Undergraduate Course: Linear Programming & Numerical Analysis (MATH08037)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Credits | 10 |
| Home subject area | Mathematics |
Other subject area | Specialist Mathematics & Statistics (Year 1) |
| Course website |
https://info.maths.ed.ac.uk/teaching.html |
Taught in Gaelic? | No |
| Course description | *In 2011-12, this course is available only to students retaking it and will be assessed on an 'exam only' basis.*
First year course primarily for Honours Degrees in Mathematics and/or Statistics. Linear programming (LP): Modelling using LP; solution of LP problems by the standard simplex method; fair prices and sensitivity for LP problems.
Numerical Analysis (NA): Interpolation and approximation; Numerical integration; Numerical differentiation; Numerical solution of a nonlinear equation. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | No |
Course Delivery Information
|
| Delivery period: 2012/13 Semester 2, Available to all students (SV1)
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WebCT enabled: No |
Quota: 0 |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| No Classes have been defined for this Course |
| First Class |
First class information not currently available |
| Additional information |
Tutorials: F at 0900, 1000 and 1110. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
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| Main Exam Diet S2 (April/May) | | 2:00 | | | | Resit Exam Diet (August) | | 2:00 | | |
Summary of Intended Learning Outcomes
1. Ability to model simple continuous linear decision-making scenarios.
2. Mastery of the simplex method for linear programming and ability to extract the solution and sensitivity information from the optimal tableau.
3. Ability to perform numerical integration, differentiation and determine the solution of a single nonlinear equation numerically.
4. Understanding of the order of approximation error and the interplay between approximation error and computational error in the case of numerical differentiation. |
Assessment Information
| Coursework (which may include a Project): 15%; Degree Examination: 85%. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | LPNA |
Contacts
| Course organiser | Dr Toby Bailey
Tel: (0131 6)50 5068
Email: |
Course secretary | Ms Louise Durie
Tel: (0131 6)50 5050
Email: |
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