50 research outputs found
A deterministic algorithm for fitting a step function to a weighted point-set
Given a set of n points in the plane, each point having a positive weight,
and an integer k>0, we present an optimal O(n \log n)-time deterministic
algorithm to compute a step function with k steps that minimizes the maximum
weighted vertical distance to the input points. It matches the expected time
bound of the best known randomized algorithm for this problem. Our approach
relies on Cole's improved parametric searching technique.Comment: 5 pages, 2 figure
New results on minimax regret single facility ordered median location problems on networks
We consider the single facility ordered median location problem with uncertainty in the parameters (weights) defining the objective function. We study two cases. In the first case the uncertain weights belong to a region with a finite number of extreme points, and in the second case they must also satisfy some order constraints and belong to some box, (convex case). To deal with the uncertainty we apply the minimax regret approach, providing strongly polynomial time algorithms to solve these problems
The -Center Problem in Tree Networks Revisited
We present two improved algorithms for weighted discrete -center problem
for tree networks with vertices. One of our proposed algorithms runs in
time. For all values of , our algorithm
thus runs as fast as or faster than the most efficient time
algorithm obtained by applying Cole's speed-up technique [cole1987] to the
algorithm due to Megiddo and Tamir [megiddo1983], which has remained
unchallenged for nearly 30 years. Our other algorithm, which is more practical,
runs in time, and when it is
faster than Megiddo and Tamir's time algorithm
[megiddo1983]
matching, interpolation, and approximation ; a survey
In this survey we consider geometric techniques which have been used to
measure the similarity or distance between shapes, as well as to approximate
shapes, or interpolate between shapes. Shape is a modality which plays a key
role in many disciplines, ranging from computer vision to molecular biology.
We focus on algorithmic techniques based on computational geometry that have
been developed for shape matching, simplification, and morphing
Computing the Similarity Between Moving Curves
In this paper we study similarity measures for moving curves which can, for
example, model changing coastlines or retreating glacier termini. Points on a
moving curve have two parameters, namely the position along the curve as well
as time. We therefore focus on similarity measures for surfaces, specifically
the Fr\'echet distance between surfaces. While the Fr\'echet distance between
surfaces is not even known to be computable, we show for variants arising in
the context of moving curves that they are polynomial-time solvable or
NP-complete depending on the restrictions imposed on how the moving curves are
matched. We achieve the polynomial-time solutions by a novel approach for
computing a surface in the so-called free-space diagram based on max-flow
min-cut duality
A generalized model of equality measures in network location problems
In this paper, the concept of the ordered weighted averaging operator is applied to define a model which unifies and generalizes several inequality measures. For a location x, the value of the new objective function is the ordered weighted average of the absolute deviations from the average distance from the facilities to the location x. Several kinds of networks are studied: cyclic, tree and path networks and, for each of them, the properties of the objective function are analyzed in order to identify a finite dominating set for optimal locations. Polynomial-time algorithms are proposed for these problems, and the corresponding complexity is discussed.Future and Emerging Technologies Unit (European Commission)Ministerio de Educación y Cienci