Parallel Approximate Maximum Flows in Near-Linear Work and Polylogarithmic Depth-Reference-Cited by-同舟云学术

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Parallel Approximate Maximum Flows in Near-Linear Work and Polylogarithmic Depth

Published:2024-01 Issue: Volume: Page:3997-4061
ISSN:
Container-title:Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)
language:en
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Author:

Agarwal Arpit¹,Khanna Sanjeev²,Li Huan²,Patil Prathamesh²,Wang Chen³,White Nathan⁴,Zhong Peilin⁵

Affiliation:

1. Columbia University.

2. University of Pennsylvania. Research supported in part by NSF awards CCF-1934876 and CCF-2008305.

3. Rutgers University., supported in part by NSF CAREER Grant CCF-2047061, a gift from Google Research, and a Rutgers Research Fulcrum Award.

4. University of Pennsylvania. Research supported in part by NSF awards CCF-1934876 and CCF-2008305, and partially supported by the Department of Defense (DoD) through the National Defense Science and Engineering Graduate (NDSEG) Fellowship Program.

5. Google Research.

Publisher

Society for Industrial and Applied Mathematics

Cited by 1 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. A Simpler and Parallelizable O(√log n)-approximation Algorithm for Sparsest Cut;Proceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures;2024-06-17

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