Matrix algebra plays a key role in a lot of application areas such as electrical and electronics engineering, computer science and engineering, industrial engineering to name a few. This course complements the already existing standard courses such as Matrix Theory and Linear Algebra which do not explore the computational aspects and applications in detail.
This course is intended for the audience who are mathematicians as well as engineers with a little background in matrix algebra. We provide an introduction to basic concepts in this area and cover important decompositions such as spectral decomposition, LDU, QR, and SVD. We
illustrate the applications of these decompositions in solving the system of linear equations, least squares problems, and eigenvalue problems.
Advanced concepts such as the proof of SVD as an application of spectral theorem, condition numbers, sensitivity analysis are also covered which have practical significance. A variety of numerical techniques related to the theory will be discussed. Also, this gives a scope to have some hands-on experience on computations using PYTHON.
- Introduction to vectors and matrices, norm, matrix vector, multiplication, complexity
- Rank, Nullity, Row span, Column span, eigenvalues and eigenvectors of a square matrix
- Inner product and orthogonality
- Spectral representation of semi-simple matrices
- Spectral theorem for symmetric matrices and its applications
- Linear equations: Gaussian elimination, LDU decomposition, Cholesky decomposition
- Condition number and sensitivity results
- QR factorization, Least Squares (LS) problem, and solution
- Singular Value Decomposition and its applications
- Low-rank approximations and Sensitivity results for LS problem
How to Apply
Interested candidates can apply by clicking course.
For Industry People: Rs. 10,000 + GST/-
For Students: Rs. 5000 + GST/-
Last Date of Registration is July 15, 2019.
Phone Number: +91-3222-282856
Email ID: email@example.com
For further details, click here.
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