Professor Dr. Kathrin Klamroth
Bergische Universität Wuppertal
Dienstag, 4. Dezember 2018
Raum 2004 (L1)
über das Thema:
Multi-objective combinatorial optimization (MOCO) is a quickly growing field that is highly relevant for a multitude of application areas and at the same time highly challenging due to its inherent complexity. Typical examples of MOCO problems include multi-objective knapsack and assignment problems, the multi-objective TSP, and network problems like multi-objective minimum spanning tree, shortest path, and minimum cost flow problems.|
While MOCO problems are relatively well understood in the bi-objective case, they are intrinsically difficult in three and more dimensions. The exact determination of the Pareto set, as well as its approximation with guaranteed accuracy, are challenging particularly in three dimensions and beyond.
The talk will start with a brief review of recent developments in MOCO. We discuss the complexity of MOCO problems, identify cases where they are actually easy, and discuss general concepts such as generic scalarization based algorithms, branch and bound methods, and upper and lower bound sets. This leads to the question of concisely describing the search region and efficiently updating intersections of polyhedral cones.
|Hierzu ergeht herzliche Einladung.|
|Prof. Dr. Mirjam Dür|
Kaffee, Tee und Gebäck eine halbe Stunde vor Vortragsbeginn im Raum 2006 (L1).