 Inverse optimization for linearly constrained convex separable programming problemsJianzhong Zhang and Chengxian Xu European Journal of Operational Research, 2010, vol. 200, issue 3, 671-679 Abstract: In this paper, we study inverse optimization for linearly constrained convex separable programming problems that have wide applications in industrial and managerial areas. For a given feasible point of a convex separable program, the inverse optimization is to determine whether the feasible point can be made optimal by adjusting the parameter values in the problem, and when the answer is positive, find the parameter values that have the smallest adjustments. A sufficient and necessary condition is given for a feasible point to be able to become optimal by adjusting parameter values. Inverse optimization formulations are presented with l1 and l2 norms. These inverse optimization problems are either linear programming when l1 norm is used in the formulation, or convex quadratic separable programming when l2 norm is used. Keywords: Inverse; optimization; Convex; separable; program; KKT; conditions; Linear; programming; Quadratic; programming (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:200:y:2010:i:3:p:671-679 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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