The course offers a top level view on the area of optimization. The part on unconstrained continuous optimization covers iterative methods ranging from steepest descent to trust region methods The part on constrained continuous optimization covers concepts like duality, convexity, and Lagrange approaches. This part is applied in a project on a topic from electrical engineering. The part on discrete optimization covers mixed integer programming (MIP), elementary graph theory, and concludes by showing how to model several important discrete optimization problems as network LPs.