2,627 research outputs found
Intersection of paraboloids and application to Minkowski-type problems
In this article, we study the intersection (or union) of the convex hull of N
confocal paraboloids (or ellipsoids) of revolution. This study is motivated by
a Minkowski-type problem arising in geometric optics. We show that in each of
the four cases, the combinatorics is given by the intersection of a power
diagram with the unit sphere. We prove the complexity is O(N) for the
intersection of paraboloids and Omega(N^2) for the intersection and the union
of ellipsoids. We provide an algorithm to compute these intersections using the
exact geometric computation paradigm. This algorithm is optimal in the case of
the intersection of ellipsoids and is used to solve numerically the far-field
reflector problem
A greedy algorithm for yield surface approximation
International audienceThis Note presents an approximation method for convex yield surfaces in the framework of yield design theory. The proposed algorithm constructs an approximation using a convex hull of ellipsoids such that the approximate criterion can be formulated in terms of second-order conic constraints. The algorithm can treat bounded as well as unbounded yield surfaces. Its efficiency is illustrated on two yield surfaces obtained using up-scaling procedures
Convex bodies of states and maps
We give a general solution to the question when the convex hulls of orbits of
quantum states on a finite-dimensional Hilbert space under unitary actions of a
compact group have a non-empty interior in the surrounding space of all density
states. The same approach can be applied to study convex combinations of
quantum channels. The importance of both problems stems from the fact that,
usually, only sets with non-vanishing volumes in the embedding spaces of all
states or channels are of practical importance. For the group of local
transformations on a bipartite system we characterize maximally entangled
states by properties of a convex hull of orbits through them. We also compare
two partial characteristics of convex bodies in terms of largest balls and
maximum volume ellipsoids contained in them and show that, in general, they do
not coincide. Separable states, mixed-unitary channels and k-entangled states
are also considered as examples of our techniques.Comment: 18 pages, 1 figur
Classification and Geometry of General Perceptual Manifolds
Perceptual manifolds arise when a neural population responds to an ensemble
of sensory signals associated with different physical features (e.g.,
orientation, pose, scale, location, and intensity) of the same perceptual
object. Object recognition and discrimination requires classifying the
manifolds in a manner that is insensitive to variability within a manifold. How
neuronal systems give rise to invariant object classification and recognition
is a fundamental problem in brain theory as well as in machine learning. Here
we study the ability of a readout network to classify objects from their
perceptual manifold representations. We develop a statistical mechanical theory
for the linear classification of manifolds with arbitrary geometry revealing a
remarkable relation to the mathematics of conic decomposition. Novel
geometrical measures of manifold radius and manifold dimension are introduced
which can explain the classification capacity for manifolds of various
geometries. The general theory is demonstrated on a number of representative
manifolds, including L2 ellipsoids prototypical of strictly convex manifolds,
L1 balls representing polytopes consisting of finite sample points, and
orientation manifolds which arise from neurons tuned to respond to a continuous
angle variable, such as object orientation. The effects of label sparsity on
the classification capacity of manifolds are elucidated, revealing a scaling
relation between label sparsity and manifold radius. Theoretical predictions
are corroborated by numerical simulations using recently developed algorithms
to compute maximum margin solutions for manifold dichotomies. Our theory and
its extensions provide a powerful and rich framework for applying statistical
mechanics of linear classification to data arising from neuronal responses to
object stimuli, as well as to artificial deep networks trained for object
recognition tasks.Comment: 24 pages, 12 figures, Supplementary Material
High posterior density ellipsoids of quantum states
Regions of quantum states generalize the classical notion of error bars. High
posterior density (HPD) credible regions are the most powerful of region
estimators. However, they are intractably hard to construct in general. This
paper reports on a numerical approximation to HPD regions for the purpose of
testing a much more computationally and conceptually convenient class of
regions: posterior covariance ellipsoids (PCEs). The PCEs are defined via the
covariance matrix of the posterior probability distribution of states. Here it
is shown that PCEs are near optimal for the example of Pauli measurements on
multiple qubits. Moreover, the algorithm is capable of producing accurate PCE
regions even when there is uncertainty in the model.Comment: TL;DR version: computationally feasible region estimator
On the volume of the convex hull of two convex bodies
In this note we examine the volume of the convex hull of two congruent copies
of a convex body in Euclidean -space, under some subsets of the isometry
group of the space. We prove inequalities for this volume if the two bodies are
translates, or reflected copies of each other about a common point or a
hyperplane containing it. In particular, we give a proof of a related
conjecture of Rogers and Shephard.Comment: 9 pages, 3 figure
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