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Continuous location problems and Big Triangle Small Triangle: constructing better bounds

机译:连续位置问题和大三角小三角:构造更好的边界

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摘要

The Big Triangle Small Triangle method has shown to be a powerful global optimization procedure to address continuous location problems. In the paper published in J. Global Optim. (37:305-319, 2007), Drezner proposes a rather general and effective approach for constructing the bounds needed. Such bounds are obtained by using the fact that the objective functions in continuous location models can usually be expressed as a difference of convex functions. In this note we show that, exploiting further the rich structure of such objective functions, alternative bounds can be derived, yielding a significant improvement in computing times, as reported in our numerical experience.
机译:大三角形小三角形方法已证明是解决连续位置问题的强大全局优化程序。在发表于J. Global Optim的论文中。 (37:305-319,2007),Drezner提出了一种相当通用和有效的方法来构造所需的界限。通过使用连续位置模型中的目标函数通常可以表示为凸函数之差这一事实来获得这样的界限。在本注释中,我们表明,进一步利用此类目标函数的丰富结构,可以得出替代的边界,从而大大缩短了计算时间,这在我们的数值经验中已有报道。

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