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Abstract:
We present a method for sparse regression problems. Our method is based on
the nonsmooth trust-region framework that minimizes a sum of smooth convex
functions and a nonsmooth convex regularizer. By employing a separable
quadratic approximation to the smooth part, the method enables the use of proximity
operators, which in turn allow tackling the nonsmooth part efficiently. We
illustrate our method by implementing it for three important sparse regression
problems. In experiments with synthetic and real-world large-scale data, our
method is seen to be competitive, robust, and scalable.
