Search results for "Computer Science - Computational Geometry"

showing 7 items of 7 documents

On the reducibility of geometric constraint graphs

2018

Geometric modeling by constraints, whose applications are of interest to communities from various fields such as mechanical engineering, computer aided design, symbolic computation or molecular chemistry, is now integrated into standard modeling tools. In this discipline, a geometric form is specified by the relations that the components of this form must verify instead of explicitly specifying these components. The purpose of the resolution is to deduce the form satisfying all these constraints. Various methods have been proposed to solve this problem. We will focus on the so-called graph-based or graph-based methods with application to the two-dimensional space.

Computational Geometry (cs.CG)FOS: Computer and information sciences[ INFO.INFO-CG ] Computer Science [cs]/Computational Geometry [cs.CG]Computer Science - Computational Geometry[INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG]
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Computation of the topological type of a real Riemann surface

2012

We present an algorithm for the computation of the topological type of a real compact Riemann surface associated to an algebraic curve, i.e., its genus and the properties of the set of fixed points of the anti-holomorphic involution $\tau$, namely, the number of its connected components, and whether this set divides the surface into one or two connected components. This is achieved by transforming an arbitrary canonical homology basis to a homology basis where the $\mathcal{A}$-cycles are invariant under the anti-holomorphic involution $\tau$.

Computational Geometry (cs.CG)FOS: Computer and information sciencesreal Riemann surface[MATH.MATH-AG] Mathematics [math]/Algebraic Geometry [math.AG]homology basis[ MATH.MATH-AG ] Mathematics [math]/Algebraic Geometry [math.AG]Mathematics - Algebraic Geometryreal algebraic curveholomorphic differentialsFOS: MathematicsComputer Science - Computational Geometryreal ovals[MATH.MATH-AG]Mathematics [math]/Algebraic Geometry [math.AG]Algebraic Geometry (math.AG)
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Efficient computation of the branching structure of an algebraic curve

2012

An efficient algorithm for computing the branching structure of a compact Riemann surface defined via an algebraic curve is presented. Generators of the fundamental group of the base of the ramified covering punctured at the discriminant points of the curve are constructed via a minimal spanning tree of the discriminant points. This leads to paths of minimal length between the points, which is important for a later stage where these paths are used as integration contours to compute periods of the surface. The branching structure of the surface is obtained by analytically continuing the roots of the equation defining the algebraic curve along the constructed generators of the fundamental gro…

Discrete mathematicsCircular algebraic curveComputational Geometry (cs.CG)FOS: Computer and information sciencesStable curveApplied MathematicsButterfly curve (algebraic)010102 general mathematics010103 numerical & computational mathematics01 natural sciencesModular curveMathematics - Algebraic GeometryComputational Theory and Mathematics14Q05Algebraic surfaceFOS: MathematicsComputer Science - Computational GeometryAlgebraic functionAlgebraic curve0101 mathematicsHyperelliptic curveAlgebraic Geometry (math.AG)AnalysisMathematics
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Fully Automatic Trunk Packing with Free Placements

2010

We present a new algorithm to compute the volume of a trunk according to the SAE J1100 standard. Our new algorithm uses state-of-the-art methods from computational geometry and from combinatorial optimization. It finds better solutions than previous approaches for small trunks.

Computational Geometry (cs.CG)FOS: Computer and information sciencesDiscrete Mathematics (cs.DM)ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKSComputer Science - Data Structures and AlgorithmsComputer Science - Computational GeometryData Structures and Algorithms (cs.DS)Computer Science - Discrete Mathematics
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Topology-based goodness-of-fit tests for sliced spatial data

2023

In materials science and many other application domains, 3D information can often only be extrapolated by taking 2D slices. In topological data analysis, persistence vineyards have emerged as a powerful tool to take into account topological features stretching over several slices. In the present paper, we illustrate how persistence vineyards can be used to design rigorous statistical hypothesis tests for 3D microstructure models based on data from 2D slices. More precisely, by establishing the asymptotic normality of suitable longitudinal and cross-sectional summary statistics, we devise goodness-of-fit tests that become asymptotically exact in large sampling windows. We illustrate the test…

