This course syllabus is discontinued or replaced by a new course syllabus.

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School of Science and Technology

Course Syllabus


Numerical Linear Algebra, 7.5 Credits


Course Code: MA116G Subject Area: Field of Science
Main Field of Study: Mathematics Credits: 7.5
    Subject Group (SCB): Mathematics
Education Cycle: First Cycle Progression: G2F
Established: 2014-12-09 Last Approved: 2018-03-28
Valid from: Autumn semester 2018 Approved by: Head of School


Aims and Objectives

General aims for first cycle education

First-cycle courses and study programmes shall develop:
- the ability of students to make independent and critical assessments
- the ability of students to identify, formulate and solve problems autonomously, and
- the preparedness of students to deal with changes in working life.

In addition to knowledge and skills in their field of study, students shall develop the ability to:
- gather and interpret information at a scholarly level
- stay abreast of the development of knowledge, and
- communicate their knowledge to others, including those who lack specialist knowledge in the field.

(Higher Education Act, Chapter 1, Section 8)

Course Objectives

Knowledge and comprehension
After the course the student should
-understand the most important numerical methods for linear systems of equations, linear least squares problems, and linear eigenvalue problems.

Proficiency and ability
At the end of the course the student should be able to
-define and use the terms condition and stability for linear problems,
-define and use the singular value decomposition, and
-use and analyse the most important direct and iterative methods for linear problems.


Main Content of the Course

Gaussian elimination with extensions. Condition, perturbation analysis, stability and backward stability for linear systems, least squares and eigenvalue problems. Gram-Schmidt. QR-faktorization. Singular value decomposition. The power method and QR-method. Classical interative methods for linear systems of equations. Conjugated gradient methods with convergence analysis. Different softwares for sparse systems. Applications from biology, mechanics and medicin.


Teaching Methods

Teaching will be in the form of lectures and supervised computer sessions.
The teaching methods may be altered, should only a few students take the course.

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.


Examination Methods

Examination, 7.5 Credits. (Code: 0100)
Obligatory computer assignments presented in written and oral form

For further information, see the university's local examination regulations (in Swedish).


Grades

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may prescribe which grading system shall apply. The grade is to be determined by a teacher specifically appointed by the university (an examiner).

According to regulations on grading systems for first- and second-cycle education (vice-chancellor's decision 2010-10-19, reg. no. CF 12-540/2010), one of the following grades is to be used: fail, pass, or pass with distinction. The vice-chancellor or a person appointed by the vice-chancellor may decide on exceptions from this provision for a specific course, if there are special reasons.

Grades used on course are Fail (U), Pass (G) or Pass with Distinction (VG).

Examination
Grades used are Fail (U), Pass (G) or Pass with Distinction (VG).


The course grading is translated to the ECTS grading scale.


For further information, see the university's local examination regulations (in Swedish).


Specific entry requirements

Optimization, 7,5 Credits.

For further information, see the university's admission regulations (in Swedish).


Transfer of Credits for Previous Studies

Students who have previously completed higher education or other activities are, in accordance with the Higher Education Ordinance, entitled to have these credited towards the current programme, providing that the previous studies or activities meet certain criteria.


For further information, see the university's local credit transfer regulations (in Swedish).


Reading List and Other Teaching Materials

Required Reading

Material handed out by the Department of Mathematics.
Material som tillhandahålles av enheten för matematik
Watkins, David S. (2002)
Fundamentals of Matrix Computations
Wiley-Blackwell

Additional Reading
Golub, Gene & Van Loan, Charles (2012)
Matrix Computations
Joans Hopkins


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