Basket (0)
Wishlist (0)
Your personal newsletter
__

Introduction To Linear Algebra: Computation, Application, and Theory
 
Product details:
ISBN13:9781032109381
ISBN10:1032109386
Binding:Paperback
No. of pages:434 pages
Size:254x178 mm
Weight:800 g
Language:English
Illustrations: 66 Illustrations, black & white; 1 Halftones, black & white; 65 Line drawings, black & white
0
Category:

Introduction To Linear Algebra

Computation, Application, and Theory
 
Edition number: 1
Publisher: Chapman and Hall
Date of Publication:
 
Normal price:
Publisher's listprice:
GBP 45.99
Estimated price in HUF:
22 213 HUF (21 155 HUF + 5% VAT)
Why estimated?
 
Your price:
17 770 (16 924 HUF + 5% VAT )
discount is: 20% (approx 4 443 HUF off)
Discount is valid until: 30 June 2024
The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
Click here to subscribe.
 
Availability:
 
Add to basket
  Piece(s)

 
Short description:

This book is designed for students who have never been exposed to the topics in a linear algebra course. The text is ?lled with interesting and diverse application sections but is also a theoretical text which aims to train students to do succinct computation in a knowledgeable way.


 

Long description:

Introduction to Linear Algebra: Computation, Application, and Theory is designed for students who have never been exposed to the topics in a linear algebra course. The text is ?lled with interesting and diverse application sections but is also a theoretical text which aims to train students to do succinct computation in a knowledgeable way. After completing the course with this text, the student will not only know the best and shortest way to do linear algebraic computations but will also know why such computations are both e?ective and successful.


Features:




  • Includes cutting edge applications in machine learning and data analytics



  • Suitable as a primary text for undergraduates studying linear algebra



  • Requires very little in the way of pre-requisites


"Exceptionally well organized and thoroughly 'student friendly' in presentation, Introduction To Linear Algebra: Computation, Application, and Theory is an ideal textbook for highschool, college, and university curriculums"
- Midwest Books Review
Table of Contents:

1. Examples of Vector Spaces. 1.1. First Vector Space: Tuples. 1.2. Dot Product. 1.3. Application: Geometry. 1.4. Second Vector Space: Matrices. 1.5. Matrix Multiplication. 2. Matrices and Linear Systems. 2.1. Systems of Linear Equations. 2.2. Gaussian Elimination. 2.3. Application: Markov Chains. 2.4. Application: The Simplex Method. 2.5. Elementary Matrices and Matrix Equivalence. 2.6. Inverse of a Matrix. 2.7. Application: The Simplex Method Revisited. 2.8. Homogeneous/Nonhomogeneous Systems and Rank. 2.9. Determinant. 2.10. Applications of the Determinant. 2.11. Application: Lu Factorization. 3. Vector Spaces. 3.1. Definition and Examples. 3.2. Subspace. 3.3. Linear Independence. 3.4. Span. 3.5. Basis and Dimension. 3.6. Subspaces Associated with a Matrix. 3.7. Application: Dimension Theorems. 4. Linear Transformations. 4.1. Definition and Examples. 4.2. Kernel and Image. 4.3. Matrix Representation. 4.4. Inverse and Isomorphism. 4.5. Similarity of Matrices. 4.6. Eigenvalues and Diagonalization. 4.7. Axiomatic Determinant. 4.8. Quotient Vector Space. 4.9. Dual Vector Space. 5. Inner Product Spaces. 5.1. Definition, Examples and Properties. 5.2. Orthogonal and Orthonormal. 5.3. Orthogonal Matrices. 5.4. Application: QR Factorization. 5.5. Schur Triangularization Theorem. 5.6. Orthogonal Projections and Best Approximation. 5.7. Real Symmetric Matrices. 5.8. Singular Value Decomposition. 5.9. Application: Least Squares Optimization. 6. Applications in Data Analytics. 6.1. Introduction. 6.2. Direction of Maximal Spread. 6.3. Principal Component Analysis. 6.4. Dimensionality Reduction. 6.5. Mahalanobis Distance. 6.6. Data Sphering. 6.7. Fisher Linear Discriminant Function. 6.8. Linear Discriminant Functions in Feature Space. 6.9. Minimal Square Error Linear Discriminant Function. 7. Quadratic Forms. 7.1. Introduction to Quadratic Forms. 7.2. Principal Minor Criterion. 7.3. Eigenvalue Criterion. 7.4. Application: Unconstrained Nonlinear Optimization. 7.5. General Quadratic Forms. Appendix A. Regular Matrices. Appendix B. Rotations and Reflections in Two Dimensions. Appendix C. Answers to Selected Exercises.