A Unified Approach to Discrepancy Minimization
Data Structures and Algorithms
2022-05-03 v1
Abstract
We study a unified approach and algorithm for constructive discrepancy minimization based on a stochastic process. By varying the parameters of the process, one can recover various state-of-the-art results. We demonstrate the flexibility of the method by deriving a discrepancy bound for smoothed instances, which interpolates between known bounds for worst-case and random instances.
Cite
@article{arxiv.2205.01023,
title = {A Unified Approach to Discrepancy Minimization},
author = {Nikhil Bansal and Aditi Laddha and Santosh S. Vempala},
journal= {arXiv preprint arXiv:2205.01023},
year = {2022}
}