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Syllabus for

Academic year
TMA265 - Numerical linear algebra
 
Owner: TM
5,0 Credits (ECTS 7,5)
Grading: TH - Five, Four, Three, Not passed
Level: A
Department: 0702 - Matematik MV CTH/GU


Teaching language: Swedish

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 No Sp
0101 Examination 5,0 c Grading: TH   5,0 c   Contact examiner

In programs

TTFYA ENGINEERING PHYSICS, Year 4 (elective)
TITEA INFORMATION ENGINEERING, Year 3 (elective)
TDATA COMPUTER SCIENCE AND ENGINEERING, Year 3 (elective)
TDATA COMPUTER SCIENCE AND ENGINEERING - Algorithms, Year 4 (elective)
TM Teknisk matematik, Year 2 (elective)
EMMAS MSc PROGR IN ENGINEERING MATHEMATICS, Year 1 (elective)

Examiner:




Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

Basic knowledge of numerical analysis, linear algebra.

Aim

To give the students knowledge and skill in using algorithms and numerical software for linear algebra problems.

Content

Numerical linear algebra problems arise in many different fields of science like solid mechanics, electrical networks, signal analysis and optimisa-tion. In this course we study basic linear algebra concepts like matrix algebra, vector- and matrix norms, error analysis and condition numbers. For solving linear systems of equations we consider Gaussian elimination with different pivoting strate-gies. For least-squares problems we study QR-factorisation and singular value decomposition. The metods for eigenvalue problems are based on transformation techniques for symmetric and non-symmetric matrices.
We discuss the numerical algorithms with respect to computing time and memory requirements. By homework assignments and project work the students get experiences in implementation and evaluation of numerical algorithms for linear algebra problems.

Literature

Applied Numerical Linear Algebra, James W. Demmel, SIAM 1997, chapters 1-5
J. R. Gilbert and C. Moler and R. Schreiber, 1992, Sparse Matrices in {MATLAB}: Design and Implementation
SIAM Journal on Matrix Analysis and Applications, 13, 333--356",

Examination

Experimental assignments and oral examination.


Page manager Published: Thu 03 Nov 2022.