550 research outputs found
A Look at the Generalized Heron Problem through the Lens of Majorization-Minimization
In a recent issue of this journal, Mordukhovich et al.\ pose and solve an
interesting non-differentiable generalization of the Heron problem in the
framework of modern convex analysis. In the generalized Heron problem one is
given closed convex sets in \Real^d equipped with its Euclidean norm
and asked to find the point in the last set such that the sum of the distances
to the first sets is minimal. In later work the authors generalize the
Heron problem even further, relax its convexity assumptions, study its
theoretical properties, and pursue subgradient algorithms for solving the
convex case. Here, we revisit the original problem solely from the numerical
perspective. By exploiting the majorization-minimization (MM) principle of
computational statistics and rudimentary techniques from differential calculus,
we are able to construct a very fast algorithm for solving the Euclidean
version of the generalized Heron problem.Comment: 21 pages, 3 figure
Dense point sets have sparse Delaunay triangulations
The spread of a finite set of points is the ratio between the longest and
shortest pairwise distances. We prove that the Delaunay triangulation of any
set of n points in R^3 with spread D has complexity O(D^3). This bound is tight
in the worst case for all D = O(sqrt{n}). In particular, the Delaunay
triangulation of any dense point set has linear complexity. We also generalize
this upper bound to regular triangulations of k-ply systems of balls, unions of
several dense point sets, and uniform samples of smooth surfaces. On the other
hand, for any n and D=O(n), we construct a regular triangulation of complexity
Omega(nD) whose n vertices have spread D.Comment: 31 pages, 11 figures. Full version of SODA 2002 paper. Also available
at http://www.cs.uiuc.edu/~jeffe/pubs/screw.htm
Analysis of parametric biological models with non-linear dynamics
In this paper we present recent results on parametric analysis of biological
models. The underlying method is based on the algorithms for computing
trajectory sets of hybrid systems with polynomial dynamics. The method is then
applied to two case studies of biological systems: one is a cardiac cell model
for studying the conditions for cardiac abnormalities, and the second is a
model of insect nest-site choice.Comment: In Proceedings HSB 2012, arXiv:1208.315
Bregman Voronoi Diagrams: Properties, Algorithms and Applications
The Voronoi diagram of a finite set of objects is a fundamental geometric
structure that subdivides the embedding space into regions, each region
consisting of the points that are closer to a given object than to the others.
We may define many variants of Voronoi diagrams depending on the class of
objects, the distance functions and the embedding space. In this paper, we
investigate a framework for defining and building Voronoi diagrams for a broad
class of distance functions called Bregman divergences. Bregman divergences
include not only the traditional (squared) Euclidean distance but also various
divergence measures based on entropic functions. Accordingly, Bregman Voronoi
diagrams allow to define information-theoretic Voronoi diagrams in statistical
parametric spaces based on the relative entropy of distributions. We define
several types of Bregman diagrams, establish correspondences between those
diagrams (using the Legendre transformation), and show how to compute them
efficiently. We also introduce extensions of these diagrams, e.g. k-order and
k-bag Bregman Voronoi diagrams, and introduce Bregman triangulations of a set
of points and their connexion with Bregman Voronoi diagrams. We show that these
triangulations capture many of the properties of the celebrated Delaunay
triangulation. Finally, we give some applications of Bregman Voronoi diagrams
which are of interest in the context of computational geometry and machine
learning.Comment: Extend the proceedings abstract of SODA 2007 (46 pages, 15 figures
Responsible Scoring Mechanisms Through Function Sampling
Human decision-makers often receive assistance from data-driven algorithmic
systems that provide a score for evaluating objects, including individuals. The
scores are generated by a function (mechanism) that takes a set of features as
input and generates a score.The scoring functions are either machine-learned or
human-designed and can be used for different decision purposes such as ranking
or classification.
Given the potential impact of these scoring mechanisms on individuals' lives
and on society, it is important to make sure these scores are computed
responsibly. Hence we need tools for responsible scoring mechanism design. In
this paper, focusing on linear scoring functions, we highlight the importance
of unbiased function sampling and perturbation in the function space for
devising such tools. We provide unbiased samplers for the entire function
space, as well as a -vicinity around a given function.
We then illustrate the value of these samplers for designing effective
algorithms in three diverse problem scenarios in the context of ranking.
Finally, as a fundamental method for designing responsible scoring mechanisms,
we propose a novel approach for approximating the construction of the
arrangement of hyperplanes. Despite the exponential complexity of an
arrangement in the number of dimensions, using function sampling, our algorithm
is linear in the number of samples and hyperplanes, and independent of the
number of dimensions
Orthogonal weighted linear L1 and L∞ approximation and applications
AbstractLet S={s1,s2,...,sn} be a set of sites in Ed, where every site si has a positive real weight ωi. This paper gives algorithms to find weighted orthogonal L∞ and L1 approximating hyperplanes for S. The algorithm for the weighted orthogonal L1 approximation is shown to require O(nd) worst-case time and O(n) space for d ≥ 2. The algorithm for the weighted orthogonal L∞ approximation is shown to require O(n log n) worst-case time and O(n) space for d = 2, and O(n⌊dl2 + 1⌋) worst-case time and O(n⌊(d+1)/2⌋) space for d > 2. In the latter case, the expected time complexity may be reduced to O(n⌊(d+1)/2⌋). The L∞ approximation algorithm can be modified to solve the problem of finding the width of a set of n points in Ed, and the problem of finding a stabbing hyperplane for a set of n hyperspheres in Ed with varying radii. The time and space complexities of the width and stabbing algorithms are seen to be the same as those of the L∞ approximation algorithm
On the multisource hyperplanes location problem to fitting set of points
In this paper we study the problem of locating a given number of hyperplanes
minimizing an objective function of the closest distances from a set of points.
We propose a general framework for the problem in which norm-based distances
between points and hyperplanes are aggregated by means of ordered median
functions. A compact Mixed Integer Linear (or Non Linear) programming
formulation is presented for the problem and also an extended set partitioning
formulation with an exponential number of variables is derived. We develop a
column generation procedure embedded within a branch-and-price algorithm for
solving the problem by adequately performing its preprocessing, pricing and
branching. We also analyze geometrically the optimal solutions of the problem,
deriving properties which are exploited to generate initial solutions for the
proposed algorithms. Finally, the results of an extensive computational
experience are reported. The issue of scalability is also addressed showing
theoretical upper bounds on the errors assumed by replacing the original
datasets by aggregated versions.Comment: 30 pages, 5 Tables, 3 Figure
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