DOCSLIB.ORG

3 Finitely Generated Gyrovector Subspaces Let (G, ⊕, ⊗) Be a Gyrovector Space

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
3 Finitely Generated Gyrovector Subspaces Let (G, ⊕, ⊗) Be a Gyrovector Space Accepted Manuscript Finitely generated gyrovector subspaces and orthogonal gyrodecomposition in the Möbius gyrovector space Toshikazu Abe, Keiichi Watanabe PII: S0022-247X(16)30729-6 DOI: http://dx.doi.org/10.1016/j.jmaa.2016.11.039 Reference: YJMAA 20892 To appear in: Journal of Mathematical Analysis and Applications Received date: 3 October 2016 Please cite this article in press as: T. Abe, K. Watanabe, Finitely generated gyrovector subspaces and orthogonal gyrodecomposition in the Möbius gyrovector space, J. Math. Anal. Appl. (2017), http://dx.doi.org/10.1016/j.jmaa.2016.11.039 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Finitely generated gyrovector subspaces and orthogonal gyrodecomposition in the M¨obius gyrovector space Toshikazu Abe and Keiichi Watanabe Toshikazu Abe: College of Engineering, Ibaraki University, Hitachi, Ibaraki 316- 8511, Japan E-mail address: [email protected] Keiichi Watanabe: Department of Mathematics, Faculty of Science, Niigata Uni- versity, Niigata 950–2181, Japan E-mail address: [email protected] Key words and phrases. M¨obius gyrogroup, M¨obius gyrovector space Mathematical Subject Classification (2010). Primary 51M10; Secondary 20N05, 46C99, 51P05. Abstract. We will show that any finitely generated gyrovector subspace in the M¨obius gyrovector space coincides with the intersection of the vector subspace generated by the same generators and the M¨obius ball. As an application, we will present a notion of orthogonal gyrodecomposition and clarify the relation to the orthogonal decomposition. 1 Introduction A gyrogroup (resp. gyrocommutative gyrogroup) is one of the most natural generalization of a group (resp. commutative group). Ungar has intensively stud- ied gyrogroups since 1988. The first known gyrogroup was the ball of Euclidean space R3 endowed with Einstein’s velocity addition associated with the special theory of relativity. Another example of a gyrogroup is the complex unit disc D = {a ∈ C : |a| < 1} endowed with the M¨obius addition. Ungar extended the M¨obius addition in the complex disc to the ball of an arbitrary real inner product space, and observed that the ball endowed with the M¨obius addition turns out to be a gyrocommutative gyrogroup. Some gyrocommutative gyrogroups admit scalar multiplication, turning them- selves into gyrovector spaces. The latter, in turn, are analogous to vector spaces just as gyrogroups are analogous to groups. Indeed, gyrovector spaces provide 1 the setting for hyperbolic geometry just as vector spaces provide setting for Eu- clidean geometry. In particular, M¨obiusgyrovector spaces form the setting for the Poincar´eball model of hyperbolic geometry while, similarly, Einstein gyrovector spaces form the setting for the Beltrami-Klein ball model. The readers may consult with [U1] and references therein for general informations of gyrovector spaces. In [AH], the first author and Hatori introduced a notion of generalized gyrovector spaces (GGVs in short) in a way analogous to the generalization of inner prod- uct spaces into normed spaces. Accordingly, the notion of a GGV is a common generalization of the notion of a real