MM525: Convex Analysis
Study Board of Science
Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N300049112, N300049102
Assessment: Second examiner: None, Second examiner: External
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Bachelor
STADS ID (UVA): N300049101
ECTS value: 5
Date of Approval: 01-11-2022
Duration: 1 semester
Version: Archive
Comment
Entry requirements
Academic preconditions
Students taking the course are expected to:
Be familiar with: systems of linear equations, matrices, determinants, vector spaces, scalar product and orthogonality, linear transformations, eigenvectors and eigenvalues, polynomials, the concept of a function and its derivatives, real numbers, vector calculus.
Course introduction
The course will introduce analytic techniques and geometrical concepts in order to solve linear and non-linear optimization problems, mostly in economy.The course builds on the knowledge acquired in the courses MM505 Linear Algebra, or MM540, or MM538, and MM533 Mathematical and Numerical Analysis.
The course is of high multidisciplinary value and gives an academic basis for a Bachelor Project in several core areas of Natural Sciences and Economy.
The course is of high multidisciplinary value and gives an academic basis for a Bachelor Project in several core areas of Natural Sciences and Economy.
In relation to the competence profile of the degree it is the explicit focus of the course to:
Give the competence to:
- handle complex and development-oriented situations in study and work contexts.
Give skills to:
- apply the thinking and terminology from the subject's basic disciplines.
- analyze and evaluate the theoretical and practical problems for the application of a suitable mathematical model.
Give knowledge and understanding of:
- basic knowledge generation, theory and methods in mathematics.
- how to conduct analyses using mathematical methods and critically evaluate scientific theories and models.
Expected learning outcome
The learning objectives of the course is that the student demonstrates the ability to:
- Correctly answer written assignments and prove results within the syllabus of the course.
- Reproduce and illustrate definitions and results within the syllabus of the course.
- Formulate answers to written assignments in a mathematically correct language.
- Give arguments for the steps in the solution of the exercises.
- Compare key results within the syllabus of the course.
Content
Convex sets and their topology, convex functions, conjugation, subdifferentiability, minimization, Kuhn-Tucker theory, Numerical optimization methods
Literature
Examination regulations
Exam element a)
Timing
Spring
Tests
Mandatory assignments
EKA
N300049112
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
1
Exam element b)
Timing
June
Tests
Oral exam
EKA
N300049102
Assessment
Second examiner: External
Grading
7-point grading scale
Identification
Student Identification Card
Language
Normally, the same as teaching language
Duration
30 minutes
Examination aids
To be announced during the course
ECTS value
4
Indicative number of lessons
Teaching Method
Teacher responsible
Timetable
Administrative Unit
Team at Educational Law & Registration
Offered in
Recommended course of study
Transition rules
Transitional arrangements describe how a course replaces another course when changes are made to the course of study.
If a transitional arrangement has been made for a course, it will be stated in the list.
See transitional arrangements for all courses at the Faculty of Science.