|
KTH
/ CSC
/ Kurser
/ 2D1252
/ numalg07
Numerical Algebra
This course consists of two parts: |
Grade | Points | people |
A | 31 | 7 |
B | 27 | 8 |
C | 25 | 4 |
not yet passed | 10 |
Meeting |
Preparation |
Text |
Contents |
F1, Sept 5 10:15-12 in M3 |
1.2, 1.3 |
L 1 D 1.2-3 D 2.1-4 |
Introduction: General concepts in
numerical linear algebra. The standard problems. Linear systems: Gaussian elimination and factorizations, pivoting, error analysis |
L1, Sept 11 15-17 |
1. Floating point arithmetic, Gaussian
elimination: Lab 1 distributed |
||
F2, Sept 13 13:15-15 in V2 |
Linear systems, continued |
||
L2, Sept 18 15-17 |
Work on lab 1 |
||
F3, Sept 19 10:15-12 in M2 |
2.1, 2.3, 2.4, 5.1 |
L 2 D 2.7 |
Sparse matrices: Direct methods for linear
systems |
L3, Sept 25 15-17 |
2. Sparse matrices: Lab 2 distributed lab072.html |
||
F4 Sept 27 10:15-12 in M3 |
2.5, 5.2 |
L 3 D 3.1-2 3.5 |
Least squares: Theory, normal equations,
singular value decomposition (SVD), numerical rank |
L4, Sept 28 8-10 |
Work on lab 2 |
||
F5, Oct 4 10:15-12 in D34 |
1.5, |
L 4 D 4 |
Eigenvalues: Theory, perturbation
analysis, Transformation algorithms |
L5, Oct 5 8-10 |
3. SVD for data analysis: Lab 3 distributed. Data needed for pattern recognition exercise zipdata.mat ima2.m |
||
F6, Oct 11 10:15-12 in E2 |
5.3 |
L 5 D 6.6 |
Very large matrices: Iterative
algorithms |
L6, Oct 11 15-17 |
Work on lab 3 |
||
L extra 7, Oct 17 10-12 in Grå and Karmosin | Make lab 3 ready! |