normed space and the notion of a gyrovector space. Then, they showed that the gyrocommutative gyrogroup of all positive in- vertible elements in a unital C∗-algebra is indeed a GGV without using the theory of K-loops, and presented a Mazur-Ulam theorem for GGVs as well. It still continues to speculate more satisfactory axioms for generalizations of gyrovector spaces. Since gyro-operations are not commutative, associative or dis- tributive in general, lots of elementary problems remain unsolved. Very recently, the first author raised the following question in a conference (cf. [A]). Question 1.1. Let (G, ⊕, ⊗) be a gyrovector space, or any kind of generalization of gyrovector space and a1, a2 ∈ G. Can we have {r1 ⊗a1 ⊕ r2 ⊗a2 ; r1,r2 ∈ R} = {λ2 ⊗a2 ⊕ λ1 ⊗a1 ; λ1,λ2 ∈ R} ? r⊗(r1 ⊗a1 ⊕ r2 ⊗a2) ∈{λ1 ⊗a1 ⊕ λ2 ⊗a2 ; λ1,λ2 ∈ R} ? Such problems seem to be quite fundamental and important for developing the theory of generalization of gyrovector spaces, since one of the virtue of gyrovector spaces is that they have the nature which are fully analogous to vector spaces. In this paper we will consider this problem and its natural extension in the M¨obius gyrovector space (Vs, ⊕ , ⊗ ). M M 2 Gyrovector subspaces generated by two ele- ments Assume s = 1 without loss of generality. The M¨obius addition and the M¨obius scalar multiplication are given by the equations (1 + 2a·b + ||b||2) a +(1−||a||2) b a ⊕ b = M 1+2a·b + ||a||2||b||2 a r ⊗ a =tanh r tanh−1 ||a|| (if a = 0),r⊗ 0 = 0 M ||a|| M 2 for any a, b ∈ V1,r∈ R. We simply denote ⊕ , ⊗ by ⊕, ⊗, respectively. If ⊕ and M M ⊗ appear in a formula simultaneously, we always give priority to the operation ⊗, that is, r1 ⊗a1 ⊕ r2 ⊗a2 =(r1 ⊗a1) ⊕ (r2 ⊗a2), and the parentheses will be omitted in such cases. Note that, in general, gyroad- dition does not distribute with (gyro) scalar multiplication ([U1, (6.5)]): r⊗(a ⊕ b) = r⊗a ⊕ r⊗b. In this section, we will give an affirmative answer to Question 1.1 for the M¨obius gyrovector space via the equation {r1 ⊗a1 ⊕ r2 ⊗a2 ; r1,r2 ∈ R} = {λ1a1 + λ2a2; λ1,λ2 ∈ R}∩V1 for a1, a2 ∈ V1. Indeed, one of the inclusion (⊂) in the above equation is obvious, since r1 ⊗a1 ⊕ r2 ⊗a2 is a linear combination of a1, a2 by the definitions of ⊕, ⊗, and it belongs to V1 which is closed under these operations. The following theorem asserts that the other inclusion (⊃) holds as well. Theorem 2.1. Let (V1, ⊕, ⊗) be the Mo¨bius gyrovector space and 0 = a1, a2 ∈ V1. a1 a2 Put α = · . Suppose that 0 = t1,t2 ∈ R satisfy the condition ||a1|| ||a2|| a1 a2 t1 + t2 < 1. ||a1|| ||a2|| (I) If 2αt2 + t1 =0 , we put 2 2 2 2 2 2 t1 +2αt1t2 + t2 +1− (t1 +2αt1t2 + t2 +1) − 8αt1t2 − 4t1 c1 = 2(2αt2 + t1) 2 2 2 2 2 2 t1 +2αt1t2 + t2 − 1+ (t1 +2αt1t2 + t2 +1) − 8αt1t2 − 4t1 c2 = . 2t2 (II) If 2αt2 + t1 =0,weput t c 1 1 = 2 t2 +1 c2 = t1. Then we have 0 < |c1|, |c2| < 1 and a1 a2 t1 + t2 = r1 ⊗a1 ⊕ r2 ⊗a2, ||a1|| ||a2|| where −1 −1 tanh c1 tanh c2 r1 = −1 and r2 = −1 . tanh ||a1|| tanh ||a2|| 3 In order to prove this theorem, we should express the element r1 ⊗a1 ⊕ r2 ⊗a2 as a linear combination of a1, a2 by the definition of operations ⊕, ⊗: −1 a1 −1 a2 r1 ⊗a1 ⊕ r2 ⊗a2 = tanh(r1 tanh ||a1||) ⊕ tanh(r2 tanh ||a2||) ||a1|| ||a2|| a1 a2 = c1 ⊕ c2 ||a1|| ||a2|| 2 a1 2 a2 (1+2c1c2α + c2 ) c1 +(1− c1 ) c2 ||a1|| ||a2|| . = 2 2 1+2c1c2α + c1 c2 Thus the problem is that, for t1,t2 satisfying the assumption of Theorem 2.1, whether there exists a pair c1,c2 which satisfies the conditions |c1|, |c2| < 1and 2 a1 2 a2 a a (1 + 2c1c2α + c2 ) c1 +(1− c1 ) c2 t 1 t 2 ||a1|| ||a2|| . 