In this seminar, research of the DiDy Group is presented, and other research related topics are discussed. This semester, the faculty will introduce themselves / their research.

Combinatorial optimization minimizes a “cost” function. When true costs are involved – in terms of money, energy, computation time – it is not a problem to optimize under multiple objectives. True costs simply add up. But what if our objectives are incommensurable:

• Sequence similarity versus folding energy?
  Preservation of specific motifs versus overall conservation?
  Maximizing similarity versus minimizing partner switching?

Should we choose love over gold, gold over love, or what else?

Pareto optimization avoids the artificial combination of diverse objectives. It gives the best of two worlds in a mathematically strict sense, is safe to use, and has efficient implementations.