In this paper, we develop a computational procedure for the hyperbolic distance function
(HDF) within the nonparametric framework of data envelopment analysis. We convert the
nonlinear HDF model under variable returns to scale into an equivalent conic optimization
problem with linear constraints plus a “toppled ice cream” cone constraint that can be
efficiently solved by specialized interior point methods in about the same time as a linear
program. Applying the dual of an ice cream cone, we formulate a multiplier based (dual) …