1 + 2 = 2 2 ||a1|| ||a2|| 1+2c1c2α + c1 c2 If this is the case, then we would have 2 2 2 1+2c1c2α + c1 c2 t1 = 1+2c1c2α + c2 c1 (1) 2 2 2 1+2c1c2α + c1 c2 t2 =(1− c1 )c2. (2) The equation (2) yields 2 2 1 2 1+2c1c2α + c1 c2 = (1 − c1 )c2, (3) t2 from which we have t1 2 2 (1 − c1 )c2 = 1+2c1c2α + c2 c1 (4) t2 t1 1 by substituting (3) to (1). Putting β = ,γ= , then the equations (3) and (4) t2 t2 can be rewritten to a system of equations: 2 2 2 c1 c2 +(γc1 +2αc1 − γ)c2 +1=0 2 2 c1c2 + ((2α + β)c1 − β)c2 + c1 =0. Therefore, we should consider the system of equations (5) and (6) of Theorem 2.4 in the sequel. We need two inequalities which are satisfied by the coefficients of the system of equations. Lemma 2.2. Suppose that 0 = t1,t2 ∈ R satisfy the condition a1 a2 t1 + t2 < 1. ||a1|| ||a2|| 4 If we put a1 a2 t1 1 α = · ,β= ,γ= ||a1|| ||a2|| t2 t2 as before, then we have the inequality 1+β(2α + β) <γ2. Proof. It is easy to see that t t β α β 1 α 1 1 t 2 αt t t 2 1+ (2 + )=1+ 2 + = 2 2 +2 1 2 + 1 t2 t2 t2 a a 2 1 · t 1 t 2 < 1 γ2. = 2 1 + 2 2 = t2 ||a1|| ||a2|| t2 Lemma 2.3. Suppose that α, β, γ ∈ R, −1 ≤ α ≤ 1 and 1+β(2α + β) <γ2. Then we have the inequality 4(α + β)2γ2 < (1 + β(2α + β)+γ2)2. Proof. Note that 1 + β(2α + β)=(α + β)2 +1− α2 ≥ 0. It follows from Arithmetic-Geometric mean inequality and its equality condition that {1+β(2α + β)} + γ2 {1+β(2α + β)}·γ2 < . 2 Thus we have 4(α + β)2γ2 ≤ 4{1+β(2α + β)}γ2 < (1 + β(2α + β)+γ2)2 as desired. Theorem 2.4. Consider the following system of equations for real numbers: ⎧ ⎨x2y2 +(γx2 +2αx − γ)y +1=0 (5) ⎩ xy2 + ((2α + β)x2 − β)y + x =0 (6) Suppose that −1 ≤ α ≤ 1,β=0 and 1+β(2α + β) <γ2.
Load more

Recommended publications
  • Generalized Gyrovector Spaces and the Positive Cone of a Unital C
    Generalized gyrovector spaces and the positive cone of Contact Information: ∗ Faculty of Engineering a unital C -algebra Niigata University 8050 2no-cho ikarashi Nishi-ku Niigata-shi, Niigata- ken, Japan Toshikazu Abe Phone: +81 (25) 262 7469 Niigata University, Faculty of Engineering Email: [email protected] Abstract Definition 1. A magma (G; ⊕) is called a gyrogroup if there exists an Note that a real normed space (V; +; ×) is a GGV with ϕ = idV. Results The concept of generalized gyrovecor space (GGV, for short) is a com- element e such that the binary operation ⊕ satisfies the following (G1) On a GGV, we can define the gyromidpoint. It is a algebraic mid- mon generalization of the concept of real normed spaces and of the gyrovector spaces. The addition of a GGV is not necessarily a commutative group but is a to (G5). point on the GGV. On a real normed space, the gyromidpoint is the A Mazur-Ulam Theorem for GGV’s gyrocommutative gyrogroup. A typical example of GGV’s is the positive cone 8 2 ⊕ arithmetric mean. ∗ (G1) a G, e a = a. of a unital C -algebra. (G; ⊕; ⊗) p(a; b) We have a Mazur-Ulam type theorem for GGV’s as follows. This is a (G2) 8a 2 G, 9 ⊖ a s.t. ⊖a ⊕ a = e: Definition 4. Let be a GGV. The gyromidpoint of a; b 2 (G; ⊕; ⊗) is defined as p(a; b) = 1 ⊗ (a ⊞ b), where ⊞ is the generalization of the celebrated Mazur-Ulam theorem. A proof of this 8 2 9 2 ⊕ ⊕ ⊕ ⊕ 2 (G3) a; b; c G, ! gyr[a; b]c G s.t.
