Math 2.0

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Numerical Linear Algebra and Application N7E8DJVKJF

URIm2://res/N7E8DJVKJF
Kindcourse
CreatorsSrinivasa Rao Pentyala (instructor)
Date2024-01-09
Scopein

Expressions

e1: edition

Languageen
Date2024-01-09
Contents
  • part 1: Week 1 :
  • session 1: Lecture 01 : Linear Algebra and Introduction
  • session 2: Lecture 02 : Computational Difficulties
  • session 3: Lecture 03 : Computational Error
  • session 4: Lecture 04 : Stability
  • session 5: Lecture 05: Gaussian Elimination
  • part 2: Week 2 :
  • session 6: Lecture 06 : LU Factorization
  • session 7: Lecture 07 : Iterative refinement
  • session 8: Lecture 08 : QR Factorization
  • session 9: Lecture 09 : Gram-Schmidt Orthogonalization
  • session 10: Lecture 10 : Cholesky Decomposition
  • part 3: Week 3 :
  • session 11: Lecture 11: Projections
  • session 12: Lecture 12 : House-Holder Reflectors
  • session 13: Lecture 13 : Image Compression
  • session 14: Lecture 14 : Singular Value Decomposition
  • session 15: Lecture 15 : Least Square Solutions
  • part 4: Week 4 :
  • session 16: Lecture 16 : Pseudo-Inverse
  • session 17: Lecture 17 : Normal Equations
  • session 18: Lecture 18 : Eigenvalue problems
  • session 19: Lecture 19 : Gershgorin Theorem
  • session 20: Lecture 20 : Similarity Transforms
  • part 5: Week 5 :
  • session 21: Lecture 21 : Eigenvalues
  • session 22: Lecture 22 : Sensitivity Vectors
  • session 23: Lecture 23 : Power method
  • session 24: Lecture 24 : Schur Decomposition
  • session 25: Lecture 25 : Jordan Canonical form
  • part 6: Week 6 :
  • session 26: Lecture 26 : QR Iteration
  • session 27: Lecture 27 : Heisenberg transformation
  • session 28: Lecture 28 : Rayleigh Quotient
  • session 29: Lecture 29 : Symmetric eigenvalue problem
  • session 30: Lecture 30 : Jacobi Method
  • part 7: Week 7 :
  • session 31: Lecture 31 : Divide and Conquer
  • session 32: Lecture 32 : Computing the Singular Value Decomposition
  • session 33: Lecture 33 : Golub-Kahan-Reinsch Algorithm
  • session 34: Lecture 34 : Chan SVD Algorithm
  • session 35: Lecture 35 : Generalized SVD
  • part 8: Week 8 :
  • session 36: Lecture 36 : Generalized and Quadratic Eigenvalue Problems
  • session 37: Lecture 37 : Generalized Schur Decomposition (QZ Decomposition)
  • session 38: Lecture 38 : Iterative Methods for Large Linear Systems: Jacobi
  • part 9: Week 9 :
  • session 39: Lecture 39 : Iterative methods for large linear systems: Gauss-Seidel method
  • session 40: Lecture 40 : Iterative methods for large linear systems: SOR method
  • session 41: Lecture 41 : Convergence of iterative algorithms
  • session 42: Lecture 42 : Krylov subspace methods
  • session 43: Lecture 43 : Lanczos
  • session 44: Lecture 44 : Arnoldi
  • session 45: Lecture 45 : Stability of the Cholesky QR Algorithm
  • part 10: Week 10 :
  • session 46: Lecture 46 : Conditioning of the eigenvalues
  • session 47: Lecture 47 : Symmetric definite pencil
  • session 48: Lecture 48 : AI applications
  • session 49: Lecture 49 : Sensitive systems
  • session 50: Lecture 50 : Real Life Systems
  • part 11: Week 11 :
  • session 51: Lecture 51 : Transient thermal systems
  • session 52: Lecture 52 : Left Inverse
  • session 53: Lecture 53 : Right Inverse
  • session 54: Lecture 54 : Generalized Inverse
  • session 55: Lecture 55 : Applications
  • part 12: Week 12 :
  • session 56: Lecture 56 : Applications (Contd.)
  • session 57: Lecture 57 : Applications (Contd.)
  • session 58: Lecture 58 : Applications (Contd.)
  • session 59: Lecture 59 : Applications (Contd.)
  • session 60: Lecture 60 : Applications of the Matrices in Real Life Systems
  • session 61: Lecture 61 : Matrices and Its Fundamentals: Recalling Examples
  • session 62: Lecture 62 : Properties of Matrices: Recalling and Revision, Examples
  • session 63: Lecture 63 : Matrices: Finite Digit Arithmetic: recalling and Examples

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