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.

翻译：我们研究基于随机过程的建设性差异最小化的统一办法和算法,通过改变过程参数,可以回收各种最新结果。我们通过为平滑的事例得出差异来显示方法的灵活性,这些事例在已知的最坏事例和随机事例的界限之间相互交错。