Module Details |
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module. |
Title | Optimisation | ||
Code | COMP557 | ||
Coordinator |
Dr C Ikenmeyer Computer Science Christian.Ikenmeyer@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2021-22 | Level 7 FHEQ | First Semester | 15 |
Aims |
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To provide a foundation for modelling various continuous and discrete optimisation problems. To provide the tools and paradigms for the design and analysis of algorithms for continuous and discrete optimisation problems. Apply these tools to real-world problems. To review the links and interconnections between optimisation and computational complexity theory. To provide an in-depth, systematic and critical understanding of selected significant topics at the intersection of optimisation, algorithms and (to a lesser extent) complexity theory, together with the related research issues. |
Learning Outcomes |
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(LO1) The ability to recognise potential research opportunities and research directions |
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(LO2) The ability to read, understand and communicate research literature in the field of optimisation. |
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(LO3) The ability to use appropriate algorithmic paradigms and techniques in context of a particular optimisation model. |
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(LO4) The ability to formulate optimisation models for the purpose of modelling particular applications. |
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(LO5) A critical awareness of current problems and research issues in the field of optimisation. |
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(S1) Critical thinking and problem solving - Critical analysis |
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(S2) Communication (oral, written and visual) - Presentation skills – oral |
Syllabus |
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Basics: Linear Algebra, Geometry and Graph Theory. (5 lectures) Linear Programming Basics: Introduction, Definitions, Examples, Geometric and Algebraic views of Linear Programming, Mixed Integer Linear Programming (7 lectures) Linear Programming: Simplex Algorithm (6 lecture) Linear Programming: Duality (5 lectures ) Algorithms for important optimisation problems (e.g. optimal trees and paths, network flows). (7 lectures) |
Teaching and Learning Strategies |
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Teaching Method 1 - Lectures Due to Covid-19, in 2021/22, one or more of the following delivery methods will be implemented based on the current local conditions. (b) Fully online delivery and assessment (c) Standard on-campus delivery |
Teaching Schedule |
Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
Study Hours |
30 |
10 |
40 | ||||
Timetable (if known) | |||||||
Private Study | 110 | ||||||
TOTAL HOURS | 150 |
Assessment |
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EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
(557) Final exam | 70 | |||||
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
(557.1) Assessment 1 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Semester 1 | 2 sets of assessment | 15 | ||||
(557.2) Assessment 2 There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Semester 1 | 2 sets of assessment | 15 |
Recommended Texts |
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Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. |