9.3 Linear Programming--Simplex Method 9.4 Duality 10. Finite-State Markov Chains (Online Only) Introductory Example: Googling Markov Chains 10.1 Introduction and Examples 10.2 The Steady-State Vector and Google''s PageRank 10.3 Finite-State Markov Chains 10.4 Classification of States and Periodicity 10.5 The Fundamental Matrix 10.6 Markov Chains and Baseball Statistics Appendices A. Uniqueness of the Reduced Echelon Form B. Complex Numbers
Copyright Date
2016
Target Audience
College Audience
Dewey Decimal
512.5
Dewey Edition
22
Illustrated
Yes
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