Module Information

In the 40 or so years that people have been developing software, a range of common tasks, such as sorting and searching, have been identified and a body of knowledge has been accumulated about how these tasks are best carried out. This knowledge usually consists of various ways of structuring the data, a range of different algorithms, and performance analysis that enables a designer to chose the algorithm and data structure most appropriate to the circumstances of the system, and to predict how the system will perform.

This module introduces students to this body of knowledge and sets it into the context of modern software design and structuring techniques.

The aims of this module are to:

• introduce students to the standard data structures and algorithms that form the common currency of software design
• develop students understanding of the object-oriented model of software;
• introduce students to the analysis and prediction of performance.

1. Introduction - 15 Lectures
Course Overview. An introduction into time/space complexity. Mathematical underpinnings. Issues of correctness as they relate to the definition of ADTs. The key ideas of abstraction and encapsulation. Notations for describing ADTs. Java support for their implementation: packages, exceptions and interfaces.

2. Introduction to Complexity - 3 Lectures
O() notation, growth rates. Measurement of execution time of some real programs and estimation of their time complexity. Some examples of time/space trade-offs.

3. Classes of Algorithm - 4 Lectures
An overview will be given on the different classes of algorithm; for example, divide and conquer and greedy algorithms. Genetic algorithms will also be discussed. P and NP.

4. Recursion - 3 Lectures
An introduction to recursive thinking. Examples of recursion.

5. Storing and Retrieving Data by Key (1) - 12 Lectures
This problem will be used to motivate the discussion of a wide variety of different implementation techniques. The features of some typical solutions will be related to the dimensions of the problem such as the volume of data to be handled, volatility and the operations required. Internal Storage: linear and binary searching. Linked representations; an introduction to hashing, binary search trees and heaps.

6. Storing and Retrieving Data by Key (2): External storage - 4 Lectures
Performance issues. Hashing and B-tree organisations. The Hashable class in Java.

7. Representing Text - 4 Lectures
String matching algorithms and their performance. Search engines case study.

8. Sorting - 4 Lectures
A comparison of divide and conquer, priority queue and address calculation based sorting algorithms. Performance characteristics of these algorithms will be discussed.

9. Representing Complex Relationships: Graphs - 6 Lectures
Some examples of greedy algorithms. Terminology and implementation considerations. A look at some graph-related problems such as: finding a route (shortest paths); planning a communications network (minimum spanning trees); network routing management (flow graphs); compiling a program or planning a project (topological sorting).