Matrix Algebra with Computational Applications by Dirk Colbry
Matrix Algebra with Computational Applications - Table of Contents
- 1. Matrix Algebra Preparation Checklist
- 2. Welcome to Computational Matrix Algebra
- 3. Vectors (Part 1)
- 4. Vectors (Part 2)
- 5. Linear Equations
- 6. Solving Linear Systems of Equations
- 7. Python Linear Algebra Packages
- 8. Linear Algebra and Python
- 9. Gauss-Jordan Elimination
- 10. Gauss-Jordan Implementation
- 11. Matrix Mechanics
- 12. Matrix Multiplication
- 13. Transformation Matrix
- 14. Linear Transformations
- 15. Robotics and Reference Frames
- 16. Kinematics of Robotics
- 17. Determinants
- 18. Determinants Properties
- 19. Eigenvectors and Eigenvalues
- 20. Eigenproblems
- 21. Vector Spaces
- 22. Vector Spaces Properties
- 23. Matrix Spaces
- 24. Matrix Representation
- 25. Projections
- 26. Orthogonal Projections
- 27. Fundamental Spaces
- 28. Subspaces and Fundamental Spaces
- 29. Diagonalization and Powers
- 30. Diagonalization Applications
- 31. Linear Dynamical Systems
- 32. Dynamical Systems Analysis
- 33. Matrix Decompositions
- 34. Decompositions and Gaussian Elimination
- 35. Inner Product
- 36. Inner Product Spaces
- 37. Least Squares Fit (Regression)
- 38. Least Squares Fit (LSF Methods)
- 39. Least Squares Fit Applications
- 40. Numerical Linear Systems Solvers
- 41. QR Decomposition Solvers
- 42. Advanced Python Linear Algebra
- 43. Jupyter Getting Started Guide
- 44. Python Packages Reference
What You Will Learn in Matrix Algebra with Computational Applications
Matrix Algebra with Computational Applications by Dirk Colbry is an innovative, open-access textbook designed to integrate fundamental linear algebra with modern numerical programming and Python computations. Widely utilized in matrix algebra with computational applications courses, this text bridges abstract mathematical theory and real-world scientific computing.
The book systematically explores systems of linear equations, Gauss-Jordan elimination, matrix algebra, vector spaces, determinants, eigenvalues, eigenvectors, and singular value decomposition (SVD). Studying the matrix algebra with computational applications Dirk Colbry pdf equips students with practical coding techniques alongside theoretical proofs to solve complex data science and engineering models.
Praised by STEM educators for its interactive computational notebooks and hands-on problem sets, this volume prepares students for advanced scientific programming. Mastering matrix algebra with computational applications delivers essential analytical and algorithmic skills for modern computational fields.
Book Details & Specifications
Title:
Matrix Algebra with Computational Applications by Dirk Colbry
Publisher:
Michigan State University Libraries
Year:
2021
Pages:
110
Type:
PDF
Language:
English
ISBN-10 #:
3319648667
ISBN-13 #:
978-3319648668
License:
CC BY-NC 4.0
Amazon:
Amazon
About the Author: Dr. Dirk Colbry
The author Dr. Dirk Colbry
is a Faculty Member in the Department of Computational Mathematics, Science, and Engineering (CMSE) at Michigan State University.
He is a pioneer in computational pedagogy, developing open educational resources in matrix algebra with computational applications and high-performance computing.
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