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Linear Programming in Linear Time When the Dimension Is Fixed
Open accessAbstract
It is demonstrated that the linear programming problem in d variables and n constraints can be solved in O(n) time when d is fixed.This bound follows from a multidimensional search technique which is applicable for quadratic programming as well.There is also developed an algorithm that is polynomial in both n and d provided d is bounded by a certain slowly growing function of n.
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Cited by
Learnability and the Vapnik-Chervonenkis dimension
Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1989) · Journal of the ACM · doi:10.1145/76359.76371 · Cited by 1,839
New applications of random sampling in computational geometry
Clarkson, K. L. (1987) · Discrete & Computational Geometry · doi:10.1007/bf02187879 · Cited by 317
Efficient algorithms for geometric optimization
Agarwal, P. K., & Sharir, M. (1998) · ACM Computing Surveys · doi:10.1145/299917.299918 · Cited by 257
Metadata
- Authors
- Megiddo, N.
- Venue
- Journal of the ACM
- Published
- 1984-01
- Volume
- 31
- Issue
- 1
- Pages
- 114-127
- ISSN
- 0004-5411 (ISSN-L)
- 1557-735X
- OpenAlex
- W1993058867
- Cited by
- 669
- References
- 0
- FWCI
- 14.16
- Type
- journal article
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: Linear Programming in Linear Time When the Dimension Is Fixed. https://app.cochat.ai/papers/10.1145/2422.322418
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- Megiddo, N. (1984). Linear Programming in Linear Time When the Dimension Is Fixed. Journal of the ACM. https://doi.org/10.1145/2422.322418
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.
