Constructions of Cubical Polytopes Alexander Schwartz
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Request for Contract Update
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On the Circuit Diameter Conjecture
On the Circuit Diameter Conjecture Steffen Borgwardt1, Tamon Stephen2, and Timothy Yusun2 1 University of Colorado Denver [email protected] 2 Simon Fraser University {tamon,tyusun}@sfu.ca Abstract. From the point of view of optimization, a critical issue is relating the combina- torial diameter of a polyhedron to its number of facets f and dimension d. In the seminal paper of Klee and Walkup [KW67], the Hirsch conjecture of an upper bound of f − d was shown to be equivalent to several seemingly simpler statements, and was disproved for unbounded polyhedra through the construction of a particular 4-dimensional polyhedron U4 with 8 facets. The Hirsch bound for bounded polyhedra was only recently disproved by Santos [San12]. We consider analogous properties for a variant of the combinatorial diameter called the circuit diameter. In this variant, the walks are built from the circuit directions of the poly- hedron, which are the minimal non-trivial solutions to the system defining the polyhedron. We are able to prove that circuit variants of the so-called non-revisiting conjecture and d-step conjecture both imply the circuit analogue of the Hirsch conjecture. For the equiva- lences in [KW67], the wedge construction was a fundamental proof technique. We exhibit why it is not available in the circuit setting, and what are the implications of losing it as a tool. Further, we show the circuit analogue of the non-revisiting conjecture implies a linear bound on the circuit diameter of all unbounded polyhedra – in contrast to what is known for the combinatorial diameter. Finally, we give two proofs of a circuit version of the 4-step conjecture. -
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. -
7 LATTICE POINTS and LATTICE POLYTOPES Alexander Barvinok
7 LATTICE POINTS AND LATTICE POLYTOPES Alexander Barvinok INTRODUCTION Lattice polytopes arise naturally in algebraic geometry, analysis, combinatorics, computer science, number theory, optimization, probability and representation the- ory. They possess a rich structure arising from the interaction of algebraic, convex, analytic, and combinatorial properties. In this chapter, we concentrate on the the- ory of lattice polytopes and only sketch their numerous applications. We briefly discuss their role in optimization and polyhedral combinatorics (Section 7.1). In Section 7.2 we discuss the decision problem, the problem of finding whether a given polytope contains a lattice point. In Section 7.3 we address the counting problem, the problem of counting all lattice points in a given polytope. The asymptotic problem (Section 7.4) explores the behavior of the number of lattice points in a varying polytope (for example, if a dilation is applied to the polytope). Finally, in Section 7.5 we discuss problems with quantifiers. These problems are natural generalizations of the decision and counting problems. Whenever appropriate we address algorithmic issues. For general references in the area of computational complexity/algorithms see [AB09]. We summarize the computational complexity status of our problems in Table 7.0.1. TABLE 7.0.1 Computational complexity of basic problems. PROBLEM NAME BOUNDED DIMENSION UNBOUNDED DIMENSION Decision problem polynomial NP-hard Counting problem polynomial #P-hard Asymptotic problem polynomial #P-hard∗ Problems with quantifiers unknown; polynomial for ∀∃ ∗∗ NP-hard ∗ in bounded codimension, reduces polynomially to volume computation ∗∗ with no quantifier alternation, polynomial time 7.1 INTEGRAL POLYTOPES IN POLYHEDRAL COMBINATORICS We describe some combinatorial and computational properties of integral polytopes. -
The Geometry of Nim Arxiv:1109.6712V1 [Math.CO] 30
