Postgraduate Course: Topics in Applied Optimization (MATH11194)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Optimization comes in many flavours: linear and nonlinear; unconstrained and constrained; convex and non-convex; continuous and discrete; deterministic and stochastic. The fundamentals of optimization are studied in the core Operational Research MSc course of that name, but it is not possible to cover all the many variants of the subject, even though each is critical to the solution of some practical problem. Topics in Optimization will study the mathematics of a range of optimization problems and their application. |
| Course description |
This course will study the mathematics of a range of optimization problems and their application. The syllabus will vary from year to year. Possible topics include:
- Unconstrained optimization
- Nonlinear constrained optimization
- Stochastic optimization
- Mixed-integer optimization
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Course Delivery Information
|
| Academic year 2018/19, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Supervised Practical/Workshop/Studio Hours 3,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
66 )
|
| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
20% continuous assessment
80% examination |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | Topics in Applied Optimization (MATH11194) | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- identify the mathematical nature of a given optimization problem
- analyse a range of classes of optimization problems
- identify solution methods for the optimization problems studied
- identify suitable software to solve the optimization problems studied
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Reading List
S. Boyd and L. Vandenberghe, Convex Optimization, CUP
R. Fletcher, Practical Methods of Optimization, Wiley
Nocedal and S. J. Wright, Numerical Optimization, Springer
L. A. Wolsey, Integer Programming, Wiley |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | TAO,Optimization,Applications |
Contacts
| Course organiser | Dr Andreas Grothey
Tel: (0131 6)50 5747
Email: Andreas.Grothey@ed.ac.uk |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: f.c.reid@ed.ac.uk |
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