Math 210C. Clifford Algebras and Spin Groups
Total Page:16
File Type:pdf, Size:1020Kb

Load more
Recommended publications
-
Orthogonal Bases and the -So in Section 4.8 We Discussed the Problem of Finding the Orthogonal Projection P
Orthogonal Bases and the -So In Section 4.8 we discussed the problemR. of finding the orthogonal projection p the vector b into the V of , . , suhspace the vectors , v2 ho If v1 v,, form a for V, and the in x n matrix A has these basis vectors as its column vectors. ilt the orthogonal projection p is given by p = Ax where x is the (unique) solution of the normal system ATAx = A7b. The formula for p takes an especially simple and attractive Form when the ba vectors , . .. , v1 v are mutually orthogonal. DEFINITION Orthogonal Basis An orthogonal basis for the suhspacc V of R” is a basis consisting of vectors , ,v,, that are mutually orthogonal, so that v v = 0 if I j. If in additii these basis vectors are unit vectors, so that v1 . = I for i = 1. 2 n, thct the orthogonal basis is called an orthonormal basis. Example 1 The vectors = (1, 1,0), v2 = (1, —1,2), v3 = (—1,1,1) form an orthogonal basis for . We “normalize” ‘ R3 can this orthogonal basis 1w viding each basis vector by its length: If w1=—- (1=1,2,3), lvii 4.9 Orthogonal Bases and the Gram-Schmidt Algorithm 295 then the vectors /1 I 1 /1 1 ‘\ 1 2” / I w1 0) W2 = —— _z) W3 for . form an orthonormal basis R3 , . ..., v, of the m x ii matrix A Now suppose that the column vectors v v2 form an orthogonal basis for the suhspacc V of R’. Then V}.VI 0 0 v2.v .. -
The Magic Square of Reflections and Rotations
THE MAGIC SQUARE OF REFLECTIONS AND ROTATIONS RAGNAR-OLAF BUCHWEITZ†, ELEONORE FABER, AND COLIN INGALLS Dedicated to the memories of two great geometers, H.M.S. Coxeter and Peter Slodowy ABSTRACT. We show how Coxeter’s work implies a bijection between complex reflection groups of rank two and real reflection groups in O(3). We also consider this magic square of reflections and rotations in the framework of Clifford algebras: we give an interpretation using (s)pin groups and explore these groups in small dimensions. CONTENTS 1. Introduction 2 2. Prelude: the groups 2 3. The Magic Square of Rotations and Reflections 4 Reflections, Take One 4 The Magic Square 4 Historical Note 8 4. Quaternions 10 5. The Clifford Algebra of Euclidean 3–Space 12 6. The Pin Groups for Euclidean 3–Space 13 7. Reflections 15 8. General Clifford algebras 15 Quadratic Spaces 16 Reflections, Take two 17 Clifford Algebras 18 Real Clifford Algebras 23 Cl0,1 23 Cl1,0 24 Cl0,2 24 Cl2,0 25 Cl0,3 25 9. Acknowledgements 25 References 25 Date: October 22, 2018. 2010 Mathematics Subject Classification. 20F55, 15A66, 11E88, 51F15 14E16 . Key words and phrases. Finite reflection groups, Clifford algebras, Quaternions, Pin groups, McKay correspondence. R.-O.B. and C.I. were partially supported by their respective NSERC Discovery grants, and E.F. acknowl- edges support of the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 789580. All three authors were supported by the Mathematisches Forschungsinstitut Oberwolfach through the Leibniz fellowship program in 2017, and E.F and C.I. -
Math 2331 – Linear Algebra 6.2 Orthogonal Sets
6.2 Orthogonal Sets Math 2331 { Linear Algebra 6.2 Orthogonal Sets Jiwen He Department of Mathematics, University of Houston [email protected] math.uh.edu/∼jiwenhe/math2331 Jiwen He, University of Houston Math 2331, Linear Algebra 1 / 12 6.2 Orthogonal Sets Orthogonal Sets Basis Projection Orthonormal Matrix 6.2 Orthogonal Sets Orthogonal Sets: Examples Orthogonal Sets: Theorem Orthogonal Basis: Examples Orthogonal Basis: Theorem Orthogonal Projections Orthonormal Sets Orthonormal Matrix: Examples Orthonormal Matrix: Theorems Jiwen He, University of Houston Math 2331, Linear Algebra 2 / 12 6.2 Orthogonal Sets Orthogonal Sets Basis Projection Orthonormal Matrix Orthogonal Sets Orthogonal Sets n A set of vectors fu1; u2;:::; upg in R is called an orthogonal set if ui · uj = 0 whenever i 6= j. Example 82 3 2 3 2 39 < 1 1 0 = Is 4 −1 5 ; 4 1 5 ; 4 0 5 an orthogonal set? : 0 0 1 ; Solution: Label the vectors u1; u2; and u3 respectively. Then u1 · u2 = u1 · u3 = u2 · u3 = Therefore, fu1; u2; u3g is an orthogonal set. Jiwen He, University of Houston Math 2331, Linear Algebra 3 / 12 6.2 Orthogonal Sets Orthogonal Sets Basis Projection Orthonormal Matrix Orthogonal Sets: Theorem Theorem (4) Suppose S = fu1; u2;:::; upg is an orthogonal set of nonzero n vectors in R and W =spanfu1; u2;:::; upg. Then S is a linearly independent set and is therefore a basis for W . Partial Proof: Suppose c1u1 + c2u2 + ··· + cpup = 0 (c1u1 + c2u2 + ··· + cpup) · = 0· (c1u1) · u1 + (c2u2) · u1 + ··· + (cpup) · u1 = 0 c1 (u1 · u1) + c2 (u2 · u1) + ··· + cp (up · u1) = 0 c1 (u1 · u1) = 0 Since u1 6= 0, u1 · u1 > 0 which means c1 = : In a similar manner, c2,:::,cp can be shown to by all 0. -
CLIFFORD ALGEBRAS Property, Then There Is a Unique Isomorphism (V ) (V ) Intertwining the Two Inclusions of V
CHAPTER 2 Clifford algebras 1. Exterior algebras 1.1. Definition. For any vector space V over a field K, let T (V ) = k k k Z T (V ) be the tensor algebra, with T (V ) = V V the k-fold tensor∈ product. The quotient of T (V ) by the two-sided⊗···⊗ ideal (V ) generated byL all v w + w v is the exterior algebra, denoted (V ).I The product in (V ) is usually⊗ denoted⊗ α α , although we will frequently∧ omit the wedge ∧ 1 ∧