SF1543 Computer Science and Numerical Methods, Basic Course 7.5 credits

Basic ideas and concepts: algorithm, local linearisation, iteration, extrapolation, discretisation, convergence, stability, condition.

Estimation of reliability: parameter sensitivity, experimental perturbation calculation, precision.

Numerical methods for: linear systems of equations, nonlinear equations and systems of equations, interpolation, model adaptation with the least squares method, optimisation, integrals and differential equations.

Using mathematical software to solve engineering mathematical problems, make numerical experiments and present efficient algorithms.

A general aim with the course is to give the student the understanding that numerical methods are needed to make reliable and efficient simulations of technical and scientific processes based on mathematical models.

On completion of the course, the student should be able to

• identify different mathematical problems and reformulate them in a way that is appropriate for numerical treatment
• choose appropriate numerical method for treatment of the given problem
• explain choice of method by accounting for advantages and limitations
• choose an algorithm that implies efficient calculations and implement it in a programming language, suited for calculations e.g. Matlab
• estimate the reliability of the results
• use functions from the programming language library for efficient calculations and visualisation
• apply computer science for the solution of practical problems.

Mandatory first year courses in mathematics and a course in computer science or programming.

G Eriksson: Numeriska algoritmer med Matlab, CSC/Nada 2002.

If the course is discontinued, students may request to be examined during the following two academic years.

• LABA - Laboratory Work, 1.5 credits, grading scale: P, F
• LABB - Laboratory Work, 3.0 credits, grading scale: P, F
• TEN1 - Examination, 3.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

· LABA - Laboratory sessions, 1.5 credits, grading scale: P, F

· LABB - Laboratory sessions, 3.0 credits, grading scale: P, F

· TEN1 - Examination, 3.0 credits, grading scale: A, B, C, D, E, FX, F

In this course, the code of honour of the school is applied, see: http://www.sci.kth.se/institutioner/math/avd/na/utbildning/hederskodex-for-studenter-och-larare-vid-kurser-pa-avdelningen-for-numerisk-analys-1.357185

An examination (TEN1; 3 credits)
Laboratory assignments (LABA; 1.5 credits)
Individual programming assignment (LABB; 3 credits)

Katarina Gustavsson

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

DD1332 Object Oriented Programming, DD1347 Project in Computer Science, DD2310 Java Programming for Python Programmers, DN2220 Applied Numerical Methods I, DN2225 Numerical Solutions of Differential Equations and others.

In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see:  http://www.sci.kth.se/institutioner/math/avd/na/utbildning/hederskodex-for-studenter-och-larare-vid-kurser-pa-avdelningen-for-numerisk-analys-1.357185