  Group-Lasso (gLasso) identifies important explanatory factors in predicting
the response variable by considering the grouping structure over input
variables. However, most existing algorithms for gLasso are not scalable to
deal with large-scale datasets, which are becoming a norm in many applications.
In this paper, we present a divide-and-conquer based parallel algorithm
(DC-gLasso) to scale up gLasso in the tasks of regression with grouping
structures. DC-gLasso only needs two iterations to collect and aggregate the
local estimates on subsets of the data, and is provably correct to recover the
true model under certain conditions. We further extend it to deal with
overlappings between groups. Empirical results on a wide range of synthetic and
real-world datasets show that DC-gLasso can significantly improve the time
efficiency without sacrificing regression accuracy.
