The Parameterized Complexity Analysis of Partition Sort for Negative Binomial Distribution Inputs

TL;DR

This paper investigates how the Partition sort algorithm's performance varies with negative binomial distribution inputs, revealing greater sensitivity to distribution parameters compared to binomial inputs, with significant effects from parameters and input size.

Contribution

It provides the first detailed complexity analysis of Partition sort on negative binomial inputs, highlighting parameter sensitivity and interaction effects.

Findings

Partition sort is sensitive to negative binomial distribution parameters.

Main and interaction effects are more significant for negative binomial inputs.

Algorithm performance varies notably with input distribution parameters.

Abstract

The present paper makes a study on Partition sort algorithm for negative binomial inputs. Comparing the results with those for binomial inputs in our previous work, we find that this algorithm is sensitive to parameters of both distributions. But the main effects as well as the interaction effects involving these parameters and the input size are more significant for negative binomial case.