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arXiv 0904.0019cs.LOcs.AI
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布尔多层优化问题的求解

On Solving Boolean Multilevel Optimization Problems

INESC-ID Lisboa(里斯本高等工程学院信息与决策系统研究所);IST(里斯本高等技术学院);University College Dublin(都柏林大学学院)

Josep Argelich, Ines Lynce, Joao Marques-Silva

摘要:

许多组合优化问题都涉及到多个层次相关的优化问题。经常使用的解决方案是将适当大的成本与每个单独的优化问题相关联,使得所得到的聚合优化问题的解决方案解决了原始的一组分层依赖优化问题。本文首先研究软件发行版中的软件包可移植性问题。基于最大可满足性(MaxSAT)和伪布尔(PB)优化的简单解决方案被证明是无效的,并且不太可能扩展到大型问题实例。之后,封装可扩展性问题涉及到多级优化。然后,本文开发了新的算法布尔多级优化(BMO),并强调了大量的潜在应用。实验结果表明,所提出的算法BMO允许解决优化问题,现有的MaxSAT和PB求解器,否则无法解决。

英文摘要:

Many combinatorial optimization problems entail a number of hierarchically dependent optimization problems. An often used solution is to associate a suitably large cost with each individual optimization problem, such that the solution of the resulting aggregated optimization problem solves the original set of hierarchically dependent optimization problems. This paper starts by studying the package upgradeability problem in software distributions. Straightforward solutions based on Maximum Satisfiability (MaxSAT) and pseudo-Boolean (PB) optimization are shown to be ineffective, and unlikely to scale for large problem instances. Afterwards, the package upgradeability problem is related to multilevel optimization. The paper then develops new algorithms for Boolean Multilevel Optimization (BMO) and highlights a large number of potential applications. The experimental results indicate that the proposed algorithms for BMO allow solving optimization problems that existing MaxSAT and PB solvers would otherwise be unable to solve.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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