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Non-negative and bounded-variable linear regression problems arise in avariety of applications in machine learning and signal processing. In thispaper, we propose a technique to accelerate existing solvers for these problemsby identifying saturated coordinates in the course of iterations. This is akinto safe screening techniques previously proposed for sparsity-regularizedregression problems. The proposed strategy is provably safe as it providestheoretical guarantees that the identified coordinates are indeed saturated inthe optimal solution. Experimental results on synthetic and real data showcompelling accelerations for both non-negative and bounded-variable problems.

Accelerating Non-Negative and Bounded-Variable Linear Regression Algorithms with Safe Screening