Smoothed Analysis of Algorithms: Why the Simplex Algorithm Usually Takes Polynomial Time
<|reference_start|>Smoothed Analysis of Algorithms: Why the Simplex Algorithm Usually Takes Polynomial Time: We introduce the smoothed analysis of algorithms, which is a hybrid of the worst-case and average-case analysis of algorithms. In smoothed analysis, we measure the maximum over inputs of the expected performance of an algorithm under small random perturbations of that input. We measure this performance in terms of both the input size and the magnitude of the perturbations. We show that the simplex algorithm has polynomial smoothed complexity.<|reference_end|>
@article{spielman2001smoothed,
    title={Smoothed Analysis of Algorithms: Why the Simplex Algorithm Usually Takes
  Polynomial Time},
    author={Daniel A. Spielman and Shang-Hua Teng},
    journal={arXiv preprint arXiv:cs/0111050},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0111050},
    primaryClass={cs.DS}
}