    [Show full text]
  • From the Lorentz Transformation Group in Pseudo-Euclidean Spaces to Bi-Gyrogroups
    Mathematics Interdisciplinary Research 1 (2016) 229 − 272 From the Lorentz Transformation Group in Pseudo-Euclidean Spaces to Bi-Gyrogroups Abraham A. Ungar? Abstract The Lorentz transformation of order (m = 1; n), n 2 N, is the well-known Lorentz transformation of special relativity theory. It is a transformation of m=1;n time-space coordinates of the pseudo-Euclidean space R of one time di- mension and n space dimensions (n = 3 in physical applications). A Lorentz transformation without rotations is called a boost. Commonly, the special relativistic boost is parametrized by a relativistically admissible velocity pa- n n rameter v, v 2 Rc , whose domain is the c-ball Rc of all relativistically n n admissible velocities, Rc = fv 2 R : kvk < cg, where the ambient space n R is the Euclidean n-space, and c > 0 is an arbitrarily fixed positive con- stant that represents the vacuum speed of light. The study of the Lorentz transformation composition law in terms of parameter composition reveals that the group structure of the Lorentz transformation of order (m = 1; n) induces a gyrogroup and a gyrovector space structure that regulate the pa- n rameter space Rc . The gyrogroup and gyrovector space structure of the ball n Rc , in turn, form the algebraic setting for the Beltrami-Klein ball model of n hyperbolic geometry, which underlies the ball Rc . The aim of this article is to extend the study of the Lorentz transformation of order (m; n) from m = 1 and n ≥ 1 to all m; n 2 N, obtaining algebraic structures called a bi-gyrogroup and a bi-gyrovector space.
    [Show full text]
  • The First Sharp Gyrotriangle Inequality in Möbius Gyrovector Space (D,⊕,⊗)
    Forum Geometricorum b Volume 17 (2017) 439–447. b b FORUM GEOM ISSN 1534-1178 The First Sharp Gyrotriangle Inequality in Mobius¨ Gyrovector Space (D, ⊕, ⊗) Oguzhan˘ Demirel Abstract. In this paper we present two different inequalities (with their reverse inequalities) written in a common type by using classical vector addition “+” and classical multiplication “·” in Euclidean geometry and by using Mobius¨ addi- tion “⊕” and Mobius¨ scalar multiplication “⊗” in Mobius¨ gyrovector space (D, ⊕, ⊗). It is known that this Mobius¨ gyrovector space form the algebraic setting for the Poincare´ disc model of hyperbolic geometry, just as vector spaces form the algebraic setting for the standard model of Euclidean geometry. 1. Introduction There are many fundamental inequalities in mathematics and one of them is the famous “Triangle Inequality”. In [7], Kato, Saito and Tamura presented the following “Sharp Triangle Inequality”in a Banach Space as follows: Theorem 1. For all nonzero elements x1,x2, ··· ,xn in a Banach space X, n n xj xj + n − min kxj k kx k 1≤j≤n j=1 j=1 j X X n ≤ kxjk j=1 X n n xj ≤ xj + n − max kxj k kx k 1≤j≤n j=1 j=1 j X X holds. Recently Mitani, Kato, Saito and Tamura [8] presented a new type Sharp Trian- gle Inequality as follows: Publication Date: December 5, 2017. Communicating Editor: Paul Yiu. The author would like to thank Professor Abraham Albert Ungar for his helpful and valuable comments. 440 O. Demirel Theorem 2. For all nonzero elements x1,x2, ··· ,xn in a Banach space X, n ≥ 2 —small n n k ∗ xj ∗ ∗ k xjk + k − (kx k − kx k) kx∗k k k+1 j=1 k=2 j=1 j X X X n ≤ kxjk j X=1 n n n ∗ xj ∗ ∗ ≤k xjk− k − (kx k − kx k) kx∗k n−k n−(k−1) j=1 k=2 j=n−(k−1) j X X X ∗ ∗ ∗ ∗ where x1,x2, ··· ,xn are the rearrangement of x1,x2, ··· ,xn satisfying kx1k ≥ ∗ ∗ ∗ ∗ kx2k≥···≥kxnk and x0 = xn+1 = 0.
