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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)
BACKGROUND 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(More)
In the design of greedy algorithms for the maximum car-dinality 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)
We consider the MinGreedy strategy for Maximum Cardi-nality 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)
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)
• Let 0 < 2 < 1 < 1 be given constants. Let an algorithm A output a correct solution with probability at least 1 − 1. State a sufficient and constant number k of repetitions of A such that it outputs at least one correct solution with probability at least 1 − 2. • Let algorithm A run in expected time T (n) and output a correct solution with probability at(More)
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