Numerical Analysis
Offered By: Vidyasagar University via Swayam
Course Description
Overview
Syllabus
Week 1:Errors in Numerical Computations
1. Ch 1 Mod 1:Error in Numerical Computations.
2. Ch 1 Mod 2:Propagation of Errors and Computer Arithmetic.
Week 2:Interpolation - I
3. Ch 1 Mod 3:Operators in Numerical Analysis.
4. Ch 2 Mod 1: Lagrange’s. Interpolation.
5. Ch 2 Mod 2: Newton’s Interpolation Methods.
6. Ch 2 Mod 3: Central Deference Interpolation Formulae.
Week 3:Interpolation - II
7. Ch 2 Mod 4:Aitken’s and Hermite’s Interpolation Methods.
8. Ch 2 Mod 5: Spline Interpolation.
9. Ch 2 Mod 6:Inverse Interpolation.
10. Ch 2 Mod 7:Bivariate Interpolation.
Week 4:Approximation of Functions
11. Ch 3 Mod 1: Least Squares Method.
12. Ch 3 Mod 2:Approximation of Function by Least Squares Method.
13. Ch 3 Mod 3: Approximation of Function by Chebyshev Polynomials.
Week 5:Solution of Algebraic andTranscendental Equation
14. Ch 4 Mod 1:Newton’s Method to Solve Transcendental Equation.
15. Ch 4 Mod 2: Roots of a Polynomial Equation.
16. Ch 4 Mod 3: Solution of System of Non-linear Equations.
Week 6:Solution of System of Linear Equations-I
17. Ch 5 Mod 1:Matrix Inverse Method.
18. Ch 5 Mod 2:Iteration Methods to Solve System of Linear Equations.
19. Ch 5 Mod 3: Methods of Matrix Factorization.
Week 7:Solution of System of Linear Equations-II
20. Ch 5 Mod 4:Gauss Elimination Method and Tri-diagonal Equations.
21. Ch 5 Mod 5: Generalized Inverse of Matrix.
22. Ch 5 Mod 6: Solution of Inconsistent and Ill Conditioned Systems.
Week 8:Assessment
Week 9:Eigenvalues and Eigenvectors of Matrices
23. Ch 6 Mod 1:Construction of Characteristic Equation of a Matrix.
24. Ch 6 Mod 2:Eigenvalue and Eigenvector of Arbitrary Matrices.
25. Ch 6 Mod 3: Eigenvalues and Eigenvectors of Symmetric Matrices.
Week 10:Differentiation and Integration-I
26. Ch 7 Mod 1:Numerical Differentiation.
27. Ch 7 Mod 2:Newton-Cotes Quadrature.
Week 11:Differentiation and Integration-II
28. Ch 7 Mod 3:Gaussian Quadrature.
29. Ch 7 Mod 4: Monte-Carlo Method and Double Integration.
Week 12:Ordinary Differential Equations-I
30. Ch 8 Mod 1:Runge-Kutta Methods.
31. Ch 8 Mod 2:Predictor-Corrector Methods.
Week 13:Ordinary Differential Equations-II
32. Ch 8 Mod 3:Finite Difference Method and its Stability.
33. Ch 8 Mod 4: Shooting Method and Stability Analysis.
Week 14:Partial Differential Equations
34. Ch 9 Mod 1:Partial Differential Equation: Parabolic.
35. Ch 9 Mod 2:Partial Differential Equations: Hyperbolic.
36. Ch 9 Mod 3:Partial Differential Equations: Elliptic
Week 15:Final examination
Taught by
Prof. Madhumangal Pal
Tags
Related Courses
Analyse numérique pour ingénieursÉcole Polytechnique Fédérale de Lausanne via Coursera Computer App in Engineering
Cabrillo College via California Community Colleges System Introduction to Optimization in Python
DataCamp Single Variable Calculus
University of Pennsylvania via Coursera Linear Differential Equations
Boston University via edX