The DOI matches its Crossref record.
Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms
Open accessAbstract
In this paper, we establish max-flow min-cut theorems for several important classes of multicommodity flow problems.In particular, we show that for any n-node multicommodity flow problem with uniform demands, the max-flow for the problem is within an O(log n) factor of the upper bound implied by the min-cut.The result (which is existentially optimal) establishes an important analogue of the famous 1-commodity max-flow min-cut theorem for problems with multiple commodities.The result also has substantial applications to the field of approximation algorithms.For example, we use the flow result to design the first polynomial-time (polylog n-times-optimal) approximation algorithms for well-known NP-hard optimization problems such as graph partitioning, min-cut linear arrangement, crossing number, VLSI layout, and minimum feedback arc set.Applications of the flow results to path routing problems, network reconfiguration, communication in distributed networks, scientific computing and rapidly mixing Markov chains are also described in the paper.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Spielman, D. A., & Teng, S.-H. (2013) · SIAM Journal on Computing · doi:10.1137/080744888 · Cited by 316
Algorithmic applications of low-distortion geometric embeddings
Indyk, P. (2001) · Proceedings 42nd IEEE Symposium on Foundations of Computer Science · doi:10.1109/sfcs.2001.959878 · Cited by 253
Low Distortion Delaunay Embedding of Trees in Hyperbolic Plane
Sarkar, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-25878-7_34 · Cited by 168
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss
HaoChen, J. Z., Wei, C., Gaidon, A., & Ma, T. (2021) · arXiv · doi:10.48550/arxiv.2106.04156 · Cited by 53
Metadata
- Authors
- Leighton, T., & Rao, S.
- Venue
- Journal of the ACM
- Published
- 1999-11
- Volume
- 46
- Issue
- 6
- Pages
- 787-832
- ISSN
- 0004-5411 (ISSN-L)
- 1557-735X
- OpenAlex
- W2150148016
- Cited by
- 820
- References
- 0
- FWCI
- 13.81
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms. https://app.cochat.ai/papers/10.1145/331524.331526
- The paper
- Leighton, T., & Rao, S. (1999). Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms. Journal of the ACM. https://doi.org/10.1145/331524.331526
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.