6 EC
Semester 1, period 2
5284NUAL6Y
The course gives a broad overview of basic numerical methods for solving Linear Systems, Least Squares 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 |
26 |
|
Digital test |
3 |
|
Tentamen |
3 |
|
Zelfstudie |
108 |
This programme does not have requirements concerning attendance (Ter part B).
| Item and weight | Details |
|
Final grade | |
|
0.6 (60%) Tentamen | Must be ≥ 4.5 |
|
0.2 (20%) Deeltoets digitaal | |
|
0.2 (20%) Homework grade | |
|
1 (25%) Homework 1 | |
|
1 (25%) Homework 2 | |
|
1 (25%) Homework 3 | |
|
1 (25%) Homework 4 |
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).
The digital test ("deeltoets digitaal") will be done using Jupyter Lab and Python in a dedicated computer test room, so on computers provided by the university. More info will be published on Canvas. Please note the location on datanose. At the time of writing it is scheduled for a computer test room in the AMC area (Meibergdreef).
There will be separate resits for the exam ("tentamen") and the digital test ("deeltoets digitaal"). Students may choose to resit one or both of them. Homework grades will remain valid also in the case of a resit. The grade for the resit of the exam ("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Partial results from previous years are no longer valid.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).
Approximation errors, systems of linear equations
Linear least squares/QR decomposition/SVD
Optimization, interpolation
Interpolation, integration, ordinary differential equations
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, 5 |
| 4 | chapters 5, 6 |
| 5 | chapters 6, 7 |
| 6 | chapters 8, 9 |
| 7 | chapters 9, 4 |
| 8 | exam |
Required prior knowledge: Calculus (differentiation, integration, Taylor expansion, minima and maxima, derivatives in higher dimensions) and Linear Algebra (matrices, linear equations, rank, linear independence, orthogonality, eigenvalues)
Software tools: Programming assignments are to be done in Python using JupyterLab software.