| Module Name | Download |
|---|---|
| noc18_ma13_Assignment1 | noc18_ma13_Assignment1 |
| noc18_ma13_Assignment10 | noc18_ma13_Assignment10 |
| noc18_ma13_Assignment11 | noc18_ma13_Assignment11 |
| noc18_ma13_Assignment12 | noc18_ma13_Assignment12 |
| noc18_ma13_Assignment13 | noc18_ma13_Assignment13 |
| noc18_ma13_Assignment2 | noc18_ma13_Assignment2 |
| noc18_ma13_Assignment3 | noc18_ma13_Assignment3 |
| noc18_ma13_Assignment4 | noc18_ma13_Assignment4 |
| noc18_ma13_Assignment5 | noc18_ma13_Assignment5 |
| noc18_ma13_Assignment6 | noc18_ma13_Assignment6 |
| noc18_ma13_Assignment7 | noc18_ma13_Assignment7 |
| noc18_ma13_Assignment8 | noc18_ma13_Assignment8 |
| noc18_ma13_Assignment9 | noc18_ma13_Assignment9 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Lecture 01: Introduction to Matrix Algebra - I | Download |
| 2 | Lecture 02: Introduction to Matrix Algebra - II | Download |
| 3 | Lecture 03: System of Linear Equations | Download |
| 4 | Lecture 04: Determinant of a Matrix | Download |
| 5 | Lecture 05: Determinant of a Matrix (Contd.) | Download |
| 6 | Lecture 06: Gauss Elimination | Download |
| 7 | Lecture 07: Gauss Elimination(Contd.) | Download |
| 8 | Lecture 08: LU Decomposition | Download |
| 9 | Lecture 09: Gauss-Jordon Method | Download |
| 10 | Lecture 10: Representation of Physical Systems as Matrix Equations | Download |
| 11 | Lecture 11: Tridiagonal Matrix Algorithm | Download |
| 12 | Lecture 12: Equations with Singular Matrices | Download |
| 13 | Lecture 13: Introduction to Vector Space | Download |
| 14 | Lecture 14: Vector Subspace | Download |
| 15 | Lecture 15: Column Space and Nullspace of a Matrix | Download |
| 16 | Lecture 16 : Finding Null Space of a Matrix | Download |
| 17 | Lecture 17 : Solving Ax=b when A is Singular | Download |
| 18 | Lecture 18 : Linear Independence and Spanning of a Subspace | Download |
| 19 | Lecture 19 : Basis and Dimension of a Vector Space | Download |
| 20 | Lecture 20 : Four Fundamental Subspaces of a Matrix | Download |
| 21 | Lecture 21: Left and right inverse of a matrix | Download |
| 22 | Lecture 22 : Orthogonality between the subspaces | Download |
| 23 | Lecture 23 : Best estimate | Download |
| 24 | Lecture 24 : Projection operation and linear transformation | Download |
| 25 | Lecture 25 : Creating orthogonal basis vectors | Download |
| 26 | Lecture 26: Gram-Schmidt and modified Gram-Schmidt algorithms | Download |
| 27 | Lecture 27: Comparing GS and modified GS | Download |
| 28 | Lecture 28: Introduction to eigenvalues and eigenvectors | Download |
| 29 | Lecture 29: Eigenvlues and eigenvectors for real symmetric matrix | Download |
| 30 | Lecture 30: Positive definiteness of a matrix | Download |
| 31 | Lecture 31 : Positive definiteness of a matrix (Contd.) | Download |
| 32 | Lecture 32 : Basic Iterative Methods: Jacobi and Gauss-Siedel | Download |
| 33 | Lecture 33 : Basic Iterative Methods: Matrix Representation | Download |
| 34 | Lecture 34 : Convergence Rate and Convergence Factor for Iterative Methods | Download |
| 35 | Lecture 35 : Numerical Experiments on Convergence | Download |
| 36 | Lecture 36 : Steepest Descent Method: Finding Minima of a Functional | Download |
| 37 | Lecture 37 : Steepest Descent Method: Gradient Search | Download |
| 38 | Lecture 38 : Steepest Descent Method: Algorithm and Convergence | Download |
| 39 | Lecture 39 : Introduction to General Projection Methods | Download |
| 40 | Lecture 40 : Residue Norm and Minimum Residual Algorithm | Download |
| 41 | Lecture 41 : Developing computer programs for basic iterative methods | Download |
| 42 | Lecture 42 : Developing computer programs for projection based methods | Download |
| 43 | Lecture 43 : Introduction to Krylov subspace methods | Download |
| 44 | Lecture 44 : Krylov subspace methods for linear systems | Download |
| 45 | Lecture 45 : Iterative methods for solving linear systems using Krylov subspace methods | Download |
| 46 | Lecture 46 : Conjugate gradient methods | Download |
| 47 | Lecture 47 : Conjugate gradient methods(Contd.) | Download |
| 48 | Lecture 48 : Conjugate gradient methods(Contd.) and Introduction to GMRES | Download |
| 49 | Lecture 49 : GMRES (Contd.) | Download |
| 50 | Lecture 50 : Lanczos Biorthogonalization and BCG Algorithm | Download |
| 51 | Lecture 51 : Numerical issues in BICG and polynomial based formulation | Download |
| 52 | Lecture 52 : Conjugate gradient squared and Biconjugate gradient stabilized | Download |
| 53 | Lecture 53 : Line relaxation method | Download |
| 54 | Lecture 54 : Block relaxation method | Download |
| 55 | Lecture 55 : Domain Decomposition and Parallel Computing | Download |
| 56 | Lecture 56: Preconditioners | Download |
| 57 | Lecture 57: Preconditioned conjugate gradient | Download |
| 58 | Lecture 58: Preconditioned GMRES | Download |
