Superiorization is an iterative method for constrained optimization. It is used for improving the efficacy of an iterative method whose convergence is resilient to certain kinds of perturbations. Such perturbations are designed to "force" the perturbed algorithm to produce more useful results for the intended application than the ones that are produced by the original iterative algorithm. The perturbed algorithm is called the superiorized version of the original unperturbed algorithm. If the original algorithm is computationally efficient and useful in terms of the target application and if the perturbations are inexpensive to calculate, the method may be used to steer iterates without additional computation cost.