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Although the MapReduce framework is now the de facto standard for analyzing massive data sets, many algorithms (in particular, many iterative algorithms popular in machine learning, optimization, and linear algebra) are hard to fit into MapReduce. Consider, e.g., the p regression problem: given a matrix A ∈ R m×n and a vector b ∈ R m , find a vector x * ∈ R n that minimizes f (x) = Ax − b p . The widely-used 2 regression, i.e., linear least-squares, is known to be highly sensitive to outliers;dblp:conf/icml/MengM13 fatcat:mnzpqvktdjeyxepluv6ycqkvye