| 59 | Lecture 59: Multigrid methods I | Download |
| 60 | Lecture 60: Multigrid methods II | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Lecture 01: Introduction to Matrix Algebra - I | PDF unavailable |
| 2 | Lecture 02: Introduction to Matrix Algebra - II | PDF unavailable |
| 3 | Lecture 03: System of Linear Equations | PDF unavailable |
| 4 | Lecture 04: Determinant of a Matrix | PDF unavailable |
| 5 | Lecture 05: Determinant of a Matrix (Contd.) | PDF unavailable |
| 6 | Lecture 06: Gauss Elimination | PDF unavailable |
| 7 | Lecture 07: Gauss Elimination(Contd.) | PDF unavailable |
| 8 | Lecture 08: LU Decomposition | PDF unavailable |
| 9 | Lecture 09: Gauss-Jordon Method | PDF unavailable |
| 10 | Lecture 10: Representation of Physical Systems as Matrix Equations | PDF unavailable |
| 11 | Lecture 11: Tridiagonal Matrix Algorithm | PDF unavailable |
| 12 | Lecture 12: Equations with Singular Matrices | PDF unavailable |
| 13 | Lecture 13: Introduction to Vector Space | PDF unavailable |
| 14 | Lecture 14: Vector Subspace | PDF unavailable |
| 15 | Lecture 15: Column Space and Nullspace of a Matrix | PDF unavailable |
| 16 | Lecture 16 : Finding Null Space of a Matrix | PDF unavailable |
| 17 | Lecture 17 : Solving Ax=b when A is Singular | PDF unavailable |
| 18 | Lecture 18 : Linear Independence and Spanning of a Subspace | PDF unavailable |
| 19 | Lecture 19 : Basis and Dimension of a Vector Space | PDF unavailable |
| 20 | Lecture 20 : Four Fundamental Subspaces of a Matrix | PDF unavailable |
| 21 | Lecture 21: Left and right inverse of a matrix | PDF unavailable |
| 22 | Lecture 22 : Orthogonality between the subspaces | PDF unavailable |
| 23 | Lecture 23 : Best estimate | PDF unavailable |
| 24 | Lecture 24 : Projection operation and linear transformation | PDF unavailable |
| 25 | Lecture 25 : Creating orthogonal basis vectors | PDF unavailable |
| 26 | Lecture 26: Gram-Schmidt and modified Gram-Schmidt algorithms | PDF unavailable |
| 27 | Lecture 27: Comparing GS and modified GS | PDF unavailable |
| 28 | Lecture 28: Introduction to eigenvalues and eigenvectors | PDF unavailable |
| 29 | Lecture 29: Eigenvlues and eigenvectors for real symmetric matrix | PDF unavailable |
| 30 | Lecture 30: Positive definiteness of a matrix | PDF unavailable |
| 31 | Lecture 31 : Positive definiteness of a matrix (Contd.) | PDF unavailable |
| 32 | Lecture 32 : Basic Iterative Methods: Jacobi and Gauss-Siedel | PDF unavailable |
| 33 | Lecture 33 : Basic Iterative Methods: Matrix Representation | PDF unavailable |
| 34 | Lecture 34 : Convergence Rate and Convergence Factor for Iterative Methods | PDF unavailable |
| 35 | Lecture 35 : Numerical Experiments on Convergence | PDF unavailable |
| 36 | Lecture 36 : Steepest Descent Method: Finding Minima of a Functional | PDF unavailable |
| 37 | Lecture 37 : Steepest Descent Method: Gradient Search | PDF unavailable |
| 38 | Lecture 38 : Steepest Descent Method: Algorithm and Convergence | PDF unavailable |
| 39 | Lecture 39 : Introduction to General Projection Methods | PDF unavailable |
| 40 | Lecture 40 : Residue Norm and Minimum Residual Algorithm | PDF unavailable |
| 41 | Lecture 41 : Developing computer programs for basic iterative methods | PDF unavailable |
| 42 | Lecture 42 : Developing computer programs for projection based methods | PDF unavailable |
| 43 | Lecture 43 : Introduction to Krylov subspace methods | PDF unavailable |
| 44 | Lecture 44 : Krylov subspace methods for linear systems | PDF unavailable |
| 45 | Lecture 45 : Iterative methods for solving linear systems using Krylov subspace methods | PDF unavailable |
| 46 | Lecture 46 : Conjugate gradient methods | PDF unavailable |
| 47 | Lecture 47 : Conjugate gradient methods(Contd.) | PDF unavailable |
| 48 | Lecture 48 : Conjugate gradient methods(Contd.) and Introduction to GMRES | PDF unavailable |
| 49 | Lecture 49 : GMRES (Contd.) | PDF unavailable |
| 50 | Lecture 50 : Lanczos Biorthogonalization and BCG Algorithm | PDF unavailable |
| 51 | Lecture 51 : Numerical issues in BICG and polynomial based formulation | PDF unavailable |
| 52 | Lecture 52 : Conjugate gradient squared and Biconjugate gradient stabilized | PDF unavailable |
| 53 | Lecture 53 : Line relaxation method | PDF unavailable |
| 54 | Lecture 54 : Block relaxation method | PDF unavailable |
| 55 | Lecture 55 : Domain Decomposition and Parallel Computing | PDF unavailable |
| 56 | Lecture 56: Preconditioners | PDF unavailable |
| 57 | Lecture 57: Preconditioned conjugate gradient | PDF unavailable |
| 58 | Lecture 58: Preconditioned GMRES | PDF unavailable |
| 59 | Lecture 59: Multigrid methods I | PDF unavailable |
| 60 | Lecture 60: Multigrid methods II | PDF unavailable |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Not Available |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |