Smoothed analysis of algorithms
2004; Association for Computing Machinery; Volume: 51; Issue: 3 Linguagem: Inglês
10.1145/990308.990310
ISSN1557-735X
AutoresDaniel A. Spielman, Shang‐Hua Teng,
Tópico(s)Machine Learning and Algorithms
ResumoWe introduce the smoothed analysis of algorithms , which continuously interpolates between the worst-case and average-case analyses 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 smoothed complexity polynomial in the input size and the standard deviation of Gaussian perturbations.
Referência(s)