A (5/3 + ε)-approximation for unsplittable flow on a path: placing small tasks into boxes

F. Grandoni, Tobias Mömke, Andreas Wiese, Hang Zhou
{"title":"A (5/3 + ε)-approximation for unsplittable flow on a path: placing small tasks into boxes","authors":"F. Grandoni, Tobias Mömke, Andreas Wiese, Hang Zhou","doi":"10.1145/3188745.3188894","DOIUrl":null,"url":null,"abstract":"In the unsplittable flow on a path problem (UFP) we are given a path with edge capacities and a collection of tasks. Each task is characterized by a subpath, a profit, and a demand. Our goal is to compute a maximum profit subset of tasks such that, for each edge e, the total demand of selected tasks that use e does not exceed the capacity of e. The current best polynomial-time approximation factor for this problem is 2+є for any constant є>0 [Anagostopoulos et al.-SODA 2014]. This is the best known factor even in the case of uniform edge capacities [Călinescu et al.-IPCO 2002, TALG 2011]. These results, likewise most prior work, are based on a partition of tasks into large and small depending on their ratio of demand to capacity over their respective edges: these algorithms invoke (1+є)-approximations for large and small tasks separately. The known techniques do not seem to be able to combine a big fraction of large and small tasks together (apart from some special cases and quasi-polynomial-time algorithms). The main contribution of this paper is to overcome this critical barrier. Namely, we present a polynomial-time algorithm that obtains roughly all profit from the optimal large tasks plus one third of the profit from the optimal small tasks. In combination with known results, this implies a polynomial-time (5/3+є)-approximation algorithm for UFP. Our algorithm is based on two main ingredients. First, we prove that there exist certain sub-optimal solutions where, roughly speaking, small tasks are packed into boxes. To prove that such solutions can yield high profit we introduce a horizontal slicing lemma which yields a novel geometric interpretation of certain solutions. The resulting boxed structure has polynomial complexity, hence cannot be guessed directly. Therefore, our second contribution is a dynamic program that guesses this structure (plus a packing of large and small tasks) on the fly, while losing at most one third of the profit of the remaining small tasks.","PeriodicalId":20593,"journal":{"name":"Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing","volume":"36 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3188745.3188894","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23

Abstract

In the unsplittable flow on a path problem (UFP) we are given a path with edge capacities and a collection of tasks. Each task is characterized by a subpath, a profit, and a demand. Our goal is to compute a maximum profit subset of tasks such that, for each edge e, the total demand of selected tasks that use e does not exceed the capacity of e. The current best polynomial-time approximation factor for this problem is 2+є for any constant є>0 [Anagostopoulos et al.-SODA 2014]. This is the best known factor even in the case of uniform edge capacities [Călinescu et al.-IPCO 2002, TALG 2011]. These results, likewise most prior work, are based on a partition of tasks into large and small depending on their ratio of demand to capacity over their respective edges: these algorithms invoke (1+є)-approximations for large and small tasks separately. The known techniques do not seem to be able to combine a big fraction of large and small tasks together (apart from some special cases and quasi-polynomial-time algorithms). The main contribution of this paper is to overcome this critical barrier. Namely, we present a polynomial-time algorithm that obtains roughly all profit from the optimal large tasks plus one third of the profit from the optimal small tasks. In combination with known results, this implies a polynomial-time (5/3+є)-approximation algorithm for UFP. Our algorithm is based on two main ingredients. First, we prove that there exist certain sub-optimal solutions where, roughly speaking, small tasks are packed into boxes. To prove that such solutions can yield high profit we introduce a horizontal slicing lemma which yields a novel geometric interpretation of certain solutions. The resulting boxed structure has polynomial complexity, hence cannot be guessed directly. Therefore, our second contribution is a dynamic program that guesses this structure (plus a packing of large and small tasks) on the fly, while losing at most one third of the profit of the remaining small tasks.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
路径上不可分割流的(5/3 + ε)近似:将小任务放入盒子中
在路径问题上的不可分割流(UFP)中,我们给出了一条具有边缘容量的路径和一组任务。每个任务都有一个子路径、一个利润和一个需求。我们的目标是计算任务的最大利润子集,这样,对于每个边e,使用e的选定任务的总需求不超过e的容量。对于任何常数_ >0,该问题的当前最佳多项式时间近似因子为2+ _对于任何常数_ >0 [Anagostopoulos et al.-SODA 2014]。即使在统一边缘容量的情况下,这也是最著名的因素[ccillinescu等人- ipco 2002, TALG 2011]。这些结果,类似于大多数先前的工作,是基于将任务划分为大任务和小任务,这取决于它们在各自边缘上的需求与容量的比例:这些算法分别对大任务和小任务调用(1+ _)-近似。已知的技术似乎无法将大任务和小任务的大部分组合在一起(除了一些特殊情况和准多项式时间算法)。本文的主要贡献就是克服了这一关键障碍。也就是说,我们提出了一个多项式时间算法,该算法大致从最优大任务中获得所有利润加上最优小任务的三分之一利润。结合已知结果,这意味着UFP的多项式时间(5/3+ n)近似算法。我们的算法基于两个主要成分。首先,我们证明存在某些次优解,粗略地说,小任务被打包到盒子里。为了证明这样的解可以产生高的利润,我们引入了一个水平切片引理,它产生了对某些解的一种新的几何解释。所得到的盒子结构具有多项式复杂度,因此不能直接猜测。因此,我们的第二个贡献是一个动态程序,它在运行中猜测这个结构(加上大小任务的打包),而最多损失剩余小任务的三分之一的利润。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Data-dependent hashing via nonlinear spectral gaps Interactive compression to external information The query complexity of graph isomorphism: bypassing distribution testing lower bounds Collusion resistant traitor tracing from learning with errors Explicit binary tree codes with polylogarithmic size alphabet
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1