    [Show full text]
  • Ordered Gyrovector Spaces
    S S symmetry Article Ordered Gyrovector Spaces Sejong Kim Department of Mathematics, Chungbuk National University, Cheongju 28644, Korea; [email protected] Received: 5 June 2020; Accepted: 18 June 2020; Published: 22 June 2020 Abstract: The well-known construction scheme to define a partial order on a vector space is to use a proper convex cone. Applying this idea to the gyrovector space we construct the partial order, called a gyro-order. We also give several inequalities of gyrolines and cogyrolines in terms of the gyro-order. Keywords: gyrogroup; gyrovector space; gyroline; cogyroline; partial order 1. Introduction Since finding appropriate algebraic coordinatizations in geometric settings has been attempted first by R. Descartes and P. Fermat, the study of more general algebraic structures have been actively researched. Gyrogroups and gyrovector spaces with the non-associative binary operation ⊕, introduced by A. Ungar [1], are suitable generalization of groups and vector spaces. In particular, gyrovector spaces algebraically regulate many typical examples of analytic hyperbolic geometry. For instance, the Einstein gyrovector space, Möbius gyrovector space, and Proper Velocity (PV, in short) gyrovector space provide algebraic tools to study the Beltrami–Klein, Poincaré ball models, and PV space model of hyperbolic geometry, respectively. As many recent results shows that the theory of gyrogroups and gyrovector spaces can be applied to various areas such as the loop theory, the theory of special relativity, and quantum information, it has been widely studied [2–6]. To capture fundamental analogies between gyrogroups and groups, there is another binary operation, called a coaddition in a gyrogroup: see [1] for more information.
    [Show full text]
  • Pdf 395.98 K
    Mathematics Interdisciplinary Research 1 (2016) 187 − 198 An Extension of Poincaré Model of Hyperbolic Geometry with Gyrovector Space Approach Mahfouz Rostamzadeh? and Sayed-Ghahreman Taherian Abstract The aim of this paper is to show the importance of analytic hyperbolic geometry introduced in [9]. In [1], Ungar and Chen showed that the alge- bra of the group SL(2; C) naturally leads to the notion of gyrogroups and gyrovector spaces for dealing with the Lorentz group and its underlying hy- perbolic geometry. They defined the Chen addition and then Chen model of hyperbolic geometry. In this paper, we directly use the isomorphism prop- erties of gyrovector spaces to recover the Chen’s addition and then Chen model of hyperbolic geometry. We show that this model is an extension of the Poincaré model of hyperbolic geometry. For our purpose we consider the Poincaré plane model of hyperbolic geometry inside the complex open unit disc D. Also we prove that this model is isomorphic to the Poincaré model and then to other models of hyperbolic geometry. Finally, by gyrovec- tor space approach we verify some properties of this model in details in full analogue with Euclidean geometry. Keywords: Hyperbolic geometry, gyrogroup, gyrovector space, Poincaré model, analytic hyperbolic geometry. 2010 Mathematics Subject Classification: 51M10, 51N99, 20N99. 1. Introduction Gyrogroups are noncommutative and nonassociative algebraic structures and this noncommutativity-nonassociativity turns out to be generated by the Thomas pre- cession, well-known in the special theory of relativity. Gyrogroups also revealed themselves to be specially fitting in order to deal with formerly unsolved problems in special relativity (e.g.
