DEA is a well-established, widely used, and powerful analytical resource in the toolbox of
the OR/MS analyst. It is used to assess relative efficiency of many, functionally similar …

Consider two finite sets 𝒜 and 𝒱 of points in m-dimensional space. The convex hull of 𝒜 and
the conical hull of 𝒱 can be combined to create a finitely generated unbounded polyhedron …

Abstract Data Envelopment Analysis (DEA) is a clustering methodology for records in data
sets corresponding to entities sharing a common list of attributes. Broadly defined, DEA …

This paper presents a technique to generate random data in dimensional space m such that
their convex (or positive) hull contains a specific percentage of extreme points (or vectors) …

A distributed approach is described for solving lineality (or linearity) space (LS) problems
with large cardinalities and a large number of dimensions. The LS solution has applications …