Approximation Algorithms (APX)
Many optimization problems in the real world are NP-hard, meaning that we cannot hope to solve them optimally. Instead, we use approximation algorithms to find solutions that are provably close in quality to the optimal solutions.
The course is of a theoretical nature, giving the students general guidelines for developing and analysing approximation algorithms for various optimization problems. It is aimed at graduate students who like to use mathematics to solve algorithmic problems.
The topics mentioned under Learning Outcome are covered in lectures and worked on in exercises in order to develop the necessary skills and competences.
MSc Programme in Computer Science
- Greedy algorithms and local search
- Rounding data and dynamic programming
- Deterministic rounding of linear programs
- Random sampling and randomized rounding of linear programs
- Randomized rounding of semidefinite programs
- Proving approximation guarantees for different types of algorithms
- Using linear programming, both with rounding and as a theoretical basis for primal-dual algorithms
- Analysing greedy algorithms and local search algorithms
- Apply approximation algorithms to computational problems that the student may later encounter in life.
- Communicate effectively about the theory of approximation algorithms, both for accessing advanced topics from the research literature and for convincingly presenting the results of own work.
Lectures and compulsory assignments.
See Absalon when the course is set up.
Expected to be: "The Design of Approximation Algorithms" by Shmoys and Williamson (is available for free online)
The students should be comfortable with formal, mathematical
reasoning, as the course uses the power of mathematics to
understand and prove good performance of algorithms. It is assumed
that the students have completed an algorithms course such as
Advanced Algorithms and Data Structures, and are comfortable using
mathematical proofs in the analysis of algorithms.
Academic qualifications equivalent to a BSc degree is recommended.
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- 7,5 ECTS
- Type of assessment
Oral examination, 30 minutes (including grading)The oral examination is with 30 minutes preparation
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Criteria for exam assessment
See Learning Outcome.
Single subject courses (day)
- Theory exercises
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 4
- No limit
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Computer Science
- Faculty of Science
- Rasmus Pagh (4-73646a6b43676c316e7831676e)
Are you BA- or KA-student?
Courseinformation of students