Course Syllabus for

Numerical Optimization
Numerisk optimering

FMA275F, 7.5 credits

Valid from: Autumn 2015
Decided by: FN1/AndersGustafsson
Date of establishment: 2016-02-17

General Information

Division: Mathematics
Course type: Third-cycle course
Teaching language: English


The aim of the course is to give good knowledge about modern numerical optimization algorithms especially about those suitable for large-scale problems - in particular their practical strengths and weaknesses and a deeper understanding of the basic principles behind them - in order to be able to use them in research.


Knowledge and Understanding

For a passing grade the doctoral student must

Competences and Skills

For a passing grade the doctoral student must

Course Contents

Line search and trust-region methods, conjugate gradient and quasi-Newton methods, large-scale optimization, derivative-free methods, least-squares, nonlinear equations, theory and fundamentals of algorithms for nonlinear optimization with constraints, interior-point methods, quadratic and sequential quadratic programming, penalty and augmented Lagrangian methods.

Course Literature

Nocedal, J. & Wright, S.: Numerical Optimization. Springer, 2006. ISBN 9780387303031.

Instruction Details

Type of instruction: Seminars. Seminars by the course participants

Examination Details

Examination formats: Written assignments, seminars given by participants
Grading scale: Failed, pass

Admission Details

Further Information

It should be at least seven potential participants for the course to be given.

Course Occasion Information

Contact and Other Information

Course coordinators:

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