Configurations of Lines in Del Pezzo Surfaces with Gosset Polytopes
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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. -
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. -
The Sphere Packing Problem in Dimension 8 Arxiv:1603.04246V2
The sphere packing problem in dimension 8 Maryna S. Viazovska April 5, 2017 8 In this paper we prove that no packing of unit balls in Euclidean space R has density greater than that of the E8-lattice packing. Keywords: Sphere packing, Modular forms, Fourier analysis AMS subject classification: 52C17, 11F03, 11F30 1 Introduction The sphere packing constant measures which portion of d-dimensional Euclidean space d can be covered by non-overlapping unit balls. More precisely, let R be the Euclidean d vector space equipped with distance k · k and Lebesgue measure Vol(·). For x 2 R and d r 2 R>0 we denote by Bd(x; r) the open ball in R with center x and radius r. Let d X ⊂ R be a discrete set of points such that kx − yk ≥ 2 for any distinct x; y 2 X. Then the union [ P = Bd(x; 1) x2X d is a sphere packing. If X is a lattice in R then we say that P is a lattice sphere packing. The finite density of a packing P is defined as Vol(P\ Bd(0; r)) ∆P (r) := ; r > 0: Vol(Bd(0; r)) We define the density of a packing P as the limit superior ∆P := lim sup ∆P (r): r!1 arXiv:1603.04246v2 [math.NT] 4 Apr 2017 The number be want to know is the supremum over all possible packing densities ∆d := sup ∆P ; d P⊂R sphere packing 1 called the sphere packing constant. For which dimensions do we know the exact value of ∆d? Trivially, in dimension 1 we have ∆1 = 1. -
Sequential Evolutionary Operations of Trigonometric Simplex Designs For
SEQUENTIAL EVOLUTIONARY OPERATIONS OF TRIGONOMETRIC SIMPLEX DESIGNS FOR HIGH-DIMENSIONAL UNCONSTRAINED OPTIMIZATION APPLICATIONS Hassan Musafer Under the Supervision of Dr. Ausif Mahmood DISSERTATION SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIRMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOHPY IN COMPUTER SCIENCE AND ENGINEERING THE SCHOOL OF ENGINEERING UNIVERSITY OF BRIDGEPORT CONNECTICUT May, 2020 SEQUENTIAL EVOLUTIONARY OPERATIONS OF TRIGONOMETRIC SIMPLEX DESIGNS FOR HIGH-DIMENSIONAL UNCONSTRAINED OPTIMIZATION APPLICATIONS c Copyright by Hassan Musafer 2020 iii SEQUENTIAL EVOLUTIONARY OPERATIONS OF TRIGONOMETRIC SIMPLEX DESIGNS FOR HIGH-DIMENSIONAL UNCONSTRAINED OPTIMIZATION APPLICATIONS ABSTRACT This dissertation proposes a novel mathematical model for the Amoeba or the Nelder- Mead simplex optimization (NM) algorithm. The proposed Hassan NM (HNM) algorithm allows components of the reflected vertex to adapt to different operations, by breaking down the complex structure of the simplex into multiple triangular simplexes that work sequentially to optimize the individual components of mathematical functions. When the next formed simplex is characterized by different operations, it gives the simplex similar reflections to that of the NM algorithm, but with rotation through an angle determined by the collection of nonisometric features. As a consequence, the generating sequence of triangular simplexes is guaranteed that not only they have different shapes, but also they have different directions, to search the complex landscape of mathematical problems and to perform better performance than the traditional hyperplanes simplex. To test reliability, efficiency, and robustness, the proposed algorithm is examined on three areas of large- scale optimization categories: systems of nonlinear equations, nonlinear least squares, and unconstrained minimization. The experimental results confirmed that the new algorithm delivered better performance than the traditional NM algorithm, represented by a famous Matlab function, known as "fminsearch". -
Sphere Packing, Lattice Packing, and Related Problems
