Data Envelopment Analysis (DEA): Algorithms, Computations, and Geometry

José H. Dulá ()
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José H. Dulá: University of Alabama

Chapter Chapter 2 in Data Science and Productivity Analytics, 2020, pp 35-56 from Springer

Abstract: Abstract Data Envelopment Analysis (DEA) has matured but remains vibrant and relevant, in part, because its algorithms, computational experience, and geometry have a broad impact within and beyond the field. Algorithmic, computational, and geometric results in DEA allow us to solve larger problems faster; they also contribute to various other fields including computational geometry, statistics, and machine learning. This chapter reviews these topics from a historical viewpoint, as they currently stand, and as to how they will evolve in the future.

Date: 2020
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DOI: 10.1007/978-3-030-43384-0_2

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