17 research outputs found
An Optimal Algorithm for the Maximum-Density Segment Problem
We address a fundamental problem arising from analysis of biomolecular
sequences. The input consists of two numbers and and a
sequence of number pairs with . Let {\em segment}
of be the consecutive subsequence of between indices and
. The {\em density} of is
. The {\em maximum-density
segment problem} is to find a maximum-density segment over all segments
with . The best
previously known algorithm for the problem, due to Goldwasser, Kao, and Lu,
runs in time. In the present paper, we solve
the problem in O(n) time. Our approach bypasses the complicated {\em right-skew
decomposition}, introduced by Lin, Jiang, and Chao. As a result, our algorithm
has the capability to process the input sequence in an online manner, which is
an important feature for dealing with genome-scale sequences. Moreover, for a
type of input sequences representable in space, we show how to
exploit the sparsity of and solve the maximum-density segment problem for
in time.Comment: 15 pages, 12 figures, an early version of this paper was presented at
11th Annual European Symposium on Algorithms (ESA 2003), Budapest, Hungary,
September 15-20, 200
A Center Transversal Theorem for Hyperplanes and Applications to Graph Drawing
Motivated by an open problem from graph drawing, we study several
partitioning problems for line and hyperplane arrangements. We prove a
ham-sandwich cut theorem: given two sets of n lines in R^2, there is a line l
such that in both line sets, for both halfplanes delimited by l, there are
n^{1/2} lines which pairwise intersect in that halfplane, and this bound is
tight; a centerpoint theorem: for any set of n lines there is a point such that
for any halfplane containing that point there are (n/3)^{1/2} of the lines
which pairwise intersect in that halfplane. We generalize those results in
higher dimension and obtain a center transversal theorem, a same-type lemma,
and a positive portion Erdos-Szekeres theorem for hyperplane arrangements. This
is done by formulating a generalization of the center transversal theorem which
applies to set functions that are much more general than measures. Back to
Graph Drawing (and in the plane), we completely solve the open problem that
motivated our search: there is no set of n labelled lines that are universal
for all n-vertex labelled planar graphs. As a side note, we prove that every
set of n (unlabelled) lines is universal for all n-vertex (unlabelled) planar
graphs
Algorithms for the Problems of Length-Constrained Heaviest Segments
We present algorithms for length-constrained maximum sum segment and maximum
density segment problems, in particular, and the problem of finding
length-constrained heaviest segments, in general, for a sequence of real
numbers. Given a sequence of n real numbers and two real parameters L and U (L
<= U), the maximum sum segment problem is to find a consecutive subsequence,
called a segment, of length at least L and at most U such that the sum of the
numbers in the subsequence is maximum. The maximum density segment problem is
to find a segment of length at least L and at most U such that the density of
the numbers in the subsequence is the maximum. For the first problem with
non-uniform width there is an algorithm with time and space complexities in
O(n). We present an algorithm with time complexity in O(n) and space complexity
in O(U). For the second problem with non-uniform width there is a combinatorial
solution with time complexity in O(n) and space complexity in O(U). We present
a simple geometric algorithm with the same time and space complexities.
We extend our algorithms to respectively solve the length-constrained k
maximum sum segments problem in O(n+k) time and O(max{U, k}) space, and the
length-constrained maximum density segments problem in O(n min{k, U-L})
time and O(U+k) space. We present extensions of our algorithms to find all the
length-constrained segments having user specified sum and density in O(n+m) and
O(nlog (U-L)+m) times respectively, where m is the number of output.
Previously, there was no known algorithm with non-trivial result for these
problems. We indicate the extensions of our algorithms to higher dimensions.
All the algorithms can be extended in a straight forward way to solve the
problems with non-uniform width and non-uniform weight.Comment: 21 pages, 12 figure
Regression Depth and Center Points
We show that, for any set of n points in d dimensions, there exists a
hyperplane with regression depth at least ceiling(n/(d+1)). as had been
conjectured by Rousseeuw and Hubert. Dually, for any arrangement of n
hyperplanes in d dimensions there exists a point that cannot escape to infinity
without crossing at least ceiling(n/(d+1)) hyperplanes. We also apply our
approach to related questions on the existence of partitions of the data into
subsets such that a common plane has nonzero regression depth in each subset,
and to the computational complexity of regression depth problems.Comment: 14 pages, 3 figure
Model-based probing strategies for convex polygons
AbstractWe prove that n+4 finger probes are sufficient to determine the shape of a convex n-gon from a finite collection of models, improving the previous result of 2n+1. Further, we show that n−1 are necessary, proving this is optimal to within an additive constant. For line probes, we show that 2n+4 probes are sufficient and 2n−3 necessary. The difference between these results is particularly interesting in light of the duality relationship between finger and line probes
Partial-Matching and Hausdorff RMS Distance Under Translation: Combinatorics and Algorithms
We consider the RMS distance (sum of squared distances between pairs of
points) under translation between two point sets in the plane, in two different
setups. In the partial-matching setup, each point in the smaller set is matched
to a distinct point in the bigger set. Although the problem is not known to be
polynomial, we establish several structural properties of the underlying
subdivision of the plane and derive improved bounds on its complexity. These
results lead to the best known algorithm for finding a translation for which
the partial-matching RMS distance between the point sets is minimized. In
addition, we show how to compute a local minimum of the partial-matching RMS
distance under translation, in polynomial time. In the Hausdorff setup, each
point is paired to its nearest neighbor in the other set. We develop algorithms
for finding a local minimum of the Hausdorff RMS distance in nearly linear time
on the line, and in nearly quadratic time in the plane. These improve
substantially the worst-case behavior of the popular ICP heuristics for solving
this problem.Comment: 31 pages, 6 figure
Minimum Partial-Matching and Hausdorff RMS-Distance under Translation: Combinatorics and Algorithms
We consider the RMS-distance (sum of squared distances between pairs of points) under translation between two point sets in the plane. In the Hausdorff setup, each point is paired to its nearest neighbor in the other set. We develop algorithms for finding a local minimum in near-linear time on the line, and in nearly quadratic time in the plane. These improve substantially the worst-case behavior of the popular ICP heuristics for solving this problem. In the partial-matching setup, each point in the smaller set is matched to a distinct point in the bigger set. Although the problem is not known to be polynomial, we establish several structural properties of the underlying subdivision of the plane and derive improved bounds on its complexity. In addition, we show how to compute a local minimum of the partial-matching RMS-distance under translation, in polynomial time