Approximating Unrelated Machine Weighted Completion Time Using Iterative Rounding and Computer Assisted Proofs

IF 1 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Shi Li
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引用次数: 0

Abstract

We revisit the unrelated machine scheduling problem with the weighted completion time objective. It is known that independent rounding achieves a 1.5 approximation for the problem, and many prior algorithms improve upon this ratio by leveraging strong negative correlation schemes. On each machine i, these schemes introduce strong negative correlation between events that some pairs of jobs are assigned to i, while maintaining non-positive correlation for all pairs. Our algorithm deviates from this methodology by relaxing the pairwise non-positive correlation requirement. On each machine i, we identify many groups of jobs. For a job j and a group B not containing j, we only enforce non-positive correlation between j and the group as a whole, allowing j to be positively-correlated with individual jobs in B. This relaxation suffices to maintain the 1.5-approximation, while enabling us to obtain a much stronger negative correlation within groups using an iterative rounding procedure: at most one job from each group is scheduled on i. We prove that the algorithm achieves a 1.36-approximation, improving upon the previous best approximation ratio of 1.4 due to Harris. While the improvement may not be substantial, the significance of our contribution lies in the relaxed non-positive correlation condition and the iterative rounding framework. Due to the simplicity of our algorithm, we are able to derive a closed form for the weighted completion time our algorithm achieves with a clean analysis. Unfortunately, we could not provide a good analytical analysis for the quantity; instead, we rely on a computer assisted proof. Nevertheless, the checking algorithm for the analysis is easy to implement, essentially involving evaluation of maximum values of single-variable quadratic functions over given intervals. Therefore, unlike previous results which use intricate analysis to optimize the final approximation ratio, we delegate this task to computer programs.

用迭代舍入和计算机辅助证明逼近不相关机器加权完成时间
我们重新研究了具有加权完成时间目标的不相关机器调度问题。众所周知,独立舍入可以实现问题的1.5近似值,许多先前的算法通过利用强负相关方案来改进这个比率。在每台机器i上,这些方案在一些任务对分配给i的事件之间引入了强的负相关,而对所有任务对保持非正相关。我们的算法通过放宽成对非正相关要求而偏离了这种方法。在每台机器i上,我们确定了许多组作业。对于作业j和不包含j的组B,我们只强制j与整个组之间的非正相关,允许j与B中的单个作业正相关。这种松弛足以维持1.5近似,同时使我们能够使用迭代舍入过程在组内获得更强的负相关:我们证明了该算法达到了1.36的近似值,改进了Harris给出的1.4的最佳近似值。虽然改进可能不是实质性的,但我们的贡献的意义在于放宽的非正相关条件和迭代舍入框架。由于我们算法的简单性,我们能够通过清晰的分析得出我们算法所获得的加权完成时间的封闭形式。遗憾的是,我们无法提供一个很好的数量分析;相反,我们依靠计算机辅助的证明。然而,用于分析的检查算法很容易实现,主要涉及在给定区间内评估单变量二次函数的最大值。因此,与以前使用复杂的分析来优化最终近似比率的结果不同,我们将这项任务委托给计算机程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Algorithmica
Algorithmica 工程技术-计算机:软件工程
CiteScore
2.80
自引率
9.10%
发文量
158
审稿时长
12 months
期刊介绍: Algorithmica is an international journal which publishes theoretical papers on algorithms that address problems arising in practical areas, and experimental papers of general appeal for practical importance or techniques. The development of algorithms is an integral part of computer science. The increasing complexity and scope of computer applications makes the design of efficient algorithms essential. Algorithmica covers algorithms in applied areas such as: VLSI, distributed computing, parallel processing, automated design, robotics, graphics, data base design, software tools, as well as algorithms in fundamental areas such as sorting, searching, data structures, computational geometry, and linear programming. In addition, the journal features two special sections: Application Experience, presenting findings obtained from applications of theoretical results to practical situations, and Problems, offering short papers presenting problems on selected topics of computer science.
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