Optimisation for Systems and Control

Essentially, almost all engineering problems are optimization problems. If a civil engineer designs a bridge, then one of the main objectives is to obtain the cheapest design or the design that can be implemented most rapidly, where of course several specifications and constraints such as size, strength, safety, etc. have to be taken into account. When developing a new type of engine, we look for the most economical design, the cheapest design, or the design with the highest performance. A process engineer wants a production unit to deliver a final product of maximal quality, with minimal expenditure of energy or with maximal output flow. When composing a portfolio, a financial engineer tries to maximize the expected profits, subject to the given risk constraints. So we encounter optimization problems in almost every engineering field.

How can we solve such an optimization problem? That is the topic that will be addressed in this course. We will in particular discuss several classes of optimization problems and next focus on how to select most efficient numerical algorithms to solve a given optimization problem. The examples and case studies of this course are primarily oriented towards systems and control.

More specifically, the following topics will be treated:

* Linear Programming

* Quadratic Programming

* Nonlinear Optimization

* Constraints in Nonlinear Optimization

* Convex Optimization

* Global Optimization

* Matlab Optimization Toolbox

* Multi-Objective Optimization

* Integer Optimization

Written exam (individual, fully closed-book, no calculators nor formulas sheets allowed, counts for 70% of the final marks) + 2 assignments (group assignments, assessed through written reports, count for 30% of the final marks).

To successfully pass the course the final weighted average score for the exam and the assignments together should be at least 5.75.

Important: Partial marks for the exam or the assignments do not carry over from one academic year to the next.