Algorithms and Data Structures: 2021-2022
Lecturer | |
Degrees | Part A — Computer Science and Philosophy |
Term | Hilary Term 2022 (16 lectures) |
Links |
Overview
This course builds on the first-year Design and Analysis of Algorithms course. It introduces students to a number of highly efficient algorithms and data structures for fundamental computational problems across a variety of areas. Students are also introduced to techniques such as amortised complexity analysis. As in the first-year course, the style of the presentation is rigorous but not formal.
Learning outcomes
On successful completion of the course students will:
- Be able to analyse and use some fundamental data structures, such as binary search trees and disjoint sets
- Understand the implementation, complexity analysis and applications of fundamental algorithms such as max flow and linear programming
- Have familiarity with randomised algorithms, approximation algorithms, and fixed-parameter algorithms
Syllabus
- Amortised analysis
- Disjoint sets / union-find
- Binary search trees (Red-Black trees, splay trees)
- Max flow and min cut in networks; applications
- Linear programming
- NP-hardness
- Approximation algorithms
- Fixed-parameter tractability
- Exponential algorithms
Reading list
The main text used in the course is:
- Thomas Cormen, Charles Leiserson, Ronald Rivest and Clifford Stein, Introduction to Algorithms, MIT Press, 2009 (third edition).
Other useful textbooks that cover some of the material are
- S. Dasgupta, C.H. Papadimitriou, and U. V. Vazirani, Algorithms, Mcgraw-Hill, 2006.
- J. Kleinberg and E. Tardos, Algorithm Design, Addison-Wesley, 2006.
- V. Vazirani, Approximation Algorithms, Springer, 2001
Feedback
Students are formally asked for feedback at the end of the course. Students can also submit feedback at any point here. Feedback received here will go to the Head of Academic Administration, and will be dealt with confidentially when being passed on further. All feedback is welcome.