NMAK10012U Optimization and Convexity (OK)
Volume 2013/2014
Education
MSc Programme in Mathematics
MSc Programme in Statistics
MSc Programme in Mathematics-Economics
MSc Programme in Statistics
MSc Programme in Mathematics-Economics
Content
This course aims at giving an introduction to convexity theory and
its applications to optimization problems. The following basic
topics are central to the subject: topological perperties of convex
sets, cones, extreme points, separation, convex funtions,
subdifferential calculus and the notion of conjugate duality.
We will develop the necessary and sufficient conditions of
optimality for convex problems and we will also address some
aspects of the duality theory.
Learning Outcome
Knowledge:
The student is supposed to get a good understanding of basic results in the theory of convex sets and convex functions. As examples are mentioned, separation theorems, duality and the conjugate of a convex function.
Skills:
The knowledge is applied to optimization problems and the student is supposed to be able to describe how the solution to such a problem may be obtained by an implementation of an algorithm, which in turn is based on convexity theory .
Competences:
The goal is that the student understands how theoretical problems from other sciences may be modelled and solved using optimization techniques coming from convexity theory.
The student is supposed to get a good understanding of basic results in the theory of convex sets and convex functions. As examples are mentioned, separation theorems, duality and the conjugate of a convex function.
Skills:
The knowledge is applied to optimization problems and the student is supposed to be able to describe how the solution to such a problem may be obtained by an implementation of an algorithm, which in turn is based on convexity theory .
Competences:
The goal is that the student understands how theoretical problems from other sciences may be modelled and solved using optimization techniques coming from convexity theory.
Academic qualifications
Introductory courses in
linear algebra and calculus (e.g. LinAlg and MatIntro). Analysis 1
(An1) (or similar) recommended.
Teaching and learning methods
4 hours of lectures and 2
hours og exercises/discussion per week
Workload
- Category
- Hours
- Exam
- 50
- Lectures
- 28
- Preparation
- 114
- Theory exercises
- 14
- Total
- 206
Sign up
Self Service at KUnet
As an exchange, guest and credit student - click here!
Continuing Education - click here!
As an exchange, guest and credit student - click here!
Continuing Education - click here!
Exam (3 hrs written exam)
- Credit
- 7,5 ECTS
- Type of assessment
- Written examination, 3 hrs under invigilationThe homeworks will be graded, and so will the final exam. The final grade is the average of these 2 grades.
- Exam registration requirements
- 2 sets of exercises done as homework.
- Aid
- Only certain aids allowed
Only books and notes are allowed at the exam. The homeworks may be discussed with the other students, but the final formulations of the answers must be personal and made by the student alone. .
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Exam period
- One internal examiner
- Re-exam
- Reexamination: Oral, 30 minutes, with 30 minutes preparation time. Graded according to the 7 step scale with two internal examiners.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome.
Course information
- Language
- English
- Course code
- NMAK10012U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- A
- Course capacity
- No limits
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Salvador Pineda Morente
+45 35 32 06 87, office 04.3.01
Lecturers
Salvador Pineda Morente
Saved on the
18-06-2013