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- Bert Besser, Matthias Poloczek
- Algorithmica
- 2015

Since Tinhofer proposed the MinGreedy algorithm for maximum cardinality matching in 1984, several experimental studies found the randomized algorithm to perform excellently for various classes of random graphs and benchmark instances. In contrast, only few analytical results are known. We show that MinGreedy cannot improve on the trivial approximation ratio… (More)

- Carlos Molina, Björn Rotter, +8 authors Peter Winter
- BMC Genomics
- 2008

Drought is the major constraint to increase yield in chickpea (Cicer arietinum). Improving drought tolerance is therefore of outmost importance for breeding. However, the complexity of the trait allowed only marginal progress. A solution to the current stagnation is expected from innovative molecular tools such as transcriptome analyses providing insight… (More)

- Bert Besser
- ArXiv
- 2014

We consider the MinGreedy strategy for Maximum Cardinality Matching. MinGreedy repeatedly selects an edge incident with a node of minimum degree. For graphs of degree at most ∆ we show that MinGreedy achieves approximation ratio at least ∆−1 2∆−3 in the worst case and that this performance is optimal among adaptive priority algorithms in the vertex model,… (More)

- Bert Besser, Matthias Poloczek
- Algorithmica
- 2017

In “Greedy Matching: Guarantees and Limitations” we erroneously claimed in Theorem 5 that no fully randomized priority algorithm for the maximum matching problem can achieve an expected approximation ratio better than $$\frac{5}{6}$$ 5 6 . This bound and the provided argument hold for degree-based randomized priority algorithms. For fully randomized… (More)

- Bert Besser, Bastian Werth
- ArXiv
- 2015

In the design of greedy algorithms for the maximum cardinality matching problem the utilization of degree information when selecting the next edge is a well established and successful approach. We define the class of “degree sensitive” greedy matching algorithms, which allows us to analyze many well-known heuristics, and provide tight approximation… (More)

- Bert Besser
- 2016

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