Module code: G6017
15 credits in autumn semester
Teaching method: Lecture
Assessment modes: Coursework, Unseen examination
This module is split into two parts:
In the first part of the module, you are introduced to the idea of the asymptotic analysis of algorithms. In particular, you consider the following:
- specifying a problem
- the notion of an algorithm and what it means for an algorithm to solve a problem
- the upper, lower and tight asymptotic bounds associated with an algorithm
- the best-, worst- and expected-case analysis of an algorithm
- the lower bound for a problem.
You also consider a number of important data structures, with particular emphasis on priority queues and the generic graph data structure. You look at several basic graph algorithms, in particular:
- depth-first search of graphs
- breadth-first search of graphs
- topological sorting of directed acyclic graphs.
Generic Design Paradigms
In the second part of the module, you consider four of the most important methods used as the basis for algorithm design:
- greedy methods
- divide and conquer approaches
- dynamic programming
- network flow.
In considering these generic design paradigms, you look at a number of well-known problems, including:
- interval scheduling
- single source shortest path
- minimum spanning tree
- Huffman codes construction
- weighted interval scheduling
- subset sum
- sequence alignment
- network flow
- bipartite matching.
Module learning outcomes
- Given a novel problem specification, determine an appropriate style of algorithm to deploy for that problem.
- Analyse the asymptotic efficiency of an algorithm, distinguishing best-, worst- and expected-cases.
- Design and implement algorithmic solutions to problems based on greedy, dynamic programming and network flow approaches.
- Express an algorithm using abstract pseudo-code rather than using a particular programming language.