Design and Analysis of Algorithms (G6017)
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Design and Analysis of Algorithms
Module G6017
Module details for 2025/26.
15 credits
FHEQ Level 5
Full Module Description
This module delves into the design of algorithms. We will see five powerful strategies for dealing with wide classes of algorithmic problems, namely:
a. divide-and-conquer
b. randomized algorithms
c. dynamic programming
d. greedy algorithms
e. network flow algorithms
By looking at plenty of examples, we will see how you can apply these strategies to write fast solutions to new problems,in terms of asymptotic complexity. We'll also look at when each strategy should be applied: e.g. dynamic programming is a useful hammer for solving lots of problems in polynomial time, but typically a greedy algorithm is faster when it exists.
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.
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 | 100.00% |
| Unseen Examination | 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 | 11111111111 |
| Autumn Semester | Lecture | 1 hour | 11111111111 |
| Autumn Semester | Seminar | 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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