Objectives and Content
This course deals with algorithms to solve: The eigenvalue problem superdeterminant equation systems and linear equation systems (onlyKrylov subspace iteration). Algorithms for matrix decomposition as QR-factorisation and Singular-value decomposition will be discussed and analysed with respect to stability and complexity.
Upon completion of the course, the successful candidate shall be able to:
- Choose the most appropriate numerical method to solve a given linear algebra problem.
- Explain and describe the principles of SVD, QR, LU and Cholesky factorization of matrices.
- Have the knowledge of different methods for eigenvalue problems, like power method, divide and conquer and QR method.
- Explain the principles of Krylov subspace methods, like the Arnoldi iteration, GMRES, Lanczos iteration and conjugate gradients.
- Analyze the speed and rate of convergence and stability of numerical algorithms.
Required Previous Knowledge
Recommended Previous Knowledge
MAT160 Scientific computing I
Compulsory Assignments and Attendance
Forms of Assessment
Oral exam. Exercises might be graded and included in the final grade.
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Type of assessment: Oral examination
- Withdrawal deadline