probably bitten off more than I can chew. Needed skills: Basic knowledge in exact and/or (meta-)heuristic optimization methods (e.g., from Heuristic Optimization Techniques, Algorithmics). The area contains both topics with a stronger practical focus (including evaluation) or with a more theoretical focus. Therefore, the problem of finding planarizing perfect pseudo matchings is strongly related to the cycle double cover conjecture. In this project, an A -based algorithm shall be developed and compared to existing methods in the literature. However, implementing it is turning out to be a major pain. The second part studies optimization algorithms designed for integrating information coming from different sources. This technique improves many algorithms suffering from running time and/or space depending heavily on the dimensionality. The rflcs seeks a longest common subsequence of its input strings with the additional property that each letter appears only at most once. As snarks are very well studied and there are effective techniques to generate all snarks up to a certain size we can use this techniques to generate a large number of test instances for our problem.
We are always looking for enthusiastic young people who are interested in a research project or thesis in the Bachelor, Master, and PhD programs. This feels iffy to me: master's thesis is supposed to be a sign of academic maturity, but nothing seems more unacademic to me than publishing an untested and unproven idea in the form of thesis, and not even having a working implementation. We design improved algorithms for two important problems known as rank aggregation and consensus clustering.
The longest common subsequence (LCS) of a set of input strings is a string that is a subsequence of all input strings and has maximum length. Recently, the computational aspects of these problems have been studied, and several hardness results were proved. Scientifically, the area offers a wide spectrum of topics for student theses, from theoretical questions (e.g., existence of certain layouts or algorithms with certain properties) to practical questions of modeling the actual requirements of a particular application and designing, implementing, and evaluating algorithms for solving. Some recent research in this direction can be found here and here. As a part of the thesis I wanted to implement the algorithm. Examples of algorithmic problems comprise label placement for map features, algorithms for constructing cartograms, or schematic destination maps. A pseudo matching is planarizing if the graph after contracting all connected components of the pseudo matching is planar. The types of maps range from dynamic and interactive maps,.g., on smart phones and mobile devices, to unconventional diagrammatic or schematic maps. Therefore, it is especially interesting for us to find planarizing perfect pseudo matchings in the class of all snarks.