We introduce and asses several Divide-and-Conquer heuristic strategies, aimed at solving large instances of the 0–1 Minimization Knapsack Problem. The method subdivides a large problem in two smaller ones (or recursive iterations of the same procedure), in order to lower down the global computational complexity of the original problem, at the expense of a moderate loss of quality in the solution. Theoretical mathematical results are presented to assure a successful algorithmic application of the method and to suggest the potential strategies for its implementation. In contrast, due to the lack of theoretical results, the solution’s quality deterioration is measured empirically by means of Monte Carlo simulations for several types and values of the chosen strategies. Finally, introducing parameters of efficiency we suggest the best strategies depending on the data input.

中文翻译：

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0-1最小背包问题的分治策略分析

我们介绍并评估了几种“分而治之”的启发式策略，旨在解决“ 0-1最小化背包问题”的大型实例。该方法将一个大问题细分为两个较小的问题（或同一过程的递归迭代），以降低原始问题的全局计算复杂度，但以解决方案中的质量损失为代价。提出了理论数学结果，以确保该方法的成功算法应用，并提出实施该方法的潜在策略。相反，由于缺乏理论结果，通过蒙特卡洛模拟对所选策略的几种类型和值进行经验测量，得出解决方案的质量下降。最后，