In many machine learning models, such as nonnegative regression and box regression, the optimization variables are constrained. Therefore, one needs to find an optimal solution only over a specific region of the optimization space, referred to as the feasible region in optimization parlance. The straightforward use of a gradient-descent procedure does not work, because an unconstrained step might move the optimization variables outside the feasible region.