Data envelopment analysis (DEA) in massive data sets
Handbook of massive data sets
Using lexicographic parametric programming for identifying efficient units in DEA
Computers and Operations Research
A dimensional decomposition approach to identifying efficient units in large-scale DEA models
Computers and Operations Research
A procedure for large-scale DEA computations
Computers and Operations Research
Distributed identification of the lineality space of a cone
The Journal of Supercomputing
An Algorithm to Find the Lineality Space of the Positive Hull of a Set of Vectors
Journal of Mathematical Modelling and Algorithms
An Algorithm for Data Envelopment Analysis
INFORMS Journal on Computing
Point sets and frame algorithms in management
AAIM'05 Proceedings of the First international conference on Algorithmic Applications in Management
An Algorithm for Data Envelopment Analysis
INFORMS Journal on Computing
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We present an algorithm for identifying the extreme rays of the conical hull of a finite set of vectors whose generated cone is pointed. This problem appears in diverse areas including sto-chastic programming, computational geometry, and non-parametric efficiency measurement. The standard approach consists of solving a linear program for every element of the set of vectors. The new algorithm differs in that it solves fewer and substantially smaller LPs. Extensive computational testing vali-dates the algorithm and demonstrates that for a wide range of problems it is computationally superior to the standard approach.