Program Analysis (G6017)
Program Analysis
Module G6017
Module details for 2023/24.
15 credits
FHEQ Level 5
Module Outline
Part 1: Foundations
The first part of the module introduces the idea of the asymptotic analysis of algorithms, and in particular we will 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.
In the remainder of Part 1 we consider a number of important data structures, with particular emphasis on priority queues and the generic graph data structure. Several basic graph algorithms will be considered, in particular: depth-first search of graphs; breadth-first search of graphs; and topological sorting of directed acyclic graphs.
Part 2: Generic Design Paradigms
In part 2 we will consider four of the most important methods used as the basis for algorithm design: greedy methods; divide and conquer approaches; dynamic programming; and network flow.
In considering these generic design paradigms we will 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; and bipartite matching.
Library
J. Kleinberg and E. Tardos: Algorithm Design, Addison Wesley, 2005. International Student Edition.
This text is recommended because: (a) it is the one that the course will most closely follow (we will be covering parts of the material that is presented in the first seven chapters); and (b) the concepts are presented using a wide range of reasonably realistic problems.
Alternative text
Michael T. Goodrich and Roberto Tamassia, Data Structures and Algorithms in Java, John Wiley & Sons, Inc.
This is a more traditional algorithms textbook might be more to your taste.
Reference text
Thomas H Cormen, Charles E Leiserson, Ronald L Rivest and Clifford Stein, Introduction to Algorithms, Second Edition, MIT Press, 2001.
This is a good reference book on algorithms.
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.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 25.00% | |
| Coursework components. Weighted as shown below. | ||
| Problem Set | T1 Week 6 | 50.00% |
| Problem Set | XVAC Week 1 | 50.00% |
| Computer Based Exam | Semester 1 Assessment | 75.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Autumn Semester | Lecture | 2 hours | 22222222222 |
| Autumn Semester | Workshop | 1 hour | 01111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
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