CS301 Complexity of Algorithms
CS301-15 Complexity of Algorithms
Introductory description
CS301 Complexity of Algorithms
Module aims
To learn the notions of the complexity of algorithms and the complexity of computational problems. To learn various models of computation. To understand what makes some computational problems harder than others. To understand how to deal with hard/intractable problems.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
In this module, the notions of complexity of algorithms and of computational problems will be
studied. Students will learn how to design efficient algorithms for reducing computational
problems to one another, what makes an algorithm efficient, and what makes a problem hard (so that it has no fast algorithm).
Various models of computation will be discussed, in particular, the models of classical deterministic computations, non-deterministic computations, and also of randomized computations, and approximation algorithms. Furthermore, parallel computations and on-line computations might be presented.
Some part of the module will be devoted to the discussion of what makes some computational problems harder than others, how to classify well-defined computational problems into levels of hardness, and how to deal with problems that are hard and intractable.
Learning outcomes
By the end of the module, students should be able to:
- Know and understand a variety of complexity classes.
- Understand techniques for formally proving that a computational problem is solvable or not solvable.
- Understand techniques for formally proving something about the kind and amount of computational resources (e.g. processing time, memory requirements) that are required to solve a problem.
- Formulate more tractable variations of some computationally hard problems.
Subject specific skills
N/A
Transferable skills
Critical thinking
Study time
| Type | Required |
|---|---|
| Lectures | 30 sessions of 1 hour (20%) |
| Seminars | 9 sessions of 1 hour (6%) |
| Private study | 111 hours (74%) |
| Total | 150 hours |
Private study description
Revising lecture material and background reading.
Solving exercises.
Revision for the exam.
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group B2
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| In-person Examination | 100% | No | |
|
CS301 exam
|
|||
Assessment group R1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| In-person Examination - Resit | 100% | No | |
|
CS301 resit exam
|
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Feedback on assessment
N/A
Pre-requisites
CS259 and CS260 are recommended to have been studied.
Courses
This module is Core for:
- Year 3 of UCSA-G4G1 Undergraduate Discrete Mathematics
- Year 3 of UCSA-G4G3 Undergraduate Discrete Mathematics
- Year 4 of UCSA-G4G4 Undergraduate Discrete Mathematics (with Intercalated Year)
- Year 4 of UCSA-G4G2 Undergraduate Discrete Mathematics with Intercalated Year
This module is Optional for:
- Year 4 of UMAA-G105 Undergraduate Master of Mathematics (with Intercalated Year)
- Year 4 of USTA-G1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)
-
USTA-G1G4 Undergraduate Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)
- Year 4 of G1G4 Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)
- Year 5 of G1G4 Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)
This module is Option list A for:
- Year 4 of UCSA-G504 MEng Computer Science (with intercalated year)
- Year 3 of UCSA-G500 Undergraduate Computer Science
- Year 4 of UCSA-G502 Undergraduate Computer Science (with Intercalated Year)
-
UCSA-G503 Undergraduate Computer Science MEng
- Year 3 of G500 Computer Science
- Year 3 of G503 Computer Science MEng
- Year 3 of USTA-G302 Undergraduate Data Science
- Year 3 of USTA-G304 Undergraduate Data Science (MSci)
- Year 4 of USTA-G303 Undergraduate Data Science (with Intercalated Year)
- Year 3 of UMAA-G100 Undergraduate Mathematics (BSc)
- Year 3 of UMAA-G103 Undergraduate Mathematics (MMath)
- Year 4 of UMAA-G101 Undergraduate Mathematics with Intercalated Year
This module is Option list B for:
-
UMAA-G105 Undergraduate Master of Mathematics (with Intercalated Year)
- Year 4 of G105 Mathematics (MMath) with Intercalated Year
- Year 5 of G105 Mathematics (MMath) with Intercalated Year
-
UMAA-G103 Undergraduate Mathematics (MMath)
- Year 3 of G103 Mathematics (MMath)
- Year 4 of G103 Mathematics (MMath)
- Year 4 of UMAA-G107 Undergraduate Mathematics (MMath) with Study Abroad
-
UMAA-G106 Undergraduate Mathematics (MMath) with Study in Europe
- Year 3 of G106 Mathematics (MMath) with Study in Europe
- Year 4 of G106 Mathematics (MMath) with Study in Europe
- Year 3 of USTA-GG14 Undergraduate Mathematics and Statistics (BSc)
- Year 4 of USTA-GG17 Undergraduate Mathematics and Statistics (with Intercalated Year)
This module is Option list D for:
- Year 3 of USTA-G1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)