半代数离线范围搜索和平面上的双斜分区

Pankaj K. Agarwal, Esther Ezra, Micha Sharir
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Let $\\Phi: \\Sigma \\times P \\rightarrow \\{0,1\\}$ be the Boolean predicate (of\nconstant complexity) such that $\\Phi(\\sigma,p) = 1$ if $p\\in\\sigma$ and $0$\notherwise, and let $\\Sigma\\mathop{\\Phi} P = \\{ (\\sigma,p) \\in \\Sigma\\times P\n\\mid \\Phi(\\sigma,p)=1\\}$. Our algorithm actually computes a partition\n${\\mathcal B}_\\Phi$ of $\\Sigma\\mathop{\\Phi} P$ into bipartite cliques\n(bicliques) of size (i.e., sum of the sizes of the vertex sets of its\nbicliques) $O^*\\bigl( m^{\\frac{2s}{5s-4}}n^{\\frac{5s-6}{5s-4}} + m^{2/3}n^{2/3}\n+ m + n \\bigr)$. It is straightforward to compute $w(P\\cap\\sigma)$ for all\n$\\sigma\\in \\Sigma$ from ${\\mathcal B}_\\Phi$. Similarly, if $\\eta: \\Sigma\n\\rightarrow S$ is a weight function on the regions of $\\Sigma$,\n$\\sum_{\\sigma\\in \\Sigma: p \\in \\sigma} \\eta(\\sigma)$, for every point $p\\in P$,\ncan be computed from ${\\mathcal B}_\\Phi$ in a straightforward manner. 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引用次数: 0

摘要

让 $P$ 是 ${mathbb R}^2$ 中 $m$ 点的集合,让 $\Sigma$ 是 ${mathbb R}^2$ 中具有恒定复杂性的 $n$ 半代数集合,让 $(S,+)$ 是一个半群,让 $w:P \rightarrow S$ 是 $P$ 的点上的权重函数。我们描述了一种随机算法,可以在总体预期时间 $O^*\bigl(m^{\frac{2s}{5s-4}}n^{\frac{5s-6}{5s-4}} + m^{2/3}n^{2/3} + m + n \bigr)$ 内计算 $w(P/cap/sigma)$、其中,$s>0$ 是一个常数,它限定了$\Sigma$ 区域的最大复杂度,$O^*(\cdot)$ 符号隐藏了次多项式因子。对于$s\ge 3$,令人惊讶的是,这个界限小于以在线方式回答 $m$ 此类查询的最著名界限。后者需要花费$O^*(m^{\frac{s}{2s-1}}n^{\frac{2s-2}{2s-1}}+m+n)$时间。让 $Phi:P 是布尔谓词(复杂度恒定),当 $p\in\sigma$ 时,$\Phi(\sigma,p) = 1$,否则为 $0$;让 $\Sigma\mathop\{Phi} P =\{ (\sigma,p) = 1$,否则为 $0$;让 $\Sigma\mathop\{Phi} P =\{ (\sigma,p) = 1$,否则为 $0$。P = { (\sigma,p) \in \Sigma\times P\mid \Phi(\sigma,p)=1/}$。我们的算法实际上是计算 $\Sigma\mathcal B}_\Phi$ 的一个分区。P$ 分成大小(即其双簇顶点集大小之和)为 $O^*\bigl( m^{frac{2s}{5s-4}}n^{\frac{5s-6}{5s-4}} + m^{2/3}n^{2/3}+ m + n \bigr)$ 的双簇群(双簇)。从 ${\mathcal B}_\Phi$ 计算所有 $\sigma\in \Sigma$ 的 $w(P\cap\sigma)$ 非常简单。同样,如果 $\eta:\S$ 是 $\Sigma$ 区域上的权重函数,那么对于 P$ 中的每个点 $p\ 可以直接从 ${mathcal B}_\Phi$ 计算出 $sum_{\sigma\in \Sigma: p \in \sigma} \eta(\sigma)$ 。Chan 等人最近的一项研究解决了在线版本的对偶点封闭问题,其性能与我们的离线解决方案相同。我们还提到了计算 ${\mathcal B}_\Phi$ 的一些其他应用。
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Semi-Algebraic Off-line Range Searching and Biclique Partitions in the Plane
Let $P$ be a set of $m$ points in ${\mathbb R}^2$, let $\Sigma$ be a set of $n$ semi-algebraic sets of constant complexity in ${\mathbb R}^2$, let $(S,+)$ be a semigroup, and let $w: P \rightarrow S$ be a weight function on the points of $P$. We describe a randomized algorithm for computing $w(P\cap\sigma)$ for every $\sigma\in\Sigma$ in overall expected time $O^*\bigl( m^{\frac{2s}{5s-4}}n^{\frac{5s-6}{5s-4}} + m^{2/3}n^{2/3} + m + n \bigr)$, where $s>0$ is a constant that bounds the maximum complexity of the regions of $\Sigma$, and where the $O^*(\cdot)$ notation hides subpolynomial factors. For $s\ge 3$, surprisingly, this bound is smaller than the best-known bound for answering $m$ such queries in an on-line manner. The latter takes $O^*(m^{\frac{s}{2s-1}}n^{\frac{2s-2}{2s-1}}+m+n)$ time. Let $\Phi: \Sigma \times P \rightarrow \{0,1\}$ be the Boolean predicate (of constant complexity) such that $\Phi(\sigma,p) = 1$ if $p\in\sigma$ and $0$ otherwise, and let $\Sigma\mathop{\Phi} P = \{ (\sigma,p) \in \Sigma\times P \mid \Phi(\sigma,p)=1\}$. Our algorithm actually computes a partition ${\mathcal B}_\Phi$ of $\Sigma\mathop{\Phi} P$ into bipartite cliques (bicliques) of size (i.e., sum of the sizes of the vertex sets of its bicliques) $O^*\bigl( m^{\frac{2s}{5s-4}}n^{\frac{5s-6}{5s-4}} + m^{2/3}n^{2/3} + m + n \bigr)$. It is straightforward to compute $w(P\cap\sigma)$ for all $\sigma\in \Sigma$ from ${\mathcal B}_\Phi$. Similarly, if $\eta: \Sigma \rightarrow S$ is a weight function on the regions of $\Sigma$, $\sum_{\sigma\in \Sigma: p \in \sigma} \eta(\sigma)$, for every point $p\in P$, can be computed from ${\mathcal B}_\Phi$ in a straightforward manner. A recent work of Chan et al. solves the online version of this dual point enclosure problem within the same performance bound as our off-line solution. We also mention a few other applications of computing ${\mathcal B}_\Phi$.
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