Deformations of Bordered Surfaces and Convex Polytopes Satyan L
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A General Geometric Construction of Coordinates in a Convex Simplicial Polytope
A general geometric construction of coordinates in a convex simplicial polytope ∗ Tao Ju a, Peter Liepa b Joe Warren c aWashington University, St. Louis, USA bAutodesk, Toronto, Canada cRice University, Houston, USA Abstract Barycentric coordinates are a fundamental concept in computer graphics and ge- ometric modeling. We extend the geometric construction of Floater’s mean value coordinates [8,11] to a general form that is capable of constructing a family of coor- dinates in a convex 2D polygon, 3D triangular polyhedron, or a higher-dimensional simplicial polytope. This family unifies previously known coordinates, including Wachspress coordinates, mean value coordinates and discrete harmonic coordinates, in a simple geometric framework. Using the construction, we are able to create a new set of coordinates in 3D and higher dimensions and study its relation with known coordinates. We show that our general construction is complete, that is, the resulting family includes all possible coordinates in any convex simplicial polytope. Key words: Barycentric coordinates, convex simplicial polytopes 1 Introduction In computer graphics and geometric modelling, we often wish to express a point x as an affine combination of a given point set vΣ = {v1,...,vi,...}, x = bivi, where bi =1. (1) i∈Σ i∈Σ Here bΣ = {b1,...,bi,...} are called the coordinates of x with respect to vΣ (we shall use subscript Σ hereafter to denote a set). In particular, bΣ are called barycentric coordinates if they are non-negative. ∗ [email protected] Preprint submitted to Elsevier Science 3 December 2006 v1 v1 x x v4 v2 v2 v3 v3 (a) (b) Fig. -
1 Lifts of Polytopes
Lecture 5: Lifts of polytopes and non-negative rank CSE 599S: Entropy optimality, Winter 2016 Instructor: James R. Lee Last updated: January 24, 2016 1 Lifts of polytopes 1.1 Polytopes and inequalities Recall that the convex hull of a subset X n is defined by ⊆ conv X λx + 1 λ x0 : x; x0 X; λ 0; 1 : ( ) f ( − ) 2 2 [ ]g A d-dimensional convex polytope P d is the convex hull of a finite set of points in d: ⊆ P conv x1;:::; xk (f g) d for some x1;:::; xk . 2 Every polytope has a dual representation: It is a closed and bounded set defined by a family of linear inequalities P x d : Ax 6 b f 2 g for some matrix A m d. 2 × Let us define a measure of complexity for P: Define γ P to be the smallest number m such that for some C s d ; y s ; A m d ; b m, we have ( ) 2 × 2 2 × 2 P x d : Cx y and Ax 6 b : f 2 g In other words, this is the minimum number of inequalities needed to describe P. If P is full- dimensional, then this is precisely the number of facets of P (a facet is a maximal proper face of P). Thinking of γ P as a measure of complexity makes sense from the point of view of optimization: Interior point( methods) can efficiently optimize linear functions over P (to arbitrary accuracy) in time that is polynomial in γ P . ( ) 1.2 Lifts of polytopes Many simple polytopes require a large number of inequalities to describe. -
The Orientability of Small Covers and Coloring Simple Polytopes
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Osaka City University Repository Nakayama, H. and Nishimura, Y. Osaka J. Math. 42 (2005), 243–256 THE ORIENTABILITY OF SMALL COVERS AND COLORING SIMPLE POLYTOPES HISASHI NAKAYAMA and YASUZO NISHIMURA (Received September 1, 2003) Abstract Small Cover is an -dimensional manifold endowed with a Z2 action whose or- bit space is a simple convex polytope . It is known that a small cover over is characterized by a coloring of which satisfies a certain condition. In this paper we shall investigate the topology of small covers by the coloring theory in com- binatorics. We shall first give an orientability condition for a small cover. In case = 3, an orientable small cover corresponds to a four colored polytope. The four color theorem implies the existence of orientable small cover over every simple con- vex 3-polytope. Moreover we shall show the existence of non-orientable small cover over every simple convex 3-polytope, except the 3-simplex. 