Quadratic Time Algorithms Appear to be Optimal for Sorting Evolving Data

@inproceedings{Vial2018QuadraticTA,
  title={Quadratic Time Algorithms Appear to be Optimal for Sorting Evolving Data},
  author={Juan Jos{\'e} Besa Vial and William E. Devanny and David Eppstein and Michael T. Goodrich and Timothy Johnson},
  booktitle={ALENEX},
  year={2018}
}
We empirically study sorting in the evolving data model. In this model, a sorting algorithm maintains an approximation to the sorted order of a list of data items while simultaneously, with each comparison made by the algorithm, an adversary randomly swaps the order of adjacent items in the true sorted order. Previous work studies only two versions of quicksort, and has a gap between the lower bound of Omega(n) and the best upper bound of O(n log log n). The experiments we perform in this paper… 

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