A deterministic parallel algorithm for bipartite perfect matching

Author:

Fenner Stephen1,Gurjar Rohit2,Thierauf Thomas3

Affiliation:

1. University of South Carolina, Columbia, SC

2. California Institute of Technology, Pasadena, CA

3. Aalen University, Germany

Abstract

A fundamental quest in the theory of computing is to understand the power of randomness. It is not known whether every problem with an efficient randomized algorithm also has one that does not use randomness. One of the extensively studied problems under this theme is that of perfect matching. The perfect matching problem has a randomized parallel (NC) algorithm based on the Isolation Lemma of Mulmuley, Vazirani, and Vazirani. It is a long-standing open question whether this algorithm can be derandomized. In this article, we give an almost complete derandomization of the Isolation Lemma for perfect matchings in bipartite graphs. This gives us a deterministic parallel (quasi-NC) algorithm for the bipartite perfect matching problem. Derandomization of the Isolation Lemma means that we deterministically construct a weight assignment so that the minimum weight perfect matching is unique. We present three different ways of doing this construction with a common main idea.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Strong Algebras and Radical Sylvester-Gallai Configurations;Proceedings of the 56th Annual ACM Symposium on Theory of Computing;2024-06-10

2. Parallel Algorithms for Equilevel Predicates;Proceedings of the 25th International Conference on Distributed Computing and Networking;2024-01-04

3. Generator-as-A-Matcher: Joint Tracklet Matching and Gap Filling to Alleviate Perceptual Sparsity in Roadside Mm-Wave Radar;2024

4. Demystifying the border of depth-3 algebraic circuits;2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS);2022-02

5. Implementation of Parallel Algorithm Technology for Time Series Data Mining;Journal of Physics: Conference Series;2021-11-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3