0. Introduction “Small Cover” was introduced and studied by Davis and Januszkiewicz in [5]. It is a real version of “Quasitoric manifold,” i.e., an -dimensional manifold endowed with an action of the group Z2 whose orbit space is an -dimensional simple convex poly- tope. A typical example is provided by the natural action of Z2 on the real projec- tive space R whose orbit space is an -simplex. Let be an -dimensional simple convex polytope. Here is simple if the number of codimension-one faces (which are called “facets”) meeting at each vertex is , equivalently, the dual of its boundary complex ( ) is an ( 1)-dimensional simplicial sphere. -
Generating Combinatorial Complexes of Polyhedral Type
transactions of the american mathematical society Volume 309, Number 1, September 1988 GENERATING COMBINATORIAL COMPLEXES OF POLYHEDRAL TYPE EGON SCHULTE ABSTRACT. The paper describes a method for generating combinatorial com- plexes of polyhedral type. Building blocks B are implanted into the maximal simplices of a simplicial complex C, on which a group operates as a combinato- rial reflection group. Of particular interest is the case where B is a polyhedral block and C the barycentric subdivision of a regular incidence-polytope K together with the action of the automorphism group of K. 1. Introduction. In this paper we discuss a method for generating certain types of combinatorial complexes. We generalize ideas which were recently applied in [23] for the construction of tilings of the Euclidean 3-space E3 by dodecahedra. The 120-cell {5,3,3} in the Euclidean 4-space E4 is a regular convex polytope whose facets and vertex-figures are dodecahedra and 3-simplices, respectively (cf. Coxeter [6], Fejes Toth [12]). When the 120-cell is centrally projected from some vertex onto the 3-simplex T spanned by the neighboured vertices, then a dissection of T into dodecahedra arises (an example of a central projection is shown in Figure 1). This dissection can be used as a building block for a tiling of E3 by dodecahedra. In fact, in the barycentric subdivision of the regular tessellation {4,3,4} of E3 by cubes each 3-simplex can serve as as a fundamental region for the symmetry group W of {4,3,4} (cf. Coxeter [6]). Therefore, mapping T affinely onto any of these 3-simplices and applying all symmetries in W turns the dissection of T into a tiling T of the whole space E3. -
15 BASIC PROPERTIES of CONVEX POLYTOPES Martin Henk, J¨Urgenrichter-Gebert, and G¨Unterm
15 BASIC PROPERTIES OF CONVEX POLYTOPES Martin Henk, J¨urgenRichter-Gebert, and G¨unterM. Ziegler INTRODUCTION Convex polytopes are fundamental geometric objects that have been investigated since antiquity. The beauty of their theory is nowadays complemented by their im- portance for many other mathematical subjects, ranging from integration theory, algebraic topology, and algebraic geometry to linear and combinatorial optimiza- tion. In this chapter we try to give a short introduction, provide a sketch of \what polytopes look like" and \how they behave," with many explicit examples, and briefly state some main results (where further details are given in subsequent chap- ters of this Handbook). We concentrate on two main topics: • Combinatorial properties: faces (vertices, edges, . , facets) of polytopes and their relations, with special treatments of the classes of low-dimensional poly- topes and of polytopes \with few vertices;" • Geometric properties: volume and surface area, mixed volumes, and quer- massintegrals, including explicit formulas for the cases of the regular simplices, cubes, and cross-polytopes. We refer to Gr¨unbaum [Gr¨u67]for a comprehensive view of polytope theory, and to Ziegler [Zie95] respectively to Gruber [Gru07] and Schneider [Sch14] for detailed treatments of the combinatorial and of the convex geometric aspects of polytope theory. 15.1 COMBINATORIAL STRUCTURE GLOSSARY d V-polytope: The convex hull of a finite set X = fx1; : : : ; xng of points in R , n n X i X P = conv(X) := λix λ1; : : : ; λn ≥ 0; λi = 1 : i=1 i=1 H-polytope: The solution set of a finite system of linear inequalities, d T P = P (A; b) := x 2 R j ai x ≤ bi for 1 ≤ i ≤ m ; with the extra condition that the set of solutions is bounded, that is, such that m×d there is a constant N such that jjxjj ≤ N holds for all x 2 P . -
