joint work with Federico Galvanin
In many real world applications, sequential model-based design of experiments (MBDoE) methods employed in online model-based optimization platforms may involve more than one objective in order to drive the real process towards optimal targets, while guaranteeing the satisfaction of quality, cost and operational constraints. In this work, a flexible multi-objective optimal design of experiments framework suitable for online modelling platforms is proposed. The proposed framework is integrated in an online model identification platform for studying the kinetics of chemical reaction systems.
joint work with Sebastian Peitz
It is a very challenging task to identify the objectives on which a certain decision was based, in particular if several, potentially conflicting criteria are equally important and a continuous set of optimal compromise decisions exists. This task can be understood as the inverse problem of multiobjective optimization, where the goal is to find the objective vector of a given Pareto set. To this end, we present a method to construct the objective vector of a multiobjective optimization problem (MOP) such that the Pareto critical set contains a given set of data points.
joint work with Daniel Nowak
The number of tasks performed on wireless devices is steadily increasing. Due to limited resources on these devices computation offloading became a relevant concept. In order to minimize their own task completion time, users can offload parts of their computation task to a cloudlet with limited power. We investigate the resulting nonconvex generalized Nash game, where the time restriction from offloading is formulated as a vanishing constraint. We show that a unique Nash exists and that the price of anarchy is one, i.e. the equilibrium coincides with a centralized solution.