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							@@ -14,6 +14,8 @@
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For errata and more stuff, see \url{https://sarielhp.org/book/}
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					For errata and more stuff, see \url{https://sarielhp.org/book/}
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					Note that unless specifically stated, we always consider the RAM model.
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\section{Grid}
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					\section{Grid}
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\begin{exercise}\label{ex1.1}
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					\begin{exercise}\label{ex1.1}
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Let $P$ be a max cardinality point set contained in the $d$-dimensional unit hypercube such that the smallest distance of point pairs in $P$ is 1. Prove that 
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					Let $P$ be a max cardinality point set contained in the $d$-dimensional unit hypercube such that the smallest distance of point pairs in $P$ is 1. Prove that 
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@@ -37,8 +39,46 @@ The last line is greater than $(\sqrt{d}/5)^d$ for large enough $d$.
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\end{proof}
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					\end{proof}
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\begin{exercise}
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					\begin{exercise}
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Compute clustering radius
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					Compute clustering radius.
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					Let $C$ and $P$ be two given set of points such that $k=|C|$ and $n=|P|$. Define the covering radius of $P$ by $C$ as $r=\max_{p\in P} \min_{c\in C} \norm{p-c}$.
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					\begin{enumerate}
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					\item find an $O(n+k\log n)$ expected time alg that outputs $\alpha$ such that $\alpha \leq r \leq 10\alpha$.
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					\item for prescribed $\varepsilon>0$, find an $O(n+k\varepsilon^{-2}\log n)$ expected time alg that outputs $\alpha$ s.t. $\alpha<r<(1+\epsilon)\alpha$.
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					\end{enumerate}
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\end{exercise}
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					\end{exercise}
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					\section*{Not in the book}
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					\begin{problem}[$d$-dimensional rectangle stabbing \cite{gaur_constant_2002}]
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					Given a set $R$ of $n$ axis-parallel rectangles and a set $L$ of axis-parallel real lines, find the minimum subset of $L$ such that every rectangle is stabbed by at least one line in the subset.
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					\end{problem}
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					This problem is NP-hard even for the 2D case. There is a LP rounding method which gives a $d$-approximation. Let $K_i$ be the subset of $d$th-axis parallel lines in $L$. For a rectangle $r\in R$, denote by $K_i^r$ the set of lines in $K_i$ that stab $r$. Consider the following LP.
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					\begin{equation*}
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					\begin{aligned}
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					\min& &  \sum_{\ell\in L} x_\ell&  &  &   \\
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					s.t.& &  \sum_{i\in [d]} \sum_{\ell \in K_i^r} x_\ell&\geq 1 &    &\forall r\in R\\
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					    &   &   x_\ell&\geq 0    &  &\forall \ell\in L
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					\end{aligned}
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					\end{equation*}
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					Let $\set{x^*_\ell: \ell\in L}$ be the optimal solution to the above LP.
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					For each $r$, there must be some $i\in [d]$ such that $\sum_{\ell \in K_i^r}x^*_\ell \geq 1/d$. Denote such a set for rectangle $r$ by $K_*^r$.
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					Suppose that we find a subset $L^{int}\subset L$ and define a integral solution $\set{y_\ell=1}_{\ell\in L^{int}}\cup \set{y_\ell=0}_{\ell\notin L^{int}}$ such that $\sum_{\ell\in K_*^r}\geq 1$ for each rectangle $r$. In other words, we restrict the solution to be a subset of lines that stabs every rectangle $r$ with lines in $K_*^r$.
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					One nice property of this restriction is that now the problem becomes independent for each dimension. We assign to each rectangle $r$ a dimension $i$ such that  $\sum_{\ell \in K_i^r}x^*_\ell \geq 1/d$. Let $R_i\subset R$ be the set of rectangles assigned dimension $i$. We want to solve the following IP for dimension $i\in[d]$.
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					\begin{equation*}
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					\begin{aligned}
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					\min& &  \sum_{\ell\in K_i} x_\ell&  &  &   \\
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					s.t.& &  \sum_{\ell \in K_i^r} x_\ell&\geq 1 &    &\forall r\in R_i\\
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					    &   &   x_\ell&\in \set{0,1}    &  &\forall \ell\in K_i
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					\end{aligned}
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					\end{equation*}
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					Another nice property is that the constraint matrix is TUM and therefore the its LP relaxation is integral. wait... the definition for d-dimensional rectangle stabbing is different. they do not use lines, they use hyperplanes... I cannot show that the constraint matrix is TUM under my settings.
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					\bibliographystyle{alpha}
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					\bibliography{ref}
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\end{document}
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					\end{document}
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					@article{gaur_constant_2002,
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						title = {Constant {Ratio} {Approximation} {Algorithms} for the {Rectangle} {Stabbing} {Problem} and the {Rectilinear} {Partitioning} {Problem}},
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						volume = {43},
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						issn = {0196-6774},
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						url = {https://www.sciencedirect.com/science/article/pii/S0196677402912216},
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						doi = {10.1006/jagm.2002.1221},
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						number = {1},
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						urldate = {2025-08-16},
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						journal = {Journal of Algorithms},
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						author = {Gaur, Daya Ram and Ibaraki, Toshihide and Krishnamurti, Ramesh},
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						month = apr,
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						year = {2002},
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						keywords = {approximation algorithms, combinatorial optimization, rectangle stabbing, rectilinear partitioning},
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						pages = {138--152},
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					}
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