Frequently Asked Questions in Polyhedral Computation
Frequently Asked Questions in Polyhedral Computation http://www.ifor.math.ethz.ch/~fukuda/polyfaq/polyfaq.html Komei Fukuda Swiss Federal Institute of Technology Lausanne and Zurich, Switzerland [email protected] Version June 18, 2004 Contents 1 What is Polyhedral Computation FAQ? 2 2 Convex Polyhedron 3 2.1 What is convex polytope/polyhedron? . 3 2.2 What are the faces of a convex polytope/polyhedron? . 3 2.3 What is the face lattice of a convex polytope . 4 2.4 What is a dual of a convex polytope? . 4 2.5 What is simplex? . 4 2.6 What is cube/hypercube/cross polytope? . 5 2.7 What is simple/simplicial polytope? . 5 2.8 What is 0-1 polytope? . 5 2.9 What is the best upper bound of the numbers of k-dimensional faces of a d- polytope with n vertices? . 5 2.10 What is convex hull? What is the convex hull problem? . 6 2.11 What is the Minkowski-Weyl theorem for convex polyhedra? . 6 2.12 What is the vertex enumeration problem, and what is the facet enumeration problem? . 7 1 2.13 How can one enumerate all faces of a convex polyhedron? . 7 2.14 What computer models are appropriate for the polyhedral computation? . 8 2.15 How do we measure the complexity of a convex hull algorithm? . 8 2.16 How many facets does the average polytope with n vertices in Rd have? . 9 2.17 How many facets can a 0-1 polytope with n vertices in Rd have? . 10 2.18 How hard is it to verify that an H-polyhedron PH and a V-polyhedron PV are equal? . -
Notes on Convex Sets, Polytopes, Polyhedra, Combinatorial Topology, Voronoi Diagrams and Delaunay Triangulations
Notes on Convex Sets, Polytopes, Polyhedra, Combinatorial Topology, Voronoi Diagrams and Delaunay Triangulations Jean Gallier and Jocelyn Quaintance Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104, USA e-mail: [email protected] April 20, 2017 2 3 Notes on Convex Sets, Polytopes, Polyhedra, Combinatorial Topology, Voronoi Diagrams and Delaunay Triangulations Jean Gallier Abstract: Some basic mathematical tools such as convex sets, polytopes and combinatorial topology, are used quite heavily in applied fields such as geometric modeling, meshing, com- puter vision, medical imaging and robotics. This report may be viewed as a tutorial and a set of notes on convex sets, polytopes, polyhedra, combinatorial topology, Voronoi Diagrams and Delaunay Triangulations. It is intended for a broad audience of mathematically inclined readers. One of my (selfish!) motivations in writing these notes was to understand the concept of shelling and how it is used to prove the famous Euler-Poincar´eformula (Poincar´e,1899) and the more recent Upper Bound Theorem (McMullen, 1970) for polytopes. Another of my motivations was to give a \correct" account of Delaunay triangulations and Voronoi diagrams in terms of (direct and inverse) stereographic projections onto a sphere and prove rigorously that the projective map that sends the (projective) sphere to the (projective) paraboloid works correctly, that is, maps the Delaunay triangulation and Voronoi diagram w.r.t. the lifting onto the sphere to the Delaunay diagram and Voronoi diagrams w.r.t. the traditional lifting onto the paraboloid. Here, the problem is that this map is only well defined (total) in projective space and we are forced to define the notion of convex polyhedron in projective space. -
36 COMPUTATIONAL CONVEXITY Peter Gritzmann and Victor Klee