The Geometry of Nim Kevin Gibbons Abstract We relate the Sierpinski triangle and the game of Nim. We begin by defining both a new high-dimensional analog of the Sierpinski triangle and a natural geometric interpretation of the losing positions in Nim, and then, in a new result, show that these are equivalent in each finite dimension. 0 Introduction The Sierpinski triangle (fig. 1) is one of the most recognizable figures in mathematics, and with good reason. It appears in everything from Pascal's Triangle to Conway's Game of Life. In fact, it has already been seen to be connected with the game of Nim, albeit in a very different manner than the one presented here [3]. A number of analogs have been discovered, such as the Menger sponge (fig. 2) and a three-dimensional version called a tetrix. We present, in the first section, a generalization in higher dimensions differing from the more typical simplex generalization. Rather, we define a discrete Sierpinski demihypercube, which in three dimensions coincides with the simplex generalization. In the second section, we briefly review Nim and the theory behind optimal play. As in all impartial games (games in which the possible moves depend only on the state of the game, and not on which of the two players is moving), all possible positions can be divided into two classes - those in which the next player to move can force a win, called N-positions, and those in which regardless of what the next player does the other player can force a win, called P -positions. -
Fitting Tractable Convex Sets to Support Function Evaluations
Fitting Tractable Convex Sets to Support Function Evaluations Yong Sheng Sohy and Venkat Chandrasekaranz ∗ y Institute of High Performance Computing 1 Fusionopolis Way #16-16 Connexis Singapore 138632 z Department of Computing and Mathematical Sciences Department of Electrical Engineering California Institute of Technology Pasadena, CA 91125, USA March 11, 2019 Abstract The geometric problem of estimating an unknown compact convex set from evaluations of its support function arises in a range of scientific and engineering applications. Traditional ap- proaches typically rely on estimators that minimize the error over all possible compact convex sets; in particular, these methods do not allow for the incorporation of prior structural informa- tion about the underlying set and the resulting estimates become increasingly more complicated to describe as the number of measurements available grows. We address both of these short- comings by describing a framework for estimating tractably specified convex sets from support function evaluations. Building on the literature in convex optimization, our approach is based on estimators that minimize the error over structured families of convex sets that are specified as linear images of concisely described sets { such as the simplex or the free spectrahedron { in a higher-dimensional space that is not much larger than the ambient space. Convex sets parametrized in this manner are significant from a computational perspective as one can opti- mize linear functionals over such sets efficiently; they serve a different purpose in the inferential context of the present paper, namely, that of incorporating regularization in the reconstruction while still offering considerable expressive power. We provide a geometric characterization of the asymptotic behavior of our estimators, and our analysis relies on the property that certain sets which admit semialgebraic descriptions are Vapnik-Chervonenkis (VC) classes. -
Notes Has Not Been Formally Reviewed by the Lecturer
6.896 Topics in Algorithmic Game Theory February 22, 2010 Lecture 6 Lecturer: Constantinos Daskalakis Scribe: Jason Biddle, Debmalya Panigrahi NOTE: The content of these notes has not been formally reviewed by the lecturer. It is recommended that they are read critically. In our last lecture, we proved Nash's Theorem using Broweur's Fixed Point Theorem. We also showed Brouwer's Theorem via Sperner's Lemma in 2-d using a limiting argument to go from the discrete combinatorial problem to the topological one. In this lecture, we present a multidimensional generalization of the proof from last time. Our proof differs from those typically found in the literature. In particular, we will insist that each step of the proof be constructive. Using constructive arguments, we shall be able to pin down the complexity-theoretic nature of the proof and make the steps algorithmic in subsequent lectures. In the first part of our lecture, we present a framework for the multidimensional generalization of Sperner's Lemma. A Canonical Triangulation of the Hypercube • A Legal Coloring Rule • In the second part, we formally state Sperner's Lemma in n dimensions and prove it using the following constructive steps: Colored Envelope Construction • Definition of the Walk • Identification of the Starting Simplex • Direction of the Walk • 1 Framework Recall that in the 2-dimensional case we had a square which was divided into triangles. We had also defined a legal coloring scheme for the triangle vertices lying on the boundary of the square. Now let us extend those concepts to higher dimensions. 1.1 Canonical Triangulation of the Hypercube We begin by introducing the n-simplex as the n-dimensional analog of the triangle in 2 dimensions as shown in Figure 1. -
