In optimization, the aim is to find the best, the minimum or maximum, solution to a problem formulated in mathematical terms. This course covers theory and algorithms for optimization when the problem is non-linear, which is common in a number of applications. The course considers problems with and without non-linear constraints. There is a vast freedom in how these problems can be formulated and solved. Thus, the course encompasses different optimization approaches and algorithms, as well as the theory of non-linear problems, optimality conditions, rate of convergence, sensitivity analysis, and least squares problems. Furthermore, the course covers how this knowledge can be applied in two or three selected application areas, such as geometrical measurement problems and design optimization. The course includes computer labs to provide proficiency training and increased understanding of the selected applications. Altogether, this equips the students with the tools needed to solve many important problems.
