Surrogate duality for robust optimization

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Abstract

Robust optimization problems, which have uncertain data, are considered. We prove surrogate duality theorems for robust quasiconvex optimization problems and surrogate min-max duality theorems for robust convex opti-mization problems. We give necessary and sufficient constraint qualifications for surrogate duality and surrogate min-max duality, and show some exam-ples at which such duality results are used effectively. Moreover, we obtain a surrogate duality theorem and a surrogate min-max duality theorem for semi-definite optimization problems in the face of data uncertainty.

Journal

  • European Journal of Operational Research

    European Journal of Operational Research 231(2), 257-262, 2013-12-01

Codes

  • NII Article ID (NAID)
    120005647348
  • NII NACSIS-CAT ID (NCID)
    AA0017802X
  • Text Lang
    ENG
  • Article Type
    journal article
  • ISSN
    0377-2217
  • Data Source
    IR 
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