Semester of Instruction
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
Written exam. Exercises might be graded and included in the final grade. It may be oral examination if less than 20 students attend the course.
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.