    [Show full text]
  • Hyperbolic Deep Neural Networks: a Survey
    JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 Hyperbolic Deep Neural Networks: A Survey Wei Peng, Tuomas Varanka, Abdelrahman Mostafa, Henglin Shi, Guoying Zhao* Senior Member, IEEE Abstract—Recently, there has been a rising surge of momentum for deep representation learning in hyperbolic spaces due to their high capacity of modeling data, like knowledge graphs or synonym hierarchies, possessing hierarchical structure. We refer to the model as hyperbolic deep neural network in this paper. Such a hyperbolic neural architecture can potentially lead to drastically more compact models with much more physical interpretability than its counterpart in the Euclidean space. To stimulate future research, this paper presents a coherent and a comprehensive review of the literature around the neural components in the construction of hyperbolic deep neural networks, as well as the generalization of the leading deep approaches to the hyperbolic space. It also presents current applications around various machine learning tasks on several publicly available datasets, together with insightful observations and identifying open questions and promising future directions. Index Terms—Deep Neural Networks, Hyperbolic Geometry, Neural Networks on Riemannian Manifold, Hyperbolic Neural Networks, Poincare´ Model, Lorentz Model, Negative Curvature Geometry. F 1 INTRODUCTION curvature, and it is analogous to a high-dimensional sphere with Earning semantically rich representations of data such as text constant positive curvature. L or images is a central point of interest in current machine In many domains, data is with a tree-like structure or can learning. Recently, deep neural networks [1] have showcased a be represented hierarchically [14]. For instance, social networks, great capability in extracting feature representations and have human skeletons, sentences in natural language, and evolution- dominated many research fields, such as image classification [2], ary relationships between biological entities in phylogenetics.
    [Show full text]
  • Distributivity on the Gyrovector Spaces
    KYUNGPOOK Math. J. 55(2015), 13-20 http://dx.doi.org/10.5666/KMJ.2015.55.1.13 pISSN 1225-6951 eISSN 0454-8124 ⃝c Kyungpook Mathematical Journal Distributivity on the Gyrovector Spaces Sejong Kim Department of Mathematics, Chungbuk National University, Cheongju, 362-763, Korea e-mail : [email protected] Abstract. As a vector space provides a fundamental tool for the study of Euclidean geometry, a gyrovector space provides an algebraic tool for the study of hyperbolic ge- ometry. In general, the gyrovector spaces do not satisfy the distributivity with scalar multiplication. In this article, we see under what condition the distributivity with scalar multiplication is satisfied. 1. Introduction In order to provide an algebraic tool to study Einstein's relativistic velocity sum, A. A. Ungar [2] has introduced a notion of gyrogroup and has developed to- gether the study of analytic hyperbolic geometry. The gyrogroup is a most natural extension of a group into the nonassociative algebra. The associativity (and the commutativity) of group operations is salvaged in a suitably modified form, called a gyroassociativity (and a gyrocommutativity). In Section 2 we introduce a notion of (gyrocommutative) gyrogroup with its examples. Later on it is known that gyrocommutative gyrogroups are equivalent to Bruck loops (see [1]). To elaborate a precise language, we prefix a gyro to terms that de- scribe concepts in Euclidean geometry to mean the analogous concepts in hyperbolic geometry. The prefix gyro stems from Thomas gyration, which is the mathematical abstraction of a special relativistic effect known as Thomas precession. Some gyrocommutative gyrogroups give rise to gyrovector spaces just as some commutative groups give rise to vector spaces.
