# ADVANCED ALGORITHMS - 2017/8

Module code: COM2031

Module provider

Computer Science

Module Leader

GRUNING A Dr (Computer Sci)

Number of Credits

15

ECT Credits

7.5

Framework

FHEQ Level 5

JACs code

I100

Module cap (Maximum number of students)

N/A

Module Availability

Semester 1

Overall student workload

Independent Study Hours: 106

Lecture Hours: 24

Laboratory Hours: 22

Assessment pattern

Assessment type | Unit of assessment | Weighting |
---|---|---|

Coursework | INDIVIDUAL COURSEWORK | 40% |

Examination | 2HR UNSEEN EXAM | 60% |

Alternative Assessment

N/A

Prerequisites / Co-requisites

None

Module overview

The module introduces algorithmic techniques for various sets of problems and teaches how to analyse algorithms in terms of their complexity. The techniques build upon the data structures and algorithms module provided in level 4 (COM1029) so that students can further develop their use of methods for solving complex problems. Examples will be used throughout to demonstrate the relevance of each approach.

Module aims

The aim of this module is to equip students with a tool box of efficient algorithmic techniques which they can apply to real-world problems. This will cover foundations, theoretical advantages and constraints of a broad range of algorithms. Mathematical algorithms and operational techniques will be presented using examples of problems allowing students to practice their application. Appropriate mathematical concepts will be introduced to support the theoretical design and evaluation of algorithms.

Learning outcomes

Attributes Developed | |
---|---|

Understand the distinctive features of a broad range of algorithmic techniques that can be used to solve real-world problems | KC |

Formulate problems as abstract models which can be solved by generic algorithms and mathematical methods | KC |

Critically evaluate the design and efficiency of algorithms for specific types of problem | KCT |

Execute and implement algorithms in a programming language. | |

Apply a range of appropriate algorithms to example problems | KPT |

Critically interpret the performance of an algorithm and identify improvements that can be applied to make them more efficient | KPT |

Attributes Developed

**C** - Cognitive/analytical

**K** - Subject knowledge

**T** - Transferable skills

**P** - Professional/Practical skills

Module content

Divide-and-Conquer

Concepts of Divide-and-Conquer

Selected problems and their solutions using Divide-and-Conquer

Dynamic Programming

Concepts of dynamic programming

Selected problems and their solutions using dynamic programming

Linear Programming

Concepts of linear programming

Simplex algorithm

Selected problems and their solutions using linear programming

Network Flow Problems

Paths in graphs

Max-flow/min-cut problem

Min-cost flow problem (transportation problem)

Algorithms for NP problems

Reducibility and NP-completeness

Selected NP problems and strategies for finding their solutions

A selection of the following advanced topics:

Number-Theoretic Algorithms, Modular arithmetic (extended GCD, mod exponentiation, mod inversion, Cyclic Groups

Cryptographic Algorithms, Integer factorization, Discrete Logarithms, Encryption algorithms.

Approximation Algorithms

Randomised Algorithms

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

Help students understand the distinctive features of a broad range of algorithmic techniques

Deomonstrate the application of algorithmic techniques for solving concrete problems

Explain students how to analyse the complexity of an algorithm, incl. its performance and how to compare different algorithms

Equip students with necessary mathematical background to understanding certain algorithms

Enable students to apply taught techniques to solve concrete problems

The learning and teaching methods include:

Lectures (11 weeks at 2h) using detailed lecture slides to gauge the students’ understanding

Labs/Tutorials (11 weeks at 2h) using exercises and their solutions and demonstrations.

Students will be expected to spend a minimum of 7 hours a week on self-study to prepare and revise lecture, lab and tutorial material.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module learning outcomes.

Thus, the summative assessment for this module consists of:

· An individual coursework on sets of problems that students are required to solve. This addresses LO1, LO2, LO3, LO4 and LO5.

· A 2h unseen examination on the whole course content. This addresses LO2, LO3, L05 and LO6.

The individual courseworks will be due around week 6. The exam takes place at the end of the semester during the exam period.

Formative assessment and feedback

Lecture slides are used extensively in the lectures with each lecture consisting of a number of slides explaining the theory and showing the examples. Solutions to lab exercises are explained during the lab session and provided to the students as part of preparation for the exam.

Reading list

Reading list for ADVANCED ALGORITHMS : http://aspire.surrey.ac.uk/modules/com2031

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.