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