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: 1759
Venue: PODS
Year: 2019
Pagerank: 4.456315e-05
Overall Rank: 8,759 | 39.07%
DOI: 10.1145/3294052.3319697

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,925	Parallel Communication Obliviousness: One Round and Beyond	2024	PODS	4.1945683e-05

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
613	Design and Implementation of the LogicBlox System	2015	SIGMOD	0.00019181325

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