36 COMPUTATIONAL CONVEXITY Peter Gritzmann and Victor Klee INTRODUCTION The subject of Computational Convexity draws its methods from discrete mathe- matics and convex geometry, and many of its problems from operations research, computer science, data analysis, physics, material science, and other applied ar- eas. In essence, it is the study of the computational and algorithmic aspects of high-dimensional convex sets (especially polytopes), with a view to applying the knowledge gained to convex bodies that arise in other mathematical disciplines or in the mathematical modeling of problems from outside mathematics. The name Computational Convexity is of more recent origin, having first ap- peared in print in 1989. However, results that retrospectively belong to this area go back a long way. In particular, many of the basic ideas of Linear Programming have an essentially geometric character and fit very well into the conception of Compu- tational Convexity. The same is true of the subject of Polyhedral Combinatorics and of the Algorithmic Theory of Polytopes and Convex Bodies. The emphasis in Computational Convexity is on problems whose underlying structure is the convex geometry of normed vector spaces of finite but generally not restricted dimension, rather than of fixed dimension. This leads to closer con- nections with the optimization problems that arise in a wide variety of disciplines. Further, in the study of Computational Convexity, the underlying model of com- putation is mainly the binary (Turing machine) model that is common in studies of computational complexity. This requirement is imposed by prospective appli- cations, particularly in mathematical programming. For the study of algorithmic aspects of convex bodies that are not polytopes, the binary model is often aug- mented by additional devices called \oracles." Some cases of interest involve other models of computation, but the present discussion focuses on aspects of computa- tional convexity for which binary models seem most natural. -
Convex Hull in Higher Dimensions
Comp 163: Computational Geometry Professor Diane Souvaine Tufts University, Spring 2005 1991 Scribe: Gabor M. Czako, 1990 Scribe: Sesh Venugopal, 2004 Scrib Higher Dimensional Convex Hull Algorithms 1 Convex hulls 1.0.1 Definitions The following definitions will be used throughout. Define • Sd:Ad-Simplex The simplest convex polytope in Rd.Ad-simplex is always the convex hull of some d + 1 affinely independent points. For example, a line segment is a 1 − simplex i.e., the smallest convex subspace which con- tains two points. A triangle is a 2 − simplex and a tetrahedron is a 3 − simplex. • P: A Simplicial Polytope. A polytope where each facet is a d − 1 simplex. By our assumption, a convex hull in 3-D has triangular facets and in 4-D, a convex hull has tetrahedral facets. •P: number of facets of Pi • F : a facet of P • R:aridgeofF • G:afaceofP d+1 • fj(Pi ): The number of j-faces of P .Notethatfj(Sd)=C(j+1). 1.0.2 An Upper Bound on Time Complexity: Cyclic Polytope d 1 2 d Consider the curve Md in R defined as x(t)=(t ,t , ..., t ), t ∈ R.LetH be the hyperplane defined as a0 + a1x1 + a2x2 + ... + adxd = 0. The intersection 2 d of Md and H is the set of points that satisfy a0 + a1t + a2t + ...adt =0. This polynomial has at most d real roots and therefore d real solutions. → H intersects Md in at most d points. This brings us to the following definition: A convex polytope in Rd is a cyclic polytope if it is the convex hull of a 1 set of at least d +1pointsonMd. -
Scribability Problems for Polytopes
SCRIBABILITY PROBLEMS FOR POLYTOPES HAO CHEN AND ARNAU PADROL Abstract. In this paper we study various scribability problems for polytopes. We begin with the classical k-scribability problem proposed by Steiner and generalized by Schulte, which asks about the existence of d-polytopes that cannot be realized with all k-faces tangent to a sphere. We answer this problem for stacked and cyclic polytopes for all values of d and k. We then continue with the weak scribability problem proposed by Gr¨unbaum and Shephard, for which we complete the work of Schulte by presenting non weakly circumscribable 3-polytopes. Finally, we propose new (i; j)-scribability problems, in a strong and a weak version, which