Prof Konrad Swanepoel

This course is available on the MSc in Applicable Mathematics and MSc in Operations Research & Analytics. This course is available as an outside option to students on other programmes where regulations permit.

Students must have completed Algorithms and Computation (MA407) or have taken an equivalent course to provide a basic knowledge in analysis of algorithms: running time and correctness of an algorithm, and basic knowledge of computer programming (preferably in Java). Students should be comfortable with proofs and proof techniques used in pure mathematics.

Introduction to NP-Completeness, followed by Approximation Algorithms, Randomised Algorithms, and a selection of topics from Average-Case Analysis, Streaming Algorithms, Exponential-Time Algorithms, and Numerical Algorithms.

20 hours of lectures and 15 hours of seminars in the LT. 2 hours of lectures in the ST.

In-person lectures and seminars might be replaced by online ones depending on the future COVID-19 situation.

Weekly exercises are set and marked. Some of these will include programming exercises in Java.

T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to Algorithms, 3rd or 4th ed., MIT;

M.R. Garey and D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-completeness, W.H. Freeman, 1979;

D. Williamson, D. B. Shmoys, The Design of Approximation Algorithms, Cambridge University Press, 2011;

V. Vazirani, Approximation Algorithms, 2nd ed., Springer, 2002;

Michael Mitzenmacher and Eli Upfal, Probability and Computing, 1st or 2nd ed., Cambridge University Press.

Exam (65%, duration: 2 hours and 30 minutes) in the summer exam period.
Coursework (25%) in the period between LT and ST.
Continuous assessment (10%) in the LT.