Learning outcomes

• demonstrate their understanding of the principles of abstraction and encapsulation as they apply to the design of abstract data types and programs (A1, A2, A3);
• analyse and evaluate the time and space behaviour of algorithms and understand how this is expressed and determined (A1, A2,A3);
• recognise the importance of this analysis in the design of software (A1, A2, A3);
• recognise the importance of the classes P and NP in the analysis of algorithms (A1);
• describe some of the main approaches to algorithm design such as greedy algorithms, divide and conquer and dynamic programming (A1);
• demonstrate judgement in evaluating and choosing appropriate data structures and algorithms for a range of programming problems (A1, A2, A3,);
• design and implement significant programs in Java (A2, A3).

Brief description

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

Aims

• 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.

Content

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) - 17 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, AVL 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).

Reading Lists

Notes

This module is at CQFW Level 5