Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOnline Packing to Minimize Area or Perimeter
363
0
0.0
(
0
)
Added by
Mikkel Abrahamsen
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
We consider online packing problems where we get a stream of axis-parallel rectangles. The rectangles have to be placed in the plane without overlapping, and each rectangle must be placed without knowing the subsequent rectangles. The goal is to minimize the perimeter or the area of the axis-parallel bounding box of the rectangles. We either allow rotations by 90 degrees or translations only. For the perimeter version we give algorithms with an absolute competitive ratio slightly less than 4 when only translations are allowed and when rotations are also allowed. We then turn our attention to minimizing the area and show that the asymptotic competitive ratio of any algorithm is at least $Omega(sqrt{n})$, where $n$ is the number of rectangles in the stream, and this holds with and without rotations. We then present algorithms that match this bound in both cases. We also show that the competitive ratio cannot be bounded as a function of OPT. We then consider two special cases. The first is when all the given rectangles have aspect ratios bounded by some constant. The particular variant where all the rectangles are squares and we want to minimize the area of the bounding square has been studied before and an algorithm with a competitive ratio of 8 has been given [Fekete and Hoffmann, Algorithmica, 2017]. We improve the analysis of the algorithm and show that the ratio is at most 6, which is tight. The second special case is when all edges have length at least 1. Here, the $Omega(sqrt n)$ lower bound still holds, and we turn our attention to lower bounds depending on OPT. We show that any algorithm has an asymptotic competitive ratio of at least $Omega(sqrt{OPT})$ for the translational case and $Omega(sqrt[4]{OPT})$ when rotations are allowed. For bo
rate research
Read More
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 exact and approximation methods for solving a geometric relaxation of the Traveling Salesman Problem (TSP) that occurs in curve reconstruction: for a given set of vertices in the plane, the problem Minimum Perimeter Polygon (MPP) asks for a (not necessarily simply connected) polygon with shortest possible boundary length. Even though the closely related problem of finding a minimum cycle cover is polynomially solvable by matching techniques, we prove how the topological structure of a polygon leads to NP-hardness of the MPP. On the positive side, we show how to achieve a constant-factor approximation. When trying to solve MPP instances to provable optimality by means of integer programming, an additional difficulty compared to the TSP is the fact that only a subset of subtour constraints is valid, depending not on combinatorics, but on geometry. We overcome this difficulty by establishing and exploiting additional geometric properties. This allows us to reliably solve a wide range of benchmark instances with up to 600 vertices within reasonable time on a standard machine. We also show that using a natural geometry-based sparsification yields results that are on average within 0.5% of the optimum.
We consider the online problem of packing circles into a square container. A sequence of circles has to be packed one at a time, without knowledge of the following incoming circles and without moving previously packed circles. We present an algorithm that packs any online sequence of circles with a combined area not larger than 0.350389 0.350389 of the squares area, improving the previous best value of {pi}/10 approx 0.31416; even in an offline setting, there is an upper bound of {pi}/(3 + 2 sqrt{2}) approx 0.5390. If only circles with radii of at least 0.026622 are considered, our algorithm achieves the higher value 0.375898. As a byproduct, we give an online algorithm for packing circles into a 1times b rectangle with b geq 1. This algorithm is worst case-optimal for b geq 2.36.
We provide an O(log log OPT)-approximation algorithm for the problem of guarding a simple polygon with guards on the perimeter. We first design a polynomial-time algorithm for building epsilon-nets of size O(1/epsilon log log 1/epsilon) for the instances of Hitting Set associated with our guarding problem. We then apply the technique of Bronnimann and Goodrich to build an approximation algorithm from this epsilon-net finder. Along with a simple polygon P, our algorithm takes as input a finite set of potential guard locations that must include the polygons vertices. If a finite set of potential guard locations is not specified, e.g. when guards may be placed anywhere on the perimeter, we use a known discretization technique at the cost of making the algorithms running time potentially linear in the ratio between the longest and shortest distances between vertices. Our algorithm is the first to improve upon O(log OPT)-approximation algorithms that use generic net finders for set systems of finite VC-dimension.
We study several problems on geometric packing and covering with movement. Given a family $mathcal{I}$ of $n$ intervals of $kappa$ distinct lengths, and another interval $B$, can we pack the intervals in $mathcal{I}$ inside $B$ (respectively, cover $B$ by the intervals in $mathcal{I}$) by moving $tau$ intervals and keeping the other $sigma = n - tau$ intervals unmoved? We show that both packing and covering are W[1]-hard with any one of $kappa$, $tau$, and $sigma$ as single parameter, but are FPT with combined parameters $kappa$ and $tau$. We also obtain improved polynomial-time algorithms for packing and covering, including an $O(nlog^2 n)$ time algorithm for covering, when all intervals in $mathcal{I}$ have the same length.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