Interval Vector Polytopes
Interval Vector Polytopes By Gabriel Dorfsman-Hopkins SENIOR HONORS THESIS ROSA C. ORELLANA, ADVISOR DEPARTMENT OF MATHEMATICS DARTMOUTH COLLEGE JUNE, 2013 Contents Abstract iv 1 Introduction 1 2 Preliminaries 4 2.1 Convex Polytopes . 4 2.2 Lattice Polytopes . 8 2.2.1 Ehrhart Theory . 10 2.3 Faces, Face Lattices and the Dual . 12 2.3.1 Faces of a Polytope . 12 2.3.2 The Face Lattice of a Polytope . 15 2.3.3 Duality . 18 2.4 Basic Graph Theory . 22 3IntervalVectorPolytopes 23 3.1 The Complete Interval Vector Polytope . 25 3.2 TheFixedIntervalVectorPolytope . 30 3.2.1 FlowDimensionGraphs . 31 4TheIntervalPyramid 38 ii 4.1 f-vector of the Interval Pyramid . 40 4.2 Volume of the Interval Pyramid . 45 4.3 Duality of the Interval Pyramid . 50 5 Open Questions 57 5.1 Generalized Interval Pyramid . 58 Bibliography 60 iii Abstract An interval vector is a 0, 1 -vector where all the ones appear consecutively. An { } interval vector polytope is the convex hull of a set of interval vectors in Rn.Westudy several classes of interval vector polytopes which exhibit interesting combinatorial- geometric properties. In particular, we study a class whose volumes are equal to the catalan numbers, another class whose legs always form a lattice basis for their affine space, and a third whose face numbers are given by the pascal 3-triangle. iv Chapter 1 Introduction An interval vector is a 0, 1 -vector in Rn such that the ones (if any) occur consecu- { } tively. These vectors can nicely and discretely model scheduling problems where they represent the length of an uninterrupted activity, and so understanding the combi- natorics of these vectors is useful in optimizing scheduling problems of continuous activities of certain lengths. -
Simplicial Complexes
46 III Complexes III.1 Simplicial Complexes There are many ways to represent a topological space, one being a collection of simplices that are glued to each other in a structured manner. Such a collection can easily grow large but all its elements are simple. This is not so convenient for hand-calculations but close to ideal for computer implementations. In this book, we use simplicial complexes as the primary representation of topology. Rd k Simplices. Let u0; u1; : : : ; uk be points in . A point x = i=0 λiui is an affine combination of the ui if the λi sum to 1. The affine hull is the set of affine combinations. It is a k-plane if the k + 1 points are affinely Pindependent by which we mean that any two affine combinations, x = λiui and y = µiui, are the same iff λi = µi for all i. The k + 1 points are affinely independent iff P d P the k vectors ui − u0, for 1 ≤ i ≤ k, are linearly independent. In R we can have at most d linearly independent vectors and therefore at most d+1 affinely independent points. An affine combination x = λiui is a convex combination if all λi are non- negative. The convex hull is the set of convex combinations. A k-simplex is the P convex hull of k + 1 affinely independent points, σ = conv fu0; u1; : : : ; ukg. We sometimes say the ui span σ. Its dimension is dim σ = k. We use special names of the first few dimensions, vertex for 0-simplex, edge for 1-simplex, triangle for 2-simplex, and tetrahedron for 3-simplex; see Figure III.1. -
Archimedean Solids