Sphere packing, lattice packing, and related problems Abhinav Kumar Stony Brook April 25, 2018 Sphere packings Definition n A sphere packing in R is a collection of spheres/balls of equal size which do not overlap (except for touching). The density of a sphere packing is the volume fraction of space occupied by the balls. ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ In dimension 1, we can achieve density 1 by laying intervals end to end. In dimension 2, the best possible is by using the hexagonal lattice. [Fejes T´oth1940] Sphere packing problem n Problem: Find a/the densest sphere packing(s) in R . In dimension 2, the best possible is by using the hexagonal lattice. [Fejes T´oth1940] Sphere packing problem n Problem: Find a/the densest sphere packing(s) in R . In dimension 1, we can achieve density 1 by laying intervals end to end. Sphere packing problem n Problem: Find a/the densest sphere packing(s) in R . In dimension 1, we can achieve density 1 by laying intervals end to end. In dimension 2, the best possible is by using the hexagonal lattice. [Fejes T´oth1940] Sphere packing problem II In dimension 3, the best possible way is to stack layers of the solution in 2 dimensions. This is Kepler's conjecture, now a theorem of Hales and collaborators. mmm m mmm m There are infinitely (in fact, uncountably) many ways of doing this! These are the Barlow packings. Face centered cubic packing Image: Greg A L (Wikipedia), CC BY-SA 3.0 license But (until very recently!) no proofs. In very high dimensions (say ≥ 1000) densest packings are likely to be close to disordered. -
Physical Interpretation of the 30 8-Simplexes in the E8 240-Polytope
Physical Interpretation of the 30 8-simplexes in the E8 240-Polytope: Frank Dodd (Tony) Smith, Jr. 2017 - viXra 1702.0058 248-dim Lie Group E8 has 240 Root Vectors arranged on a 7-sphere S7 in 8-dim space. The 12 vertices of a cuboctahedron live on a 2-sphere S2 in 3-dim space. They are also the 4x3 = 12 outer vertices of 4 tetrahedra (3-simplexes) that share one inner vertex at the center of the cuboctahedron. This paper explores how the 240 vertices of the E8 Polytope in 8-dim space are related to the 30x8 = 240 outer vertices (red in figure below) of 30 8-simplexes whose 9th vertex is a shared inner vertex (yellow in figure below) at the center of the E8 Polytope. The 8-simplex has 9 vertices, 36 edges, 84 triangles, 126 tetrahedron cells, 126 4-simplex faces, 84 5-simplex faces, 36 6-simplex faces, 9 7-simplex faces, and 1 8-dim volume The real 4_21 Witting polytope of the E8 lattice in R8 has 240 vertices; 6,720 edges; 60,480 triangular faces; 241,920 tetrahedra; 483,840 4-simplexes; 483,840 5-simplexes 4_00; 138,240 + 69,120 6-simplexes 4_10 and 4_01; and 17,280 = 2,160x8 7-simplexes 4_20 and 2,160 7-cross-polytopes 4_11. The cuboctahedron corresponds by Jitterbug Transformation to the icosahedron. The 20 2-dim faces of an icosahedon in 3-dim space (image from spacesymmetrystructure.wordpress.com) are also the 20 outer faces of 20 not-exactly-regular-in-3-dim tetrahedra (3-simplexes) that share one inner vertex at the center of the icosahedron, but that correspondence does not extend to the case of 8-simplexes in an E8 polytope, whose faces are both 7-simplexes and 7-cross-polytopes, similar to the cubocahedron, but not its Jitterbug-transform icosahedron with only triangle = 2-simplex faces. -
Self-Dual Configurations and Regular Graphs
SELF-DUAL CONFIGURATIONS AND REGULAR GRAPHS H. S. M. COXETER 1. Introduction. A configuration (mci ni) is a set of m points and n lines in a plane, with d of the points on each line and c of the lines through each point; thus cm = dn. Those permutations which pre serve incidences form a group, "the group of the configuration." If m — n, and consequently c = d, the group may include not only sym metries which permute the points among themselves but also reci procities which interchange points and lines in accordance with the principle of duality. The configuration is then "self-dual," and its symbol («<*, n<j) is conveniently abbreviated to na. We shall use the same symbol for the analogous concept of a configuration in three dimensions, consisting of n points lying by d's in n planes, d through each