Understanding Zadimoghaddam's Edge-weighted Online Matching Algorithm: Weighted Case
Data Structures and Algorithms
2019-10-09 v1 Computer Science and Game Theory
Abstract
This article presents a simplification of Zadimoghaddam's algorithm for the edge-weighted online bipartite matching problem, under the online primal dual framework. In doing so, we obtain an improved competitive ratio of . We first combine the online correlated selection (OCS), an ingredient distilled from Zadimoghaddam (2017) by Huang and Tao (2019), and an interpretation of the edge-weighted online bipartite matching problem by Devanur et al. (2016) which we will refer to as the complementary cumulative distribution function (CCDF) viewpoint, to derive an online primal dual algorithm that is -competitive. Then, we design an improved OCS which gives the ratio.
Keywords
Cite
@article{arxiv.1910.03287,
title = {Understanding Zadimoghaddam's Edge-weighted Online Matching Algorithm: Weighted Case},
author = {Zhiyi Huang},
journal= {arXiv preprint arXiv:1910.03287},
year = {2019}
}