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Data driven matrix uncertainty for robust linear programming

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  • Soyster, A.L.
  • Murphy, F.H.

Abstract

In this paper we consider robust linear programs with uncertainty sets defined by the convex hull of a finite number of m×n matrices. Embedded within the matrices are related robust linear programs defined by the rows, columns, and coefficients of the matrices. This results in a nested set of primal (and dual) linear programs with predictably different optimal objective values. The set of matrices also embed a covariance structure for the matrix coefficients and we show that when negative covariances predominate in the rows, more favorable optimal objective values for the primal can be expected.

Suggested Citation

  • Soyster, A.L. & Murphy, F.H., 2017. "Data driven matrix uncertainty for robust linear programming," Omega, Elsevier, vol. 70(C), pages 43-57.
    • Handle: RePEc:eee:jomega:v:70:y:2017:i:c:p:43-57
    DOI: 10.1016/j.omega.2016.09.001
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    References listed on IDEAS

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