Undergraduate Course: Non-linear Optimization (MATH11045)
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 11 (Year 4 Undergraduate) |
Credits |
10 |
| Home subject area |
Mathematics |
Other subject area |
Specialist Mathematics & Statistics (Honours) |
| Course website |
https://info.maths.ed.ac.uk/teaching.html
|
Taught in Gaelic? |
No |
| Course description |
The solution of optimal decision-making and engineering design problems in which the objective and constraints are nonlinear functions of potentially (very) many variables is required on an everyday basis in the commercial and academic worlds. A closely-related subject is the solution of nonlinear systems of equations, also referred to as least-squares or data fitting problems that occur in almost every instance where observations or measurements are available for modelling a continuous process or phenomenon, such as in weather forecasting. The mathematical analysis of such problems and study of the classical methods for their solution are fundamental for understanding both practical methods of solution and the nature of the solution which may be obtained. Thus it imparts knowledge and insight into the optimal choice of available methods (software) or ability to develop such techniques for the practical problems at hand.
Aims :
- To introduce the analysis of nonlinear optimization problems.
- To present the classical methods for solving nonlinear optimization problems with and without constraints, and nonlinear equations.
Syllabus summary :
Linesearch and trust-region methods for unconstrained optimization problems (steepest descent, Newton's method); conjugate gradient method; linear and nonlinear least-squares. First- and second-order optimality conditions for constrained optimization problems; overview of methods for constrained problems (active-set methods, sequential quadratic programming, interior point methods, penalty methods, filter methods). |
Information for Visiting Students
| Pre-requisites |
None |
| Displayed in Visiting Students Prospectus? |
Yes |
Course Delivery Information
|
| Delivery period: 2010/11 Semester 1, Available to all students (SV1)
|
WebCT enabled: Yes |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| King's Buildings | Lecture | | 1-11 | | | 11:10 - 13:00 | | |
| First Class |
Week 1, Wednesday, 11:10 - 13:00, Zone: King's Buildings. JCMB, room 1501 |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
Stationery Requirements |
Comments |
| Main Exam Diet S1 (December) | Non Linear Optimization | 2:00 | 16 sides | VS1 only | | Main Exam Diet S2 (April/May) | | 2:00 | 16 sides. No YAF | c/w MATH11033. To be held in the morning of the same day as MATH11031. |
|
| Delivery period: 2010/11 Semester 1, Part-year visiting students only (VV1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| King's Buildings | Lecture | | 1-11 | | | 11:10 - 13:00 | | |
| First Class |
Week 1, Wednesday, 11:10 - 13:00, Zone: King's Buildings. JCMB, room 1501 |
| No Exam Information |
Summary of Intended Learning Outcomes
| Understanding the construction and main solution ideas for nonlinear optimization problems. Ability to assess the quality of available methods and solutions for such problems, as well as to potentially develop such optimization techniques and implementations. |
Assessment Information
Examination 70%
Continuous assessment 30% |
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 |
NLO |
Contacts
| Course organiser |
Dr Tom Mackay
Tel: (0131 6)50 5058
Email: T.Mackay@ed.ac.uk |
Course secretary |
Mrs Alison Fairgrieve
Tel: (0131 6)50 6427
Email: Alison.Fairgrieve@ed.ac.uk |
|
copyright 2011 The University of Edinburgh -
31 January 2011 8:00 am
|
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