杏吧论坛

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 other topics such as some of Average-Case Analysis, Streaming Algorithms, Exponential-Time Algorithms, and Numerical Algorithms.

This course is delivered through a combination of seminars and lectures totalling a minimum of 30 hours across Lent Term. This year, apart from pre-recorded lecture videos, there will be a weekly live online session of an hour. Depending on circumstances, seminars might be online.

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 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;

R. Motwani and P. Raghavan, Randomized Algorithms, Cambridge University Press 1995.

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.

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.