Big Square Small Square is a geometrical branch-and-bound algorithm, devised by P. Hansen et al, for the solution of constrained planar minimum single-facility location problems with Lp norms and continuous non-decreasing costfunctions. The method basically works by splitting the studied planar region into squares, and either rejecting or further processing these squares by the evaluation of a lower bound. The paper presents a modified version of this algorithm aimed at correcting a small failure to converge, accelerating the calculations, minimising the information to be stored, and, most importantly, determining a region of near-optimality. Furthermore the method is applicable to any planar single-facility problem with distances measured by mixed norms and as an objective any continuous function of the distances. This includes nearly all the models which have been proposed in the literature.