point. With any configuration we can associate a diagram called the Menger graph [13, p. 28],x in which the points are represented by dots or "nodes," two of which are joined by an arc or "branch" when ever the corresponding two points are on a line of the configuration. Unfortunately, however, it often happens that two different con figurations have the same Menger graph. The present address is concerned with another kind of diagram, which represents the con figuration uniquely. In this Levi graph [32, p. 5], we represent the points and lines (or planes) of the configuration by dots of two colors, say "red nodes" and "blue nodes," with the rule that two nodes differently colored are joined whenever the corresponding elements of the configuration are incident. -
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 . -
Arxiv:2010.10200V3 [Math.GT] 1 Mar 2021 We Deduce in Particular the Following
HYPERBOLIC MANIFOLDS THAT FIBER ALGEBRAICALLY UP TO DIMENSION 8 GIOVANNI ITALIANO, BRUNO MARTELLI, AND MATTEO MIGLIORINI Abstract. We construct some cusped finite-volume hyperbolic n-manifolds Mn that fiber algebraically in all the dimensions 5 ≤ n ≤ 8. That is, there is a surjective homomorphism π1(Mn) ! Z with finitely generated kernel. The kernel is also finitely presented in the dimensions n = 7; 8, and this leads to the first examples of hyperbolic n-manifolds Mfn whose fundamental group is finitely presented but not of finite type. These n-manifolds Mfn have infinitely many cusps of maximal rank and hence infinite Betti number bn−1. They cover the finite-volume manifold Mn. We obtain these examples by assigning some appropriate colours and states to a family of right-angled hyperbolic polytopes P5;:::;P8, and then applying some arguments of Jankiewicz { Norin { Wise [15] and Bestvina { Brady [6]. We exploit in an essential way the remarkable properties of the Gosset polytopes dual to Pn, and the algebra of integral octonions for the crucial dimensions n = 7; 8. Introduction We prove here the following theorem. Every hyperbolic manifold in this paper is tacitly assumed to be connected, complete, and orientable. Theorem 1. In every dimension 5 ≤ n ≤ 8 there are a finite volume hyperbolic 1 n-manifold Mn and a map f : Mn ! S such that f∗ : π1(Mn) ! Z is surjective n with finitely generated kernel. The cover Mfn = H =ker f∗ has infinitely many cusps of maximal rank. When n = 7; 8 the kernel is also finitely presented. arXiv:2010.10200v3 [math.GT] 1 Mar 2021 We deduce in particular the following. -
Optimality and Uniqueness of the Leech Lattice Among Lattices
ANNALS OF MATHEMATICS Optimality and uniqueness of the Leech lattice among lattices By Henry Cohn and Abhinav Kumar SECOND SERIES, VOL. 170, NO. 3 November, 2009 anmaah Annals of Mathematics, 170 (2009), 1003–1050 Optimality and uniqueness of the Leech lattice among lattices By HENRY COHN and ABHINAV KUMAR Dedicated to Oded Schramm (10 December 1961 – 1 September 2008) Abstract We prove that the Leech lattice is the unique densest lattice in R24. The proof combines human reasoning with computer verification of the properties of certain explicit polynomials. We furthermore prove that no sphere packing in R24 can 30 exceed the Leech lattice’s density by a factor of more than 1 1:65 10 , and we 8 C give a new proof that E8 is the unique densest lattice in R . 1. Introduction It is a long-standing open problem in geometry and number theory to find the densest lattice in Rn. Recall that a lattice ƒ Rn is a discrete subgroup of rank n; a minimal vector in ƒ is a nonzero vector of minimal length. Let ƒ vol.Rn=ƒ/ j j D denote the covolume of ƒ, i.e., the volume of a fundamental parallelotope or the absolute value of the determinant of a basis of ƒ. If r is the minimal vector length of ƒ, then spheres of radius r=2 centered at the points of ƒ do not overlap except tangentially. This construction yields a sphere packing of density n=2 r Án 1 ; .n=2/Š 2 ƒ j j since the volume of a unit ball in Rn is n=2=.n=2/Š, where for odd n we define .n=2/Š .n=2 1/.