Trade-off preservation in inverse multi-objective convex optimization
Trade-off preservation in inverse multi-objective convex optimization
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DOI：
10.1016/j.ejor.2018.02.045
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发表时间：
2018-10-01
影响因子：
6.4
通讯作者：
Lee, Taewoo
中科院分区：
文献类型：
--
作者：
Chan, Timothy C. Y.;Lee, Taewoo
给定一个可能不是帕累托最优的输入解决方案，我们提出了一种新的多目标凸优化逆优化方法，该方法确定产生弱帕累托最优解决方案的权重向量，该弱帕累托最优解决方案保留了决策者在输入解决方案中编码的权衡意图。我们引入了权衡保留的概念，将其用作近似输入解决方案的相似性度量，并展示了其与最小化最优性差距的联系。我们提出了逆模型的线性近似和连续线性规划算法，在权衡保留和计算效率之间取得平衡，并表明我们的模型包含了文献中的许多现有逆优化模型。我们使用前列腺癌放射治疗的临床数据证明了所提出的方法。 (C) 2018 Elsevier B.V. 保留所有权利。
Given an input solution that may not be Pareto optimal, we present a new inverse optimization methodology for multi-objective convex optimization that determines a weight vector producing a weakly Pareto optimal solution that preserves the decision maker's trade-off intention encoded in the input solution. We introduce a notion of trade-off preservation, which we use as a measure of similarity for approximating the input solution, and show its connection with minimizing an optimality gap. We propose a linear approximation to the inverse model and a successive linear programming algorithm that balance between trade-off preservation and computational efficiency, and show that our model encompasses many of the existing inverse optimization models from the literature. We demonstrate the proposed method using clinical data from prostate cancer radiation therapy. (C) 2018 Elsevier B.V. All rights reserved.