From: ₹200.00
Computing forms a key component of Numerical Linear Algebra. This workshop aims to develop an understanding of how to translate numerical ideas into efficient code. The programming language used will be Python, though the concepts and techniques are easily transferable to other languages.
Dr. Abhinav Jha is an Assistant Professor in the Department of Mathematics at the Indian Institute of Technology Gandhinagar. His research interests lie in Numerical Analysis and Scientific Computing. In addition to the theoretical analysis of numerical methods, he has a strong interest in their efficient and robust implementation on modern computing architectures. His work includes two large-scale computational projects—one developed in C++ and the other in FORTRAN—focusing on the numerical solution of partial differential equations.
| Day | Module name | Concepts covered | Recorded videos - number of hours
Live sessions - No. of hours |
Assessment | Learning outcomes |
| 13/06/26 | Basics of Matrix and Vector Handling in NumPy | Array creation, indexing, slicing, matrix operations, norms, vectorization, solving linear systems using NumPy | 1 Hours | Implement basic matrix operations and solve small linear systems in Python | Build foundational skills for handling matrices and vectors efficiently in NumPy |
| 13/06/26 | Decomposition Methods (Gaussian Elimination, LU, LDL) | Gaussian elimination, LU decomposition, forward/backward substitution, LDL decomposition, numerical stability | 2 Hours | Implement LU decomposition and solve linear systems; compare with NumPy solvers | Understand direct methods for solving linear systems and their computational structure |
| 13/06/26 | Iterative Methods (Jacobi, Gauss–Seidel, SOR) | Matrix splitting, Jacobi method, Gauss–Seidel method, SOR method, convergence criteria, effect of Relaxation parameter |
2 Hours | Implement iterative solvers and compare convergence rates for different methods and parameters | Develop intuition for iterative solvers and analyze convergence behavior in practical problems |
Students – Rs. 236 (Rs. 200 + 18% GST)
Faculty/PostDocs – Rs. 590 (Rs. 500 + 18% GST)
Industry – Rs. 708 (Rs. 600 + 18% GST)
Intended Audience:
UG, PG, PhD Students, PostDocs/Faculty and Industry.
Eligibilty:
Basics of Pythons are expected. If the students want, the basics can also be covered in the preliminary classes.
Attendance is mandatory for obtaining the certificate.
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