generalize the classical ones. They ask about the existence of d-polytopes that can not be realized with all their i-faces \avoiding" the sphere and all their j-faces \cutting" the sphere. We provide such examples for all the cases where j − i ≤ d − 3. Contents 1. Introduction 2 1.1. k-scribability 2 1.2. (i; j)-scribability 3 Acknowledgements 4 2. Lorentzian view of polytopes 4 2.1. Convex polyhedral cones in Lorentzian space 4 2.2. Spherical, Euclidean and hyperbolic polytopes 5 3. Definitions and properties 6 3.1. Strong k-scribability 6 3.2. Weak k-scribability 7 3.3. Strong and weak (i; j)-scribability 9 3.4. Properties of (i; j)-scribability 9 4. Weak scribability 10 4.1. Weak k-scribability 10 4.2. Weak (i; j)-scribability 13 5. Stacked polytopes 13 5.1. Circumscribability 14 5.2. -
Combinatorial Aspects of Convex Polytopes Margaret M
Combinatorial Aspects of Convex Polytopes Margaret M. Bayer1 Department of Mathematics University of Kansas Carl W. Lee2 Department of Mathematics University of Kentucky August 1, 1991 Chapter for Handbook on Convex Geometry P. Gruber and J. Wills, Editors 1Supported in part by NSF grant DMS-8801078. 2Supported in part by NSF grant DMS-8802933, by NSA grant MDA904-89-H-2038, and by DIMACS (Center for Discrete Mathematics and Theoretical Computer Science), a National Science Foundation Science and Technology Center, NSF-STC88-09648. 1 Definitions and Fundamental Results 3 1.1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.2 Faces : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.3 Polarity and Duality : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.4 Overview : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2 Shellings 4 2.1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2.2 Euler's Relation : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2.3 Line Shellings : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 2.4 Shellable Simplicial Complexes : : : : : : : : : : : : : : : : : : : 5 2.5 The Dehn-Sommerville Equations : : : : : : : : : : : : : : : : : : 6 2.6 Completely Unimodal Numberings and Orientations : : : : : : : 7 2.7 The Upper Bound Theorem : : : : : : : : : : : : : : : : : : : : : 8 2.8 The Lower Bound Theorem : : : : : : : : : : : : : : : : : : : : : 9 2.9 Constructions Using Shellings : : : : : : : : : : : : : -
The Geometry of Colour
The Geometry of Colour Paul Centore The Geometry of Colour Paul Centore Suggested Cataloging Data Centore, Paul The Geometry of Colour/P. Centore 221 p., 21.6 by 14.0 cm ISBN 978-0-9990291-0-7 1. Color 2. Color—Mathematics 3. Colorimetry 4. Convex Geometry I. Centore, Paul, 1968- II. Title QC495.C397 2017 535.6 Coopyright c 2017 by Paul Centore • All rights reserved Printed in the United States of America ISBN 978-0-9990291-0-7 ISBN 9780999029107 9 780999 029107 Introduction Colour is a universal experience, which has been investigated since an- tiquity. Today, colour science rests firmly on both empirical findings and theoretical development. Unlike many technical fields, colour sci- ence considers not only physical factors, but also human perception, and the relationships between the two. A basic part of colour science is colour matching, which identifies when two side-by-side lights, viewed in isolation through an aperture, produce the same colour. The Geometry of Colour formulates colour matching mathematically, emphasizing geometric constructions and understanding. The motiva- tion for writing it was the unifying insight that many apparently dis- parate objects in colour science share a common zonohedral structure. The book aims at both rigor and intuition. Fortunately, many colour science objects can be explicitly constructed in a three-dimensional vector space, and, while linear algebra insures rigorous definitions, a premium is placed on a concrete visual and spatial presentation— ideally, a motivated reader could build literal models, for example with foam board and glue. This book is intended for mathematicians, colour scientists, and, as much as possible, curious non-specialists.