    [Show full text]
  • On the Geometry of Data Representations, © 2020 ABSTRACT
    diss. eth no. 26998 ONTHEGEOMETRYOF DATAREPRESENTATIONS A thesis submitted to attain the degree of doctor of sciences of eth zurich¨ (Dr. sc. ETH Zurich)¨ presented by gary becigneul´ Master in Mathematics University of Cambridge born on 11.11.1994 citizen of France Accepted on the recommendation of Prof. Dr. Thomas Hofmann (ETH Zurich)¨ Prof. Dr. Gunnar Ratsch¨ (ETH Zurich)¨ Prof. Dr. Tommi Jaakkola (MIT) Prof. Dr. Regina Barzilay (MIT) 2020 Gary Becigneul:´ On the Geometry of Data Representations, © 2020 ABSTRACT The vast majority of state-of-the-art Machine Learning (ML) methods nowadays internally represent the input data as being embedded in a “continuous” space, i.e. as sequences of floats, where nearness in this space is meant to define semantic or statistical similarity w.r.t to the task at hand. As a consequence, the choice of which metric is used to measure nearness, as well as the way data is embedded in this space, currently constitute some of the cornerstones of building meaningful data representations. Characterizing which points should be close to each other in a given set essentially defines a “geometry”. Interestingly, certain geometric properties may be incompatible in a non-trivial manner − or put an- other way, selecting desired geometric properties may have non-trivial implications. The investigation of which geometric properties are de- sirable in a given context, and how to enforce them, is one of the motivations underlying this thesis. Initially motivated by uncovering how Convolutional Neural Net- work (CNN) disentangle representations to make tangled classes linearly separable, we start our investigation by studying how invariance to nuisance deformations may help untangling classes in a classification problem, by locally contracting and flattening group orbits within data classes.
    [Show full text]
  • A Characteristic of Gyroisometries in Möbius Gyrovector Spaces
    WN North-Western European Journal of Mathematics M EJ A characteristic of gyroisometries in Möbius gyrovector spaces Oğuzhan Demırel1 Received: October 18, 2019/Accepted: January 30, 2019/Online: March 1, 2020 Abstract Hugo Steinhaus (1966a, b) has asked whether inside each acute angled triangle there is a point from which perpendiculars to the sides divide the triangle into three parts of equal areas. In this paper, we prove that f : D D is a gyroisometry (hyperbolic isometry) if, and only if it is a continuous mapping! that preserves the partition of a gyrotriangle (hyperbolic triangle) asked by Hugo Steinhaus. Keywords: Gyrogroups, Möbius Gyrovector Spaces, Gyroisometry. msc: 51B10, 30F45, 20N05, 51M09, 51M10, 51M25. 1 Introduction A Möbius transformation f : C C is a mapping of the form w = [ f1g ! [ f1g (az + b)=(cz + d) satisfying ad bc , 0, where a,b,c,d C. The set of all Möbius transformations Möb(C )−is a group with respect2 to the composition and any f Möb(C ) is conformal,[ f1g i.e. it preserves angles. Let us define 2 [ f1g Ω = S C : S is an Euclidean circle or a Euclidean line . f ⊂ [ f1g [ 1g It is well known that if C Ω and f Möb(C ), then f (C) Ω. There are well- known elementary proofs2 that if f is2 a continuous[ f1g injective map2 of the extended complex plane C that maps circles into circles, then f is Möbius. In addition to this a map is Möbius[ f1g if, and only if it preserves cross ratios. In function theory, it is known that a function f is Möbius if, and only if the Schwarzian derivative of f vanishes when f 0(z) , 0.
    [Show full text]
  • Differentiating Through the Fréchet Mean
    Differentiating through the Frechet´ Mean Aaron Lou * 1 Isay Katsman * 1 Qingxuan Jiang * 1 Serge Belongie 1 Ser-Nam Lim 2 Christopher De Sa 1 Abstract Recent advances in deep representation learning on Riemannian manifolds extend classical deep learning operations to better capture the geom- etry of the manifold. One possible extension is the Frechet´ mean, the generalization of the Eu- clidean mean; however, it has been difficult to apply because it lacks a closed form with an eas- ily computable derivative. In this paper, we show how to differentiate through the Frechet´ mean for arbitrary Riemannian manifolds. Then, fo- cusing on hyperbolic space, we derive explicit gradient expressions and a fast, accurate, and hyperparameter-free Frechet´ mean solver. This fully integrates the Frechet´ mean into the hyper- bolic neural network pipeline. To demonstrate this integration, we present two case studies. First, Figure 1. Depicted above is the Frechet´ mean, µ, of three points, we apply our Frechet´ mean to the existing Hyper- x1; x2; x3 in the Lorentz model of hyperbolic space. As one can see, the Frechet´ mean conforms with the geometry of the bolic Graph Convolutional Network, replacing its hyperboloid and is vastly different from the standard Euclidean projected aggregation to obtain state-of-the-art re- mean. sults on datasets with high hyperbolicity. Second, to demonstrate the Frechet´ mean’s capacity to gen- eralize Euclidean neural network operations, we space, in which distances grow exponentially as one moves develop a hyperbolic batch normalization method away from the origin. Such a geometry is naturally equipped that gives an improvement parallel to the one ob- to embed trees, since if we embed the root of the tree near served in the Euclidean setting1.
