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# Details for Course FMNN02F Introduction to Numerical Linear Algebra

General
• FMNN02F
• Discontinued
Course Name
• Introduction to Numerical Linear Algebra
Course Extent
• 7.5
Type of Instruction
• Course given jointly for second and third cycle
• 7154 (Centre of Mathematical Sciences / Numerical Analysis)
• 2019-09-12
• Professor Thomas Johansson

## Current Established Course Syllabus

General
Aim
• The aim of the course is to make the postgraduate student familiar with concepts and methods from numerical linear algebra. In general there are ready-made program libraries available but it is important to be able to recognize types of input which may cause problems for the most common methods.
Contents
• Norms.
Singular value decomposition and numerical rank.
QR factorization, the Gram-Schmidt process and Householder matrices.
Least squares problems and pseudoinverses.
Linear systems of equations and condition numbers.
Positive definite matrices and Cholesky factorization.
Numeric determination of eigenvalues.
Knowledge and Understanding
• For a passing grade the doctoral student must
• be able to explain the concept of matrix norm

be able to account for how one finds the singular value decomposition of a matrix, and to give examples of applications of the decomposition.

be able to define the condition number of a matrix and to explain its relevance for the solution of systems of linear equations

be able to describe common methods to numerically determine eigenvalues.
Competences and Skills
• For a passing grade the doctoral student must
• be able to implement given algorithms from numerical linear algebra in computer programs and use them to solve problems.

be able to, in a well-structured report, account for the solution to a problem within the scope of the course.

Judgement and Approach
• For a passing grade the doctoral student must
Types of Instruction
• Lectures
• Project
Examination Formats
• Oral exam
• Written assignments
• Weekly hand-in assignments.
• Failed, pass
Assumed Prior Knowledge
• Calculus in several variables. Linear algebra including eigenvalues/vectors. Programming in Matlab or Python.
Selection Criteria
Literature
• Trefethen, Lloyd N. & David Bau, I.: Numerical Linear Algebra. SIAM, 1997. ISBN 9780898713619.
Further Information
Course code
• FMNN02F
• 2019-09-12
• Professor Thomas Johansson

## All Established Course Syllabi

1 course syllabus.

Valid from First hand in Second hand in Established
Autumn 2019 2019‑05‑20 18:30:08 2019‑06‑10 08:46:04 2019‑09‑12

## Current or Upcoming Published Course Occasion

No matching course occasion was found.

## All Published Course Occasions

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