.png){kind=icon}
.png){kind=icon}
Return
Inverse optimization for linearly constrained convex separable programming problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2009.01.043.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 l(1) and l(2) norms. These inverse optimization problems are either linear programming when l(1) norm is used in the formulation, or convex quadratic separable programming when l(2) norm is used. (C) 2009 Elsevier B.V. All rights reserved.
Keywords:
Inverse optimization
Convex separable program
KKT conditions
Linear programming
Quadratic programming
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W