6 EC
Semester 1, period 2
5284NUAL6Y
The course gives a broad overview of basic numerical methods for solving Linear Systems, Least Squares problems, Eigenvalue problems, Nonlinear equations, Optimization problems, Interpolation and Quadrature problems, and Ordinary Differential Equations. These methods form the basis for many numerical algorithms used in computational science and engineering.
Micheal E. Heath, 'Scientific Computing, an Introductory Survey', SIAM, Philadelphia, USA, revised 2nd Edition, 2018, ISBN 9781611975574.
Lectures, practical sessions, self-study. See info on Canvas.
Activity | Number of hours |
Hoorcollege | 28 |
Laptopcollege | 28 |
Tentamen | 3 |
Zelfstudie | 109 |
This programme does not have requirements concerning attendance (Ter part B).
| Item and weight | Details |
|
Final grade | |
|
0.4 (40%) Homework grade | |
|
0.6 (60%) Tentamen | Must be ≥ 4.5 |
|
Final grade after retake | |
|
0.4 (40%) Homework grade | |
|
0.6 (60%) Hertentamen | Must be ≥ 4.5 |
Exam ("tentamen") grade must be >= 4.5. Tools for exam: Pen and paper. Student may use a handwritten cheat sheet of one page A4 (single sided).
Assignment grades will remain valid also in case of the resit, i.e. in the above computation the resit grade will replace the exam grade. The grade for the resit ("hertentamen") must be >=4.5, as is the case for the regular exam.
Partial results from previous years are no longer valid. This is in accordance with the teaching and exam regulations (TER).
The assignments are to be made in groups of two and will be graded.
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
Chapters 1-9 of the book by Heath are treated. Below is an indicative schedule. See Canvas for details.
| Weeknummer | Studiestof |
| 1 | chapters 1, 2 |
| 2 | chapters 2, 3 |
| 3 | chapters 3, 4 |
| 4 | chapters 4, 5 |
| 5 | chapters 5, 6 |
| 6 | chapters 7, 8 |
| 7 | chapters 8, 9 |
| 8 | exam |
The schedule for this course is published on DataNose.
Recommended prior knowledge: Basic knowledge of Calculus and Linear Algebra
Software tools: Programming assignments are to be done in Python using JupyterLab software.