Math 396. Topology on Projective Space Let V Be a Finite-Dimensional Vector Space Over R with Dimension N + 1 ≥ 2. Let P(V
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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. -
Projective Geometry: a Short Introduction
Projective Geometry: A Short Introduction Lecture Notes Edmond Boyer Master MOSIG Introduction to Projective Geometry Contents 1 Introduction 2 1.1 Objective . .2 1.2 Historical Background . .3 1.3 Bibliography . .4 2 Projective Spaces 5 2.1 Definitions . .5 2.2 Properties . .8 2.3 The hyperplane at infinity . 12 3 The projective line 13 3.1 Introduction . 13 3.2 Projective transformation of P1 ................... 14 3.3 The cross-ratio . 14 4 The projective plane 17 4.1 Points and lines . 17 4.2 Line at infinity . 18 4.3 Homographies . 19 4.4 Conics . 20 4.5 Affine transformations . 22 4.6 Euclidean transformations . 22 4.7 Particular transformations . 24 4.8 Transformation hierarchy . 25 Grenoble Universities 1 Master MOSIG Introduction to Projective Geometry Chapter 1 Introduction 1.1 Objective The objective of this course is to give basic notions and intuitions on projective geometry. The interest of projective geometry arises in several visual comput- ing domains, in particular computer vision modelling and computer graphics. It provides a mathematical formalism to describe the geometry of cameras and the associated transformations, hence enabling the design of computational ap- proaches that manipulates 2D projections of 3D objects. In that respect, a fundamental aspect is the fact that objects at infinity can be represented and manipulated with projective geometry and this in contrast to the Euclidean geometry. This allows perspective deformations to be represented as projective transformations. Figure 1.1: Example of perspective deformation or 2D projective transforma- tion. Another argument is that Euclidean geometry is sometimes difficult to use in algorithms, with particular cases arising from non-generic situations (e.g. -
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. -
Degrees of Freedom in Quadratic Goodness of Fit
Submitted to the Annals of Statistics DEGREES OF FREEDOM IN QUADRATIC GOODNESS OF FIT By Bruce G. Lindsay∗, Marianthi Markatouy and Surajit Ray Pennsylvania State University, Columbia University, Boston University We study the effect of degrees of freedom on the level and power of quadratic distance based tests. The concept of an eigendepth index is introduced and discussed in the context of selecting the optimal de- grees of freedom, where optimality refers to high power. We introduce the class of diffusion kernels by the properties we seek these kernels to have and give a method for constructing them by exponentiating the rate matrix of a Markov chain. Product kernels and their spectral decomposition are discussed and shown useful for high dimensional data problems. 1. Introduction. Lindsay et al. (2008) developed a general theory for good- ness of fit testing based on quadratic distances. This class of tests is enormous, encompassing many of the tests found in the literature. It includes tests based on characteristic functions, density estimation, and the chi-squared tests, as well as providing quadratic approximations to many other tests, such as those based on likelihood ratios. The flexibility of the methodology is particularly important for enabling statisticians to readily construct tests for model fit in higher dimensions and in more complex data. ∗Supported by NSF grant DMS-04-05637 ySupported by NSF grant DMS-05-04957 AMS 2000 subject classifications: Primary 62F99, 62F03; secondary 62H15, 62F05 Keywords and phrases: Degrees of freedom, eigendepth, high dimensional goodness of fit, Markov diffusion kernels, quadratic distance, spectral decomposition in high dimensions 1 2 LINDSAY ET AL. -
Bornologically Isomorphic Representations of Tensor Distributions
