Course syllabus

# Optimering

Optimization

## FMAN60, 6 credits, A (Second Cycle)

## General Information

## Aim

## Learning outcomes

## Contents

## Examination details

## Admission

## Reading list

## Contact and other information

Optimization

Valid for: 2019/20

Decided by: PLED F/Pi

Date of Decision: 2019-03-26

Main field: Technology.

Elective Compulsory for: I3

Elective for: BME4, D4-mai, E4, F4, F4-bs, F4-bg, F4-fm, F4-r, F4-mai, M4, Pi4-bs, Pi4-fm, Pi4-bem, Pi4-bam

Language of instruction: The course will be given in English on demand

The aim of the course is to present basic optimization theory, and to give an overview of the most important methods and their practical use.

Knowledge and understanding

For a passing grade the student must

- be familiar with and, in his/her own words, be able to describe the optimization algorithms, for problems with and without constraints, encountered in the course, and their properties.
- be familiar with the theory of convex sets and convex functions, and be able to state and derive the most important theorems on convexity.
- be aware of how to make use of convexity in the treatment of an optimization problem.
- be familiar with Kuhn-Tucker Theory and be able to state and derive the most important theorems therein.

Competences and skills

For a passing grade the student must

- be able to demonstrate an ability to solve optimization problems within the framework of the course.
- be able to demonstrate an ability to handle optimization problems using a computer.
- be able to demonstrate an ability to, in the context of problem solving, derive simple results not previously encountered in the course .
- be able to describe the connections between different concepts in the course, with proper terminology and in a well structured and logically consistent manner.
- with proper terminology, suitable notation, in a well structured way and with clear logic be able to describe the solution to a mathematical problem and the theory within the framework of the course.

Quadratic forms and matrix factorisation. Convexity. The theory of optimization with and without constraints: Lagrange functions, Kuhn-Tucker theory. Duality. Methods for optimization without constraints: line search, steepest descent, Newton methods, conjugate directions, non-linear least squares optimization. Methods for optimization with constraints: linear optimization, quadratic programming, penalty and barrier methods.

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)

Assessment: Written test comprising theory and problems. Two computer exercises and one project.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Parts

Code: 0117. Name: Optimization.

Credits: 6. Grading scale: TH.

Code: 0217. Name: Computer Programming.

Credits: 0. Grading scale: UG.

Required prior knowledge: Basic university studies in calculus and linear algebra, including basic theory of quadratic forms.

The number of participants is limited to: No

The course overlaps following course/s: FMA051, MATC51

- Lars-Christer Böiers: Mathematical Methods of Optimization. Studentlitteratur, 2010, ISBN: 978-91-44-07075-9.
- Computer Laboratory Exercises in Optimization. Provided by the department.

Course coordinator: Studierektor Anders Holst, Studierektor@math.lth.se

Course administrator: Studerandeexpeditionen, expedition@math.lth.se

Teacher: Andrey Ghulchak, ghulchak@maths.lth.se

Course homepage: http://www.maths.lth.se/course/optnykod/