Inverse optimization for multi-objective linear programming
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This paper generalizes inverse optimization for multi-objective linear programming where we are looking for the least problem modifications to make a given feasible solution a weak efficient solution. This is a natural extension of inverse optimization for single-objective linear programming with regular “optimality” replaced by the “Pareto optimality”. This extension, however, leads to a non-convex optimization problem. We prove some special characteristics of the problem, allowing us to solve the non-convex problem by solving a series of convex problems.
KeywordsMulti-objective linear programming Linear programming Inverse optimization Efficiency
This work was partially supported by the MSK Cancer Center Support Grant/Core Grant (P30 CA008748).
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