Bornologically isomorphic representations of distributions on manifolds E. Nigsch Thursday 15th November, 2018 Abstract Distributional tensor fields can be regarded as multilinear mappings with distributional values or as (classical) tensor fields with distribu- tional coefficients. We show that the corresponding isomorphisms hold also in the bornological setting. 1 Introduction ′ ′ ′r s ′ Let D (M) := Γc(M, Vol(M)) and Ds (M) := Γc(M, Tr(M) ⊗ Vol(M)) be the strong duals of the space of compactly supported sections of the volume s bundle Vol(M) and of its tensor product with the tensor bundle Tr(M) over a manifold; these are the spaces of scalar and tensor distributions on M as defined in [?, ?]. A property of the space of tensor distributions which is fundamental in distributional geometry is given by the C∞(M)-module isomorphisms ′r ∼ s ′ ∼ r ′ Ds (M) = LC∞(M)(Tr (M), D (M)) = Ts (M) ⊗C∞(M) D (M) (1) (cf. [?, Theorem 3.1.12 and Corollary 3.1.15]) where C∞(M) is the space of smooth functions on M. In[?] a space of Colombeau-type nonlinear generalized tensor fields was constructed. This involved handling smooth functions (in the sense of convenient calculus as developed in [?]) in par- arXiv:1105.1642v1 [math.FA] 9 May 2011 ∞ r ′ ticular on the C (M)-module tensor products Ts (M) ⊗C∞(M) D (M) and Γ(E) ⊗C∞(M) Γ(F ), where Γ(E) denotes the space of smooth sections of a vector bundle E over M. In[?], however, only minor attention was paid to questions of topology on these tensor products. -
Arxiv:1910.10745V1 [Cond-Mat.Str-El] 23 Oct 2019 2.2 Symmetry-Protected Time Crystals
A Brief History of Time Crystals Vedika Khemania,b,∗, Roderich Moessnerc, S. L. Sondhid aDepartment of Physics, Harvard University, Cambridge, Massachusetts 02138, USA bDepartment of Physics, Stanford University, Stanford, California 94305, USA cMax-Planck-Institut f¨urPhysik komplexer Systeme, 01187 Dresden, Germany dDepartment of Physics, Princeton University, Princeton, New Jersey 08544, USA Abstract The idea of breaking time-translation symmetry has fascinated humanity at least since ancient proposals of the per- petuum mobile. Unlike the breaking of other symmetries, such as spatial translation in a crystal or spin rotation in a magnet, time translation symmetry breaking (TTSB) has been tantalisingly elusive. We review this history up to recent developments which have shown that discrete TTSB does takes place in periodically driven (Floquet) systems in the presence of many-body localization (MBL). Such Floquet time-crystals represent a new paradigm in quantum statistical mechanics — that of an intrinsically out-of-equilibrium many-body phase of matter with no equilibrium counterpart. We include a compendium of the necessary background on the statistical mechanics of phase structure in many- body systems, before specializing to a detailed discussion of the nature, and diagnostics, of TTSB. In particular, we provide precise definitions that formalize the notion of a time-crystal as a stable, macroscopic, conservative clock — explaining both the need for a many-body system in the infinite volume limit, and for a lack of net energy absorption or dissipation. Our discussion emphasizes that TTSB in a time-crystal is accompanied by the breaking of a spatial symmetry — so that time-crystals exhibit a novel form of spatiotemporal order. -
THE DIMENSION of a VECTOR SPACE 1. Introduction This Handout
THE DIMENSION OF A VECTOR SPACE KEITH CONRAD 1. Introduction This handout is a supplementary discussion leading up to the definition of dimension of a vector space and some of its properties. We start by defining the span of a finite set of vectors and linear independence of a finite set of vectors, which are combined to define the all-important concept of a basis. Definition 1.1. Let V be a vector space over a field F . For any finite subset fv1; : : : ; vng of V , its span is the set of all of its linear combinations: Span(v1; : : : ; vn) = fc1v1 + ··· + cnvn : ci 2 F g: Example 1.2. In F 3, Span((1; 0; 0); (0; 1; 0)) is the xy-plane in F 3. Example 1.3. If v is a single vector in V then Span(v) = fcv : c 2 F g = F v is the set of scalar multiples of v, which for nonzero v should be thought of geometrically as a line (through the origin, since it includes 0 · v = 0). Since sums of linear combinations are linear combinations and the scalar multiple of a linear combination is a linear combination, Span(v1; : : : ; vn) is a subspace of V . It may not be all of V , of course. Definition 1.4. If fv1; : : : ; vng satisfies Span(fv1; : : : ; vng) = V , that is, if every vector in V is a linear combination from fv1; : : : ; vng, then we say this set spans V or it is a spanning set for V . Example 1.5. In F 2, the set f(1; 0); (0; 1); (1; 1)g is a spanning set of F 2. -