Computational Geometry (cs.CG)FOS: Computer and information sciencesStatistics and ProbabilityGoodness-of-fit testsApplied MathematicsTopological data analysisPersistence diagramMathematics - Statistics TheoryStatistics Theory (math.ST)VineyardsMaterials scienceComputational MathematicsComputational Theory and Mathematics60F05Topological data analysis Persistence diagram Materials science Vineyards Goodness-of-fit tests Asymptotic normalityFOS: MathematicsAlgebraic Topology (math.AT)Computer Science - Computational GeometryAsymptotic normalityMathematics - Algebraic TopologyComputational Statistics & Data Analysis
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Optimal rates of convergence for persistence diagrams in Topological Data Analysis

2013

Computational topology has recently known an important development toward data analysis, giving birth to the field of topological data analysis. Topological persistence, or persistent homology, appears as a fundamental tool in this field. In this paper, we study topological persistence in general metric spaces, with a statistical approach. We show that the use of persistent homology can be naturally considered in general statistical frameworks and persistence diagrams can be used as statistics with interesting convergence properties. Some numerical experiments are performed in various contexts to illustrate our results.

Computational Geometry (cs.CG)FOS: Computer and information sciences[ MATH.MATH-GT ] Mathematics [math]/Geometric Topology [math.GT][STAT.TH] Statistics [stat]/Statistics Theory [stat.TH]Topological Data analysis Persistent homology minimax convergence rates geometric complexes metric spacesGeometric Topology (math.GT)Mathematics - Statistics TheoryStatistics Theory (math.ST)[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG][STAT.TH]Statistics [stat]/Statistics Theory [stat.TH][INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG][ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH][ INFO.INFO-LG ] Computer Science [cs]/Machine Learning [cs.LG]Machine Learning (cs.LG)Computer Science - LearningMathematics - Geometric Topology[INFO.INFO-CG] Computer Science [cs]/Computational Geometry [cs.CG][INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][MATH.MATH-GT]Mathematics [math]/Geometric Topology [math.GT]FOS: Mathematics[ INFO.INFO-CG ] Computer Science [cs]/Computational Geometry [cs.CG]Computer Science - Computational Geometry[MATH.MATH-GT] Mathematics [math]/Geometric Topology [math.GT]
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Approximation of functions over manifolds : A Moving Least-Squares approach

2021

We present an algorithm for approximating a function defined over a $d$-dimensional manifold utilizing only noisy function values at locations sampled from the manifold with noise. To produce the approximation we do not require any knowledge regarding the manifold other than its dimension $d$. We use the Manifold Moving Least-Squares approach of (Sober and Levin 2016) to reconstruct the atlas of charts and the approximation is built on-top of those charts. The resulting approximant is shown to be a function defined over a neighborhood of a manifold, approximating the originally sampled manifold. In other words, given a new point, located near the manifold, the approximation can be evaluated…

Computational Geometry (cs.CG)FOS: Computer and information sciencesComputer Science - Machine LearningClosed manifolddimension reductionMachine Learning (stat.ML)010103 numerical & computational mathematicsComplex dimensionTopology01 natural sciencesMachine Learning (cs.LG)Volume formComputer Science - GraphicsStatistics - Machine Learningmanifold learningApplied mathematics0101 mathematicsfunktiotMathematicsManifold alignmentAtlas (topology)Applied Mathematicshigh dimensional approximationManifoldGraphics (cs.GR)Statistical manifold010101 applied mathematicsregression over manifoldsComputational Mathematicsout-of-sample extensionComputer Science - Computational Geometrynumeerinen analyysimonistotapproksimointimoving least-squaresCenter manifold
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