Numerical-Linear-Algebra

Numerical Linear Algebra, Trefethen and Bau, 1997.
My solutions to exercises in Numerical Linear Algebra by Trefethen and Bau.
There may be errors or wrong solutions, so use it only for reference.

Contents

I. Fundamentals

• Lecture 1. Matrix-Vector Multiplication
• Lecture 2. Orthogonal Vectors and Matrices
• Lecture 3. Norms
• Lecture 4. The Singular Value Decomposition
• Lecture 5. More on the SVD

II. QR Factorization and Least Squares

• Lecture 6. Projectors
• Lecture 7. QR Factorization
• Lecture 8. Gram-Schmidt Orthogonalization
• Lecture 9. MATLAB
• Lecture 10. Householder Triangularization
• Lecture 11. Least Squares Problems

III. Conditioning and Stability

• Lecture 12. Conditioning and Condition Numbers
• Lecture 13. Floating Point Arithmetic
• Lecture 14. Stability
• Lecture 15. More on Stability
• Lecture 16. Stability of Householder Triangularization
• Lecture 17. Stability of Back Substitution
• Lecture 18. Conditioning of Least Squares Problems
• Lecture 19. Stability of Least Squares Algorithms

IV. Systems of Equations

• Lecture 20. Gaussian Elimination
• Lecture 21. Pivoting
• Lecture 22. Stability of Gaussian Elimination
• Lecture 23. Cholesky Factorization

V. Eigenvalues

• Lecture 24. Eigenvalue Problems
• Lecture 25. Overview of Eigenvalue Algorithms
• Lecture 26. Reduction to Hessenberg or Tridiagonal Form
• Lecture 27. Rayleigh Quotient, Inverse Iteration
• Lecture 28. QR Algorithm without Shifts
• Lecture 29. QR Algorithm with Shifts
• Lecture 30. Other Eigenvalue Algorithms
• Lecture 31. Computing the SVD

VI. Iterative Methods

• Lecture 32. Overview of Iterative Methods
• Lecture 33. The Arnoldi Iteration
• Lecture 34. How Arnoldi Locates Eigenvalues
• Lecture 35. GMRES
• Lecture 36. The Lanczos Iteration
• Lecture 37. From Lanczos to Gauss Quadrature Skip
• Lecture 38. Conjugate Gradients
• Lecture 39. Biorthogonalization Methods Skip
• Lecture 40. Preconditioning