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We study linear programming and general LP-type problems in several big data(streaming and distributed) models. We mainly focus on low dimensional problemsin which the number of constraints is much larger than the number of variables.Low dimensional LP-type problems appear frequently in various machine learningtasks such as robust regression, support vector machines, and core vectormachines. As supporting large-scale machine learning queries in databasesystems has become an important direction for database research, obtainingefficient algorithms for low dimensional LP-type problems on massive datasetsis of great value. In this paper we give both upper and lower bounds forLP-type problems in distributed and streaming models. Our bounds are almosttight when the dimensionality of the problem is a fixed constant.

Distributed and Streaming Linear Programming in Low Dimensions