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LP Randomized Rounding for Maximum Coverage Problem and Minimum Set Cover with Threshold Problem

机译:LP随机取整,用于最大覆盖问题和具有阈值问题的最小集合覆盖

摘要

There are abundance of web accessible life science sources. Traversal of a particular path can answer a navigational query, returning a target object set (TOS). The cardinality of TOS is considered as the benefit of a path and there is some cost function associated with each path. It is common that multiple alternate paths satisfy the query and we are not allowed to pick all these paths to answer the query, since there could be exponential number of paths in a graph. We are interested in selecting a subset of these paths.We present two problems in this context. The first problem is to select a subset of paths of maximum benefit within a cost budget. This is known as {em Budgeted Maximum Coverage Problem} in the literature. The second problem is to select a subset of paths of minimum cost with a threshold benefit guarantee. This is the {em Minimum Set Cover with Threshold Problem}. We develop randomized approximation algorithms based on LP rounding and conduct experiments.
机译:网络上可以访问的生命科学资源很多。遍历特定路径可以回答导航查询,并返回目标对象集(TOS)。 TOS的基数被认为是一条路径的收益,并且每条路径都有一些成本函数。通常有多个备用路径满足查询,并且不允许我们选择所有这些路径来回答查询,因为图中可能存在指数级的路径。我们有兴趣选择这些路径的子集。在这种情况下,我们提出了两个问题。第一个问题是在成本预算内选择收益最大的路径子集。在文献中,这称为{ em预算最大覆盖范围问题}。第二个问题是选择具有阈值收益保证的最小成本路径的子集。这是{ em具有阈值问题的最小集覆盖率}。我们开发了基于LP舍入的随机近似算法并进行了实验。

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