Course Objective

An introduction into optimization, and in particular deterministic
optimization. One aim is to learn how to model a practical optimization
problem into the appropriate mathematical formulation. The other is to
learn the theory and application of solution methods for general classes
of optimization problems.

Course Content

This is an introductory course in deterministic optimization. The
optimization models studied are unconstrained non-linear optimization,
constrained non-linear optimization, convex optimization, linear
optimization and integer linear optimization. Solution techniques for
these classes of optimization problems are the central theme of this
course. Another important element of the course is the mathematical
formulation of (practical) verbally described problems as instances of
the optimization models, and application of the solution methods to
solve the resulting problems.

Teaching Methods

Lectures: 2 hours per week. Tutorials: 2 hours per week

Method of Assessment

Separate exams for the first half (period 1) and second half (period 2)
of the course are held at the end of each period. The overall grade is
the average of these two partial exams.

Individual assignments, such as short quizzes or written feedback on
aspects of the course - will be assigned as the course progresses.
Failure to participate will result in a penalty to the overall grade.

A re-sit combines the two parts into one exam.

Entry Requirements

Linear Algebra and Analysis

Literature

H.A. Taha: Operations Research: An Introduction, International Edition,
9th or 10th Ed., Pearson.

Target Audience

2nd-year students Econometrics and Operations Research, Applied
Mathematics students

Additional Information

The course is suitable to be taken in an exchange progam for students
who have successfully completed courses in Linear Algebra and Analysis.