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Register a new userOn Packing Almost Half of a Square with Anchored Rectangles: A Constructive Approach
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Added by
Sandip Banerjee
Publication date
2013
fields
Informatics Engineering
and research's language is
English
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In this paper, we consider the following geometric puzzle whose origin was traced to Allan Freedman cite{croft91,tutte69} in the 1960s by Dumitrescu and T{o}th cite{adriancasaba2011}. The puzzle has been popularized of late by Peter Winkler cite{Winkler2007}. Let $P_{n}$ be a set of $n$ points, including the origin, in the unit square $U = [0,1]^2$. The problem is to construct $n$ axis-parallel and mutually disjoint rectangles inside $U$ such that the bottom-left corner of each rectangle coincides with a point in $P_{n}$ and the total area covered by the rectangles is maximized. We would term the above rectangles as emph{anchored rectangles}. The longstanding conjecture has been that at least half of $U$ can be covered when anchored rectangles are properly placed. Dumitrescu and T{o}th cite{Dumitrescu2012} have shown a construction method that can cover at least $0.09121$, i.e., roughly $9%$ of the area.
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Let $P_{n}$ be a set of $n$ points, including the origin, in the unit square $U = [0,1]^2$. We consider the problem of constructing $n$ axis-parallel and mutually disjoint rectangles inside $U$ such that the bottom-left corner of each rectangle coincides with a point in $P_{n}$ and the total area covered by the rectangles is maximized cite{ibmpuzzle}, cite{Winkler2007}, cite{Winkler2010a}, cite{Winkler2010b}. The longstanding conjecture has been that at least half of $U$ can be covered when such rectangles are properly placed. In this paper, we give an existential proof of the conjecture.
In the present popular science paper we determine when a square can be dissected into rectangles similar to a given rectangle. The approach to the question is based on a physical interpretation using electrical networks. Only secondary school background is assumed in the paper.
We provide a tight result for a fundamental problem arising from packing squares into a circular container: The critical density of packing squares into a disk is $delta=frac{8}{5pi}approx 0.509$. This implies that any set of (not necessarily equal) squares of total area $A leq frac{8}{5}$ can always be packed into a disk with radius 1; in contrast, for any $varepsilon>0$ there are sets of squares of total area $frac{8}{5}+varepsilon$ that cannot be packed, even if squares may be rotated. This settles the last (and arguably, most elusive) case of packing circular or square objects into a circular or square container: The critical densities for squares in a square $left(frac{1}{2}right)$, circles in a square $left(frac{pi}{(3+2sqrt{2})}approx 0.539right)$ and circles in a circle $left(frac{1}{2}right)$ have already been established, making use of recursive subdivisions of a square container into pieces bounded by straight lines, or the ability to use recursive arguments based on similarity of objects and container; neither of these approaches can be applied when packing squares into a circular container. Our proof uses a careful manual analysis, complemented by a computer-assisted part that is based on interval arithmetic. Beyond the basic mathematical importance, our result is also useful as a blackbox lemma for the analysis of recursive packing algorithms. At the same time, our approach showcases the power of a general framework for computer-assisted proofs, based on interval arithmetic.
The Split Packing algorithm cite{splitpacking_ws, splitpackingsoda, splitpacking} is an offline algorithm that packs a set of circles into triangles and squares up to critical density. In this paper, we develop an online alternative to Split Packing to handle an online sequence of insertions and deletions, where the algorithm is allowed to reallocate circles into new positions at a cost proportional to their areas. The algorithm can be used to pack circles into squares and right angled triangles. If only insertions are considered, our algorithm is also able to pack to critical density, with an amortised reallocation cost of $O(clog frac{1}{c})$ for squares, and $O(c(1+s^2)log_{1+s^2}frac{1}{c})$ for right angled triangles, where $s$ is the ratio of the lengths of the second shortest side to the shortest side of the triangle, when inserting a circle of area $c$. When insertions and deletions are considered, we achieve a packing density of $(1-epsilon)$ of the critical density, where $epsilon>0$ can be made arbitrarily small, with an amortised reallocation cost of $O(c(1+s^2)log_{1+s^2}frac{1}{c} + cfrac{1}{epsilon})$.
We provide the solution for a fundamental problem of geometric optimization by giving a complete characterization of worst-case optimal disk coverings of rectangles: For any $lambdageq 1$, the critical covering area $A^*(lambda)$ is the minimum value for which any set of disks with total area at least $A^*(lambda)$ can cover a rectangle of dimensions $lambdatimes 1$. We show that there is a threshold value $lambda_2 = sqrt{sqrt{7}/2 - 1/4} approx 1.035797ldots$, such that for $lambda<lambda_2$ the critical covering area $A^*(lambda)$ is $A^*(lambda)=3pileft(frac{lambda^2}{16} +frac{5}{32} + frac{9}{256lambda^2}right)$, and for $lambdageq lambda_2$, the critical area is $A^*(lambda)=pi(lambda^2+2)/4$; these values are tight. For the special case $lambda=1$, i.e., for covering a unit square, the critical covering area is $frac{195pi}{256}approx 2.39301ldots$. The proof uses a careful combination of manual and automatic analysis, demonstrating the power of the employed interval arithmetic technique.
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