Computational examples show that the modified subgradient optimization algorithm for Lagrangean relaxation can reduce the iterative steps obviously, and is proved to be efficient.
算法实例表明,改进后的拉格朗日松弛算法迭代步数显著减少,证明算法是有效的。
This paper focuses on the modeling of the resources location of medical rescue. At first, the traditional Lagrangean relaxation algorithm is improved by using subgradient optimization algorithm.
以医疗急救资源的配置问题为建模核心,运用次梯度最优算法对传统的拉格朗日松弛算法进行了改进。
This paper focuses on the modeling of the resources location of medical rescue. At first, the traditional Lagrangean relaxation algorithm is improved by using subgradient optimization algorithm.
以医疗急救资源的配置问题为建模核心,运用次梯度最优算法对传统的拉格朗日松弛算法进行了改进。
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