Valid from: Autumn 2015
Decided by: FN1/AndersGustafsson
Date of establishment: 2016-02-17
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
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.
Nocedal, J. & Wright, S.: Numerical Optimization. Springer, 2006. ISBN 9780387303031.
Type of instruction: Seminars. Seminars by the course participants
Examination formats: Written assignments, seminars given by participants
Grading scale: Failed, pass
Examiner:
It should be at least seven potential participants for the course to be given.
Course coordinators: