Flows in almost linear time via adaptive preconditioning-Reference-Cited by-同舟云学术

Flows in almost linear time via adaptive preconditioning

Published:2019-06-23 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing
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Author:

Kyng Rasmus¹,Peng Richard²,Sachdeva Sushant³,Wang Di⁴

Affiliation:

1. Harvard University, USA

2. Georgia Tech, USA / Microsoft Research, USA

3. University of Toronto, Canada

4. Georgia Tech, USA

Funder

Connaught Fund

Office of Naval Research

National Science Foundation

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3313276.3316410

Reference86 articles.

1. Ravindra K. Ahuja Thomas L. Magnanti and James B. Orlin. 1993. Ravindra K. Ahuja Thomas L. Magnanti and James B. Orlin. 1993.

2. Network flows - theory algorithms and applications. Prentice Hall. Network flows - theory algorithms and applications. Prentice Hall.

3. Morteza Alamgir and Ulrike V Luxburg. 2011. Phase transition in the family of p-resistances. In Advances in Neural Information Processing Systems. 379–387. Morteza Alamgir and Ulrike V Luxburg. 2011. Phase transition in the family of p-resistances. In Advances in Neural Information Processing Systems. 379–387.

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