  In subset selection we search for the best linear predictor that involves a
small subset of variables. From a computational complexity viewpoint, subset
selection is NP-hard and few classes are known to be solvable in polynomial
time. Using mainly tools from discrete geometry, we show that some sparsity
conditions on the original data matrix allow us to solve the problem in
polynomial time.
