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arXiv 0909.1062cs.CGcs.DScs.LG
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最小包围凸集的新近似算法

New Approximation Algorithms for Minimum Enclosing Convex Shapes

University of Chicago(芝加哥大学);Purdue University(普渡大学);University of Alberta(阿尔伯塔大学)

Ankan Saha, S. V. N. Vishwanathan, Xinhua Zhang

摘要:

给定$d$维欧氏空间中的$n$点,最小包围球(MEB)问题是寻找包含所有$n$点的半径最小的球。我们给出了一个$O(nd\Qcal/\sqrtε)$近似算法,用于产生一个半径最多为$ε$的封闭球(其中$\Qcal$是点范数的上界)。这改进了使用\emph{coresets}的现有结果,其产生$O(nd/ε)$贪婪算法。寻找最小包围凸多面体(MECP)是一个相关的问题,其中给定一个固定形状的凸多面体,目的是找到包围给定点的多面体的最小放大率。对于这个问题,我们提出了一个$O(mnd\Qcal/ε)$近似算法,其中$m$是多面体的面数。我们的算法借用大量的凸对偶和最近开发的技术在非光滑优化,并与现有的方法依赖于几何参数。特别是,我们专门的过度间隙框架的\citet{Nesterov05a},以获得我们的结果。

英文摘要:

Given $n$ points in a $d$ dimensional Euclidean space, the Minimum Enclosing Ball (MEB) problem is to find the ball with the smallest radius which contains all $n$ points. We give a $O(nd\Qcal/\sqrtε)$ approximation algorithm for producing an enclosing ball whose radius is at most $ε$ away from the optimum (where $\Qcal$ is an upper bound on the norm of the points). This improves existing results using \emph{coresets}, which yield a $O(nd/ε)$ greedy algorithm. Finding the Minimum Enclosing Convex Polytope (MECP) is a related problem wherein a convex polytope of a fixed shape is given and the aim is to find the smallest magnification of the polytope which encloses the given points. For this problem we present a $O(mnd\Qcal/ε)$ approximation algorithm, where $m$ is the number of faces of the polytope. Our algorithms borrow heavily from convex duality and recently developed techniques in non-smooth optimization, and are in contrast with existing methods which rely on geometric arguments. In particular, we specialize the excessive gap framework of \citet{Nesterov05a} to obtain our results.

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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