Coverings of convex bodies have emerged as a central component in the design of efficient solutions to approximation problems involving convex bodies. Intuitively, given a convex body $K$ and $\epsilon> 0$, a covering is a collection of convex bodies whose union covers $K$ such that a constant factor expansion of each body lies within an $\epsilon$ expansion of $K$. Coverings have been employed in many applications, such as approximations for diameter, width, and $\epsilon$-kernels of point sets, approximate nearest neighbor searching, polytope approximations, and approximations to the Closest Vector Problem (CVP). It is known how to construct coverings of size $n^{O(n)} / \epsilon^{(n-1)/2}$ for general convex bodies in $\textbf{R}^n$. In special cases, such as when the convex body is the $\ell_p$ unit ball, this bound has been improved to $2^{O(n)} / \epsilon^{(n-1)/2}$. This raises the question of whether such a bound generally holds. In this paper we answer the question in the affirmative. We demonstrate the power and versatility of our coverings by applying them to the problem of approximating a convex body by a polytope, under the Banach-Mazur metric. Given a well-centered convex body $K$ and an approximation parameter $\epsilon> 0$, we show that there exists a polytope $P$ consisting of $2^{O(n)} / \epsilon^{(n-1)/2}$ vertices (facets) such that $K \subset P \subset K(1+\epsilon)$. This bound is optimal in the worst case up to factors of $2^{O(n)}$. As an additional consequence, we obtain the fastest $(1+\epsilon)$-approximate CVP algorithm that works in any norm, with a running time of $2^{O(n)} / \epsilon ^{(n-1)/2}$ up to polynomial factors in the input size, and we obtain the fastest $(1+\epsilon)$-approximation algorithm for integer programming. We also present a framework for constructing coverings of optimal size for any convex body (up to factors of $2^{O(n)}$).
翻译:覆盖 convex 机构在设计用于近似 Convex 机构问题的高效解决方案时, 已经成为一个核心部分。 直径、 宽度和 $\ 平方元( 平方元 ) 的近似( 平方元 ) 。 直径、 宽度和 美元 / 平方元( 平方元 ) 的近似( 平方元 ) 维基元( 平方元 ) 的近似( 近近邻搜索 、 超方元 近似、 近似、 近似和 近端 Victor 问题 ( CVP) 的近似( 平方元 美元 美元 ) 。 已知如何构建大小为 $ *美元 (n-1) / 每平方元( 平方元) 的常因子, 直径( 平方元) 直径( 平方元/ 平方元) 直径( 平方元) 直径( 平方元/ 平方元/ 平方元) 平方根( 平方/ 平方) 平方/ 平方/ 平方根) 显示为 。</s>