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arXiv 1003.0722cs.DS
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最优决策树和自适应TSP问题近似算法

Approximation Algorithms for Optimal Decision Trees and Adaptive TSP Problems

Anupam Gupta, Viswanath Nagarajan, R. Ravi

摘要:

我们考虑的问题,构建最佳的决策树:给定一个集合的测试,可以消除一组$m$可能的疾病之间的歧义,每个测试有一个成本,和先验的可能性的患者有任何特定的疾病,什么是一个很好的自适应策略来执行这些测试,以尽量减少预期的成本,以确定疾病?通过给出一个紧的$O(\log m)$ -近似算法,解决了该问题的可逼近性.我们还考虑了一个更实质性的推广,自适应TSP问题。给定一个基本的度量空间,从一个已知的分布中抽取一个城市的随机子集$S$,但是$S$最初对我们来说是未知的--只有当我们访问所讨论的城市时,我们才能得到关于任何城市是否在$S$中的信息。要访问随机子集$S$中的所有城市,同时最小化预期的旅行距离,什么是好的自适应方法?对于这个问题,我们给出了第一个多对数近似,并表明,该算法是最好的可能,除非我们可以改善的近似保证著名的组Steiner树问题。

英文摘要:

We consider the problem of constructing optimal decision trees: given a collection of tests which can disambiguate between a set of $m$ possible diseases, each test having a cost, and the a-priori likelihood of the patient having any particular disease, what is a good adaptive strategy to perform these tests to minimize the expected cost to identify the disease? We settle the approximability of this problem by giving a tight $O(\log m)$-approximation algorithm. We also consider a more substantial generalization, the Adaptive TSP problem. Given an underlying metric space, a random subset $S$ of cities is drawn from a known distribution, but $S$ is initially unknown to us--we get information about whether any city is in $S$ only when we visit the city in question. What is a good adaptive way of visiting all the cities in the random subset $S$ while minimizing the expected distance traveled? For this problem, we give the first poly-logarithmic approximation, and show that this algorithm is best possible unless we can improve the approximation guarantees for the well-known group Steiner tree problem.

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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