Robust canonical duality theory for solving nonconvex programming problems under data uncertainty

Linsong Shen, Yanjun Wang () and Xiaomei Zhang
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Linsong Shen: Shanghai University of Finance and Economics
Yanjun Wang: Shanghai University of Finance and Economics
Xiaomei Zhang: Shanghai University of Finance and Economics

Mathematical Methods of Operations Research, 2016, vol. 84, issue 1, No 8, 183-204

Abstract: Abstract This paper presents a robust canonical duality–triality theory for solving nonconvex programming problems under data uncertainty. This theory includes a robust canonical saddle-point theorem and robust canonical optimality conditions, which can be used to identify both robust global and local extrema of the primal problem. Two numerical examples are presented to illustrate that the robust Triality theory is particularly powerful for solving nonconvex optimization problems with data uncertainty.

Keywords: Robust canonical duality–triality; Robust canonical optimality conditions; Nonconvex programming; Data uncertainty (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s00186-016-0539-z

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