  Non-negative and bounded-variable linear regression problems arise in a
variety of applications in machine learning and signal processing. In this
paper, we propose a technique to accelerate existing solvers for these problems
by identifying saturated coordinates in the course of iterations. This is akin
to safe screening techniques previously proposed for sparsity-regularized
regression problems. The proposed strategy is provably safe as it provides
theoretical guarantees that the identified coordinates are indeed saturated in
the optimal solution. Experimental results on synthetic and real data show
compelling accelerations for both non-negative and bounded-variable problems.
