121 research outputs found
On largest volume simplices and sub-determinants
We show that the problem of finding the simplex of largest volume in the
convex hull of points in can be approximated with a factor
of in polynomial time. This improves upon the previously best
known approximation guarantee of by Khachiyan. On the other hand,
we show that there exists a constant such that this problem cannot be
approximated with a factor of , unless . % This improves over the
inapproximability that was previously known. Our hardness result holds
even if , in which case there exists a \bar c\,^{d}-approximation
algorithm that relies on recent sampling techniques, where is again a
constant. We show that similar results hold for the problem of finding the
largest absolute value of a subdeterminant of a matrix
Approximating the multi-level bottleneck assignment problem.
We consider the multi-level bottleneck assignment problem (MBA). This problem is described in the recent book 'Assignment Problems' by Burkard et al. (2009) on pages 188-189. One of the applications described there concerns bus driver scheduling.We view the problem as a special case of a bottleneck m-dimensional multi-index assignment problem. We give approximation algorithms and inapproximability results, depending upon the completeness of the underlying graph. Keywords: bottleneck problem; multidimensional assignment; approximation; computational complexity; efficient algorithm.Bottleneck problem; Multidimensional assignment; Approximation; Computational complexity; Efficient algorithm;
Improved Hardness of Approximation for Geometric Bin Packing
The Geometric Bin Packing (GBP) problem is a generalization of Bin Packing
where the input is a set of -dimensional rectangles, and the goal is to pack
them into unit -dimensional cubes efficiently. It is NP-Hard to obtain a
PTAS for the problem, even when . For general , the best known
approximation algorithm has an approximation guarantee exponential in ,
while the best hardness of approximation is still a small constant
inapproximability from the case when . In this paper, we show that the
problem cannot be approximated within factor unless NP=ZPP.
Recently, -dimensional Vector Bin Packing, a closely related problem to
the GBP, was shown to be hard to approximate within when
is a fixed constant, using a notion of Packing Dimension of set families. In
this paper, we introduce a geometric analog of it, the Geometric Packing
Dimension of set families. While we fall short of obtaining similar
inapproximability results for the Geometric Bin Packing problem when is
fixed, we prove a couple of key properties of the Geometric Packing Dimension
that highlight the difference between Geometric Packing Dimension and Packing
Dimension.Comment: 10 page
Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-HĂŒbner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro PezzĂ©, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
Approximating Smallest Containers for Packing Three-dimensional Convex Objects
We investigate the problem of computing a minimal-volume container for the
non-overlapping packing of a given set of three-dimensional convex objects.
Already the simplest versions of the problem are NP-hard so that we cannot
expect to find exact polynomial time algorithms. We give constant ratio
approximation algorithms for packing axis-parallel (rectangular) cuboids under
translation into an axis-parallel (rectangular) cuboid as container, for
cuboids under rigid motions into an axis-parallel cuboid or into an arbitrary
convex container, and for packing convex polyhedra under rigid motions into an
axis-parallel cuboid or arbitrary convex container. This work gives the first
approximability results for the computation of minimal volume containers for
the objects described
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