    [Show full text]
  • Multicoloured Random Graphs: the Random Dynamics Program
    Multicoloured Random Graphs: The Random Dynamics Program Sam Tarzi London, England arXiv:1407.6681v1 [physics.gen-ph] 15 Jul 2014 Copyright Notice © 2014 Sam Tarzi All Rights Reserved Sam Tarzi is identified as the author of this work in accordance with the Copyright, Designs and Patents Act, 1988. No part of this publication may be reproduced or stored in a retrieval system, nor transmitted in any form or by any means - digital scanning, mechanical, photo-copying, recording, or by any other media - without prior permission of the copyright owner. Every effort has been made to trace copyright ownership and permissions are confirmed in the text. Any omission brought to the notice of the publishers will be acknowledged in future impressions. i Abstract Multicoloured Random Graphs: The Random Dynamics Program The Random Dynamics program of Holger Nielsen and co-workers is a proposal to explain the origin of all symmetries, including Lorentz and gauge invariance without appeal to any fundamental invariance of the laws of nature, and to derive the known physical laws in such a way as to be almost unavoidable. The philosophy of Random Dynamics is that the most useful as- sumption that we can make about the fundamental laws is that they are random and then to see how known physics like mechanics and relativity follow from them. It is believed that almost any model of these regularities would appear in some limit for example as energies become small. Almost all theories or models at the fundamental level could then explain known physics and specific models would arise from chaotic fundamental physics which has as few starting assumptions as possible.
    [Show full text]
  • The Hyperbolic Superposition General Quantum State
    The Hyperbolic Superposition of a General Quantum State Author Grant Hubbard ABSTRACT: This dissertation documents an inquiry into the geometric structure of the Spaces used to represent a 2-Dimensional Quantum State Space as it relates to Quantum Computation. In particular we investigate the possible applications of Professor NJ Wildberger's Universal Hyperbolic Geometry and discover a genuinely intuitive method for constructing and hence visualising an otherwise abstract algebraic discipline. Submitted to the University of Hertfordshire in partial fulfilment of the requirements of the degree of MSc by Research 30th October 2015 Contents 1 Introduction 4 1.1 Objectives . .4 1.2 Quantum Considerations . .5 1.3 The Structure of the Thesis . .5 1.3.1 Notation . .7 2 Preliminaries 8 2.1 Fields . .8 2.1.1 Ordered Fields . .9 2.2 Spaces . .9 2.2.1 Vector Spaces . 10 2.2.2 Euclidean n-Space En ......................... 10 2.2.3 Spherical n-Space Sn .......................... 11 2.2.4 Lorentzian n-Space Ln ......................... 11 2.2.5 Hyperbolic n-Space Hn ......................... 11 2.2.6 Metric Spaces . 12 2.2.7 Euclidean Metric Spaces . 12 2.2.8 Elliptic Metric Spaces . 12 2.2.9 Hyperbolic Metric Spaces . 13 2.2.10 Complete Metric Spaces . 13 2.2.11 Hilbert Space H ............................. 14 2.3 Groups . 14 2.3.1 Orthogonal Groups . 15 2.3.2 Unitary Groups . 15 2.3.3 Matrices . 16 2.3.4 The Spinor Matrix . 17 2.4 Projective Geometry . 18 2.4.1 Consequences of Stereographic/Gnomonic Projections . 19 3 A Quantum Mechanical System 20 3.1 Introducing the Qubit .
    [Show full text]