Computer Engineering MA, Applied Optimization, 6 credits
Please note that the literature can be changed/revised until:
• June 1 for a course that starts in the autumn semester
• November 15 for a course that starts in the spring semester
• April 1 for a course that starts in the summer
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Syllabus:
Datateknik AV, Tillämpad optimering, 6 hp
Computer Engineering MA, Applied Optimization, 6 credits
General data
- Code: DT059A
- Subject/Main field: Computer Engineering alt. Mathematics/Applied Mathematics
- Cycle: Second cycle
- Credits: 6
- Progressive specialization: A1N - Second cycle, has only first-cycle course/s as entry requirements
- Education area: Teknik 100%
- Answerable faculty: Faculty of Science, Technology and Media
- Answerable department: Information Systems and Technology
- Approved: 2018-06-04
- Version valid from: 2018-07-01
Aim
The course aims to provide knowledge of convex optimization and how it can be used in practical applications in areas such as information technology (machine learning, robotics, algorithms and complexity), communication networks, economics, signal processing and information theory.
Course objectives
After the course the students should be able to:
- Recognize convex optimization problems that can arise in practical applications
- Formulate non-convex optimization problem so that they become convex whenever possible.
- Present the basic theory so it can be applied to the above presented problems.
- Account for central concepts and definitions within the theory of convexity, use the theory, methods and techniques to solve mathematical problems.
- Know any programming model and language for optimization.
- Present oral and written work done individually or in group.
Content
The course includes the following elements
Convex sets, functions and optimization problems. Linear programming duality, simplex algorithm, interior point methods, and orientation in complexity. Non-linear and convex optimization Lagrange, Kuhn-Tucker theorems. Examples of non-convex problems, e.g. area and mixed-integer linear programming, Examples of signal processing, statistics, machine learning, radio resource allocation, production, economics, and game theory will be treated depending on the participants interest and background.
Entry requirements
Computer Engineering BA (ABC), 30 credits, including 7.5 credits in programming. Mathematics BA (A), 22.5 credits, including Mathematical Statistics, 7.5 credits and 7.5 credits Linear Algebra.
Selection rules and procedures
The selection process is in accordance with the Higher Education Ordinance and the local order of admission.
Teaching form
Teaching is carried out by means of the following elements
- Lectures
- Assignments
Examination form
L101: Laboratory work, 1.5 Credits
Grade scale: Fail (U) or Pass (G)
T101: Written exam, 4.5 Credits
Grade scale: Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.
1.5 Credits, L101: Laboratory work
Grades: Pass or Fail
4.5 Credits, T101: Written exam.
Grades: A, B, C, D, E, Fx and F. A-E are passed and Fx and F are failed.
Grading criteria for the subject can be found at www.miun.se/GradingCriteria
The examiner has the right to offer alternative examination arrangements to students who have been granted the right to special support by Mid Sweden University’s disabilities adviser.
Grading system
Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.
Course reading
Required literature
- Author: Boyd and Vandenberghe
- Title: Convex Optimization
- Edition: 2004
- Publisher: Cambridge University Press