  Smoothed analysis is a framework for analyzing the complexity of an
algorithm, acting as a bridge between average and worst-case behaviour. For
example, Quicksort and the Simplex algorithm are widely used in practical
applications, despite their heavy worst-case complexity. Smoothed complexity
aims to better characterize such algorithms. Existing theoretical bounds for
the smoothed complexity of sorting algorithms are still quite weak.
Furthermore, empirically computing the smoothed complexity via its original
definition is computationally infeasible, even for modest input sizes. In this
paper, we focus on accurately predicting the smoothed complexity of sorting
algorithms, using machine learning techniques. We propose two regression models
that take into account various properties of sorting algorithms and some of the
known theoretical results in smoothed analysis to improve prediction quality.
We show experimental results for predicting the smoothed complexity of
Quicksort, Mergesort, and optimized Bubblesort for large input sizes, therefore
filling the gap between known theoretical and empirical results.
