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Distributed and Streaming Linear Programming in Low Dimensions

Summary: Derives nearly tight upper and lower bounds for LP and LP-type problems in streaming and distributed big-data models when the number of constraints far exceeds a constant-dimensional variable space. Focuses on efficient algorithms and matching hardness for low-dimensional ML tasks (robust regression, SVMs, core vector machines) to enable scalable LP-type query processing on massive datasets. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1791
Venue
PODS
Year
2019
Pagerank
5.3483178e-05
Overall Rank
8,929 | 38.74%
DOI
10.1145/3294052.3319697

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{assadi_pods19,
        address = {New York, NY, USA},
        series = {{PODS} '19},
        title = {{Distributed and Streaming Linear Programming in Low Dimensions}},
        url = {https://dl.acm.org/doi/10.1145/3294052.3319697},
        doi = {10.1145/3294052.3319697},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Assadi, Sepehr and Karpov, Nikolai and Zhang, Qin},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
11,145 Parallel Communication Obliviousness: One Round and Beyond 2024 PODS 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
490 Design and Implementation of the LogicBlox System 2015 SIGMOD 0.000175757
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