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln MAT Exam Expository Papers Math in the Middle Institute Partnership 7-2008 Archimedean Solids Anna Anderson University of Nebraska-Lincoln Follow this and additional works at: https://digitalcommons.unl.edu/mathmidexppap Part of the Science and Mathematics Education Commons Anderson, Anna, "Archimedean Solids" (2008). MAT Exam Expository Papers. 4. https://digitalcommons.unl.edu/mathmidexppap/4 This Article is brought to you for free and open access by the Math in the Middle Institute Partnership at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in MAT Exam Expository Papers by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Archimedean Solids Anna Anderson In partial fulfillment of the requirements for the Master of Arts in Teaching with a Specialization in the Teaching of Middle Level Mathematics in the Department of Mathematics. Jim Lewis, Advisor July 2008 2 Archimedean Solids A polygon is a simple, closed, planar figure with sides formed by joining line segments, where each line segment intersects exactly two others. If all of the sides have the same length and all of the angles are congruent, the polygon is called regular. The sum of the angles of a regular polygon with n sides, where n is 3 or more, is 180° x (n – 2) degrees. If a regular polygon were connected with other regular polygons in three dimensional space, a polyhedron could be created. In geometry, a polyhedron is a three- dimensional solid which consists of a collection of polygons joined at their edges. The word polyhedron is derived from the Greek word poly (many) and the Indo-European term hedron (seat). -
Polytopes with Special Simplices 3
POLYTOPES WITH SPECIAL SIMPLICES TIMO DE WOLFF Abstract. For a polytope P a simplex Σ with vertex set V(Σ) is called a special simplex if every facet of P contains all but exactly one vertex of Σ. For such polytopes P with face complex F(P ) containing a special simplex the sub- complex F(P )\V(Σ) of all faces not containing vertices of Σ is the boundary of a polytope Q — the basis polytope of P . If additionally the dimension of the affine basis space of F(P )\V(Σ) equals dim(Q), we call P meek; otherwise we call P wild. We give a full combinatorial classification and techniques for geometric construction of the class of meek polytopes with special simplices. We show that every wild polytope P ′ with special simplex can be constructed out of a particular meek one P by intersecting P with particular hyperplanes. It is non–trivial to find all these hyperplanes for an arbitrary basis polytope; we give an exact description for 2–basis polytopes. Furthermore we show that the f–vector of each wild polytope with special simplex is componentwise bounded above by the f–vector of a particular meek one which can be computed explicitly. Finally, we discuss the n–cube as a non–trivial example of a wild polytope with special simplex and prove that its basis polytope is the zonotope given by the Minkowski sum of the (n − 1)–cube and vector (1,..., 1). Polytopes with special simplex have applications on Ehrhart theory, toric rings and were just used by Francisco Santos to construct a counter–example disproving the Hirsch conjecture. -
F-VECTORS of BARYCENTRIC SUBDIVISIONS 1. Introduction This Work Is Concerned with the Effect of Barycentric Subdivision on the E
f-VECTORS OF BARYCENTRIC SUBDIVISIONS FRANCESCO BRENTI AND VOLKMAR WELKER Abstract. For a simplicial complex or more generally Boolean cell complex ∆ we study the behavior of the f- and h-vector under barycentric subdivision. We show that if ∆ has a non-negative h-vector then the h-polynomial of its barycentric subdivision has only simple and real zeros. As a consequence this implies a strong version of the Charney-Davis conjecture for spheres that are the subdivision of a Boolean cell complex or the subdivision of the boundary complex of a simple polytope. For a general (d − 1)-dimensional simplicial complex ∆ the h- polynomial of its n-th iterated subdivision shows convergent be- havior. More precisely, we show that among the zeros of this h- polynomial there is one converging to infinity and the other d − 1 converge to a set of d − 1 real numbers which only depends on d. 1. Introduction This work is concerned with the effect of barycentric subdivision on the enumerative structure of a simplicial complex. More precisely, we study the behavior of the f- and h-vector of a simplicial complex, or more generally, Boolean cell complex, under barycentric subdivisions. In our main result we show that if the h-vector of a simplicial complex or Boolean cell complex is non-negative then the h-polynomial (i.e., the generating polynomial of the h-vector) of its barycentric subdivi- sion has only real simple zeros. Moreover, if one applies barycentric subdivision iteratively then there is a limiting behavior of the zeros of the h-polynomial, with one zero going to infinity and the other zeros converging.