Solution: the First Element of the Dual Basis Is the Linear Function Α
Homework assignment 7 pp. 105 3 Exercise 2. Let B = f®1; ®2; ®3g be the basis for C defined by ®1 = (1; 0; ¡1) ®2 = (1; 1; 1) ®3 = (2; 2; 0): Find the dual basis of B. Solution: ¤ The first element of the dual basis is the linear function ®1 such ¤ ¤ ¤ that ®1(®1) = 1; ®1(®2) = 0 and ®1(®3) = 0. To describe such a function more explicitly we need to find its values on the standard basis vectors e1, e2 and e3. To do this express e1; e2; e3 through ®1; ®2; ®3 (refer to the solution of Exercise 1 pp. 54-55 from Homework 6). For each i = 1; 2; 3 you will find the numbers ai; bi; ci such that e1 = ai®1 + bi®2 + ci®3 (i.e. the coordinates of ei relative to the ¤ ¤ ¤ basis ®1; ®2; ®3). Then by linearity of ®1 we get that ®1(ei) = ai. Then ®2(ei) = bi, ¤ and ®3(ei) = ci. This is the answer. It can also be reformulated as follows. If P is the transition matrix from the standard basis e1; e2; e3 to ®1; ®2; ®3, i.e. ¡1 t (®1; ®2; ®3) = (e1; e2; e3)P , then (P ) is the transition matrix from the dual basis ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¡1 t e1; e2; e3 to the dual basis ®1; ®2; a3, i.e. (®1; ®2; a3) = (e1; e2; e3)(P ) . Note that this problem is basically the change of coordinates problem: e.g. ¤ 3 the value of ®1 on the vector v 2 C is the first coordinate of v relative to the basis ®1; ®2; ®3. -
Slices, Slabs, and Sections of the Unit Hypercube
Slices, Slabs, and Sections of the Unit Hypercube Jean-Luc Marichal Michael J. Mossinghoff Institute of Mathematics Department of Mathematics University of Luxembourg Davidson College 162A, avenue de la Fa¨ıencerie Davidson, NC 28035-6996 L-1511 Luxembourg USA Luxembourg [email protected] [email protected] Submitted: July 27, 2006; Revised: August 6, 2007; Accepted: January 21, 2008; Published: January 29, 2008 Abstract Using combinatorial methods, we derive several formulas for the volume of convex bodies obtained by intersecting a unit hypercube with a halfspace, or with a hyperplane of codimension 1, or with a flat defined by two parallel hyperplanes. We also describe some of the history of these problems, dating to P´olya’s Ph.D. thesis, and we discuss several applications of these formulas. Subject Class: Primary: 52A38, 52B11; Secondary: 05A19, 60D05. Keywords: Cube slicing, hyperplane section, signed decomposition, volume, Eulerian numbers. 1 Introduction In this note we study the volumes of portions of n-dimensional cubes determined by hyper- planes. More precisely, we study slices created by intersecting a hypercube with a halfspace, slabs formed as the portion of a hypercube lying between two parallel hyperplanes, and sections obtained by intersecting a hypercube with a hyperplane. These objects occur natu- rally in several fields, including probability, number theory, geometry, physics, and analysis. In this paper we describe an elementary combinatorial method for calculating volumes of arbitrary slices, slabs, and sections of a unit cube. We also describe some applications that tie these geometric results to problems in analysis and combinatorics. Some of the results we obtain here have in fact appeared earlier in other contexts. -
Vectors and Dual Vectors
