LU Decomposition in DEA with an Application to Hospitals

Mehdi Toloo*, Rahele Jalili

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


A fundamental problem that usually appears in linear systems is to find a vector (Formula presented.) satisfying (Formula presented.). This linear system is encountered in many research applications and more importantly, it is required to be solved in many contexts in applied mathematics. LU decomposition method, based on the Gaussian elimination, is particularly well suited for spars and large-scale problems. Linear programming (LP) is a mathematical method to obtain optimal solutions for a linear system that is more being considered in various fields of study in recent decades. The simplex algorithm is one of the mostly used mathematical techniques for solving LP problems. Data envelopment analysis (DEA) is a non-parametric approach based on linear programming to evaluate relative efficiency of decision making units (DMUs). The number of LP models that has to be solved in DEA is at least the same as the number of DMUs. Toloo et al. (Comput Econ 45(2):323–326, 2015) proposed an initial basic feasible solution for DEA models which practically reduces at least 50 % of the whole computations. The main contribution of this paper is in utlizing this solution to implement LU decomposition technique on the basic DEA models which is more accurate and numerically stable. It is shown that the number of computations in applying the Gaussian elimination method will be fairly reduced due to the special structure of basic DEA models. Potential uses are illustrated with applications to hospital data set.

Original languageEnglish
Pages (from-to)473-488
Number of pages16
JournalComputational Economics
Issue number3
Publication statusPublished - Mar 1 2016
Externally publishedYes


  • Computational complexity
  • Data envelopment analysis (DEA)
  • LU decomposition
  • Simplex algorithm

ASJC Scopus subject areas

  • Economics, Econometrics and Finance (miscellaneous)
  • Computer Science Applications

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