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首页> 外文期刊>INFORMS journal on computing >Automation and Combination of Linear-Programming Based Stabilization Techniques in Column Generation
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Automation and Combination of Linear-Programming Based Stabilization Techniques in Column Generation

机译：列生成中基于线性编程的稳定技术的自动化和组合

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摘要

The convergence of a column generation algorithm can be improved in practice by using stabilization techniques. Smoothing and proximal methods based on penalizing the deviation from the incumbent dual solution have become standards of the domain. Interpreting column generation as cutting plane strategies in the dual problem, we analyze the mechanisms on which stabilization relies. In particular, the link is established between smoothing and in-out separation strategies to derive generic convergence properties. For penalty function methods as well as for smoothing, we describe proposals for parameter self-adjusting schemes. Such schemes make initial parameter tuning less of an issue as corrections are made dynamically. Such adjustments also allow us to adapt the parameters to the phase of the algorithm. We provide extensive test reports that validate our self-adjusting parameter scheme and highlight their performances. Our results also show that using smoothing in combination with a penalty function yields a cumulative effect on convergence speed-ups.
机译：通过使用稳定技术，可以在实践中提高列生成算法的收敛性。基于惩罚与现有对偶解的偏差的平滑和近端方法已成为该领域的标准。将列生成解释为对偶问题中的切平面策略，我们分析了稳定所依赖的机制。特别是，在平滑和进出分离策略之间建立了联系，以得出通用的收敛特性。对于惩罚函数方法和平滑方法，我们描述了参数自调整方案的建议。这种方案使初始参数调整成为一个问题，因为动态地进行了校正。这种调整还使我们能够使参数适应算法的阶段。我们提供广泛的测试报告，这些报告可验证我们的自调整参数方案并突出其性能。我们的结果还表明，将平滑与惩罚函数结合使用可对收敛速度产生累积影响。

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