Vectors By now you have a pretty good experience with \vectors". Usually, a vector is defined as a quantity that has a direction and a magnitude, such as a position vector, velocity vector, acceleration vector, etc. However, the notion of a vector has a considerably wider realm of applicability than these examples might suggest. The set of all real numbers forms a vector space, as does the set of all complex numbers. The set of functions on a set (e.g., functions of one variable, f(x)) form a vector space. Solutions of linear homogeneous equations form a vector space. We begin by giving the abstract rules for forming a space of vectors, also known as a vector space. A vector space V is a set equipped with an operation of \addition" and an additive identity. The elements of the set are called vectors, which we shall denote as ~u, ~v, ~w, etc. For now, you can think of them as position vectors in order to keep yourself sane. Addition, is an operation in which two vectors, say ~u and ~v, can be combined to make another vector, say, ~w. We denote this operation by the symbol \+": ~u + ~v = ~w: (1) Do not be fooled by this simple notation. The \addition" of vectors may be quite a different operation than ordinary arithmetic addition. For example, if we view position vectors in the x-y plane as \arrows" drawn from the origin, the addition of vectors is defined by the parallelogram rule. Clearly this rule is quite different than ordinary \addition". -
Duality, Part 1: Dual Bases and Dual Maps Notation
Duality, part 1: Dual Bases and Dual Maps Notation F denotes either R or C. V and W denote vector spaces over F. Define ': R3 ! R by '(x; y; z) = 4x − 5y + 2z. Then ' is a linear functional on R3. n n Fix (b1;:::; bn) 2 C . Define ': C ! C by '(z1;:::; zn) = b1z1 + ··· + bnzn: Then ' is a linear functional on Cn. Define ': P(R) ! R by '(p) = 3p00(5) + 7p(4). Then ' is a linear functional on P(R). R 1 Define ': P(R) ! R by '(p) = 0 p(x) dx. Then ' is a linear functional on P(R). Examples: Linear Functionals Definition: linear functional A linear functional on V is a linear map from V to F. In other words, a linear functional is an element of L(V; F). n n Fix (b1;:::; bn) 2 C . Define ': C ! C by '(z1;:::; zn) = b1z1 + ··· + bnzn: Then ' is a linear functional on Cn. Define ': P(R) ! R by '(p) = 3p00(5) + 7p(4). Then ' is a linear functional on P(R). R 1 Define ': P(R) ! R by '(p) = 0 p(x) dx. Then ' is a linear functional on P(R). Linear Functionals Definition: linear functional A linear functional on V is a linear map from V to F. In other words, a linear functional is an element of L(V; F). Examples: Define ': R3 ! R by '(x; y; z) = 4x − 5y + 2z. Then ' is a linear functional on R3. Define ': P(R) ! R by '(p) = 3p00(5) + 7p(4). Then ' is a linear functional on P(R). R 1 Define ': P(R) ! R by '(p) = 0 p(x) dx. -
A Some Basic Rules of Tensor Calculus
A Some Basic Rules of Tensor Calculus The tensor calculus is a powerful tool for the description of the fundamentals in con- tinuum mechanics and the derivation of the governing equations for applied prob- lems. In general, there are two possibilities for the representation of the tensors and the tensorial equations: – the direct (symbolic) notation and – the index (component) notation The direct notation operates with scalars, vectors and tensors as physical objects defined in the three dimensional space. A vector (first rank tensor) a is considered as a directed line segment rather than a triple of numbers (coordinates). A second rank tensor A is any finite sum of ordered vector pairs A = a b + ... +c d. The scalars, vectors and tensors are handled as invariant (independent⊗ from the choice⊗ of the coordinate system) objects. This is the reason for the use of the direct notation in the modern literature of mechanics and rheology, e.g. [29, 32, 49, 123, 131, 199, 246, 313, 334] among others. The index notation deals with components or coordinates of vectors and tensors. For a selected basis, e.g. gi, i = 1, 2, 3 one can write a = aig , A = aibj + ... + cidj g g i i ⊗ j Here the Einstein’s summation convention is used: in one expression the twice re- peated indices are summed up from 1 to 3, e.g. 3 3 k k ik ik a gk ∑ a gk, A bk ∑ A bk ≡ k=1 ≡ k=1 In the above examples k is a so-called dummy index. Within the index notation the basic operations with tensors are defined with respect to their coordinates, e.