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Durham University

Postgraduate Module Handbook 2021/2022

Archive Module Description

This page is for the academic year 2021-22. The current handbook year is 2022-23

Department: Engineering

ENGI44C10: Optimisation 4

Type Tied Level 4 Credits 10 Availability Available in 2021/22
Tied to H1K609 New and Renewable Energy [Final intake in 2022/23]
Tied to H1K909 Electronic and Electrical Engineering [Final intake in 2022/23]
Tied to H1KA09 Advanced Mechanical Engineering

Prerequisites

  • <If other modules, please enter module code using 'Right Click, Insert module_code' or enter module title>

Corequisites

  • As specified in programme regulations.

Excluded Combination of Modules

  • As specified in programme regulations.

Aims

  • This module is designed solely for students studying Department of Engineering degree programmes.
  • To understand optimisation and the tools and techniques that can be used to improve engineering systems.
  • To give students the tools and training to recognize optimisation problems that arise in applications.
  • To present the basic theory of such problems, concentrating on results that are useful in applications and computation.
  • To give students a thorough understanding of how such problems are solved, and some experience in solving them.
  • To give students the background required to use the methods in their own research work or applications.

Content

  • Optimisation theory and techniques.
  • Recognizing and solving convex optimization problems that arise in applications.
  • Applications to signal processing, statistics and machine learning, control.

Learning Outcomes

Subject-specific Knowledge:
  • A knowledge and understanding of optimisation theory and techniques.
Subject-specific Skills:
  • An awareness of current analysis methods along with the ability to apply those methods in novel situations.
  • An in-depth knowledge and understanding of specialised and advanced technical and professional skills, an ability to perform critical assessment and review and an ability to communicate the results of their own work effectively.
Key Skills:
  • Capacity for independent self-learning within the bounds of professional practice.
  • Highly specialised numerical skills appropriate to an engineer.
  • Mathematics relevant to the application of advanced engineering concepts.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • The Optimisation module is covered in lectures, and reinforced by problem sheets, leading to the required problem solving capability. Assessment is by written examination.
  • Students are able to make use of staff 'Tutorial Hours' to discuss any aspect of the module with teaching staff on a one-to-one basis. These are sign up sessions available for up to one hour per week per lecture course.
  • Written timed examinations are appropriate because of the wide range of analytical, in-depth material covered in this module and allow students to demonstrate the ability to solve advanced problems independently as well as that they have deeply engaged with the material.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Lectures 20 Typically 1 per week 1 Hour 20
Tutorial Hours As required Weekly sign-up sessions Up to 1 Hour 10
Preparation and Reading 70
Total 100

Summative Assessment

Component: Examination Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Written Examination 2 hours 100%

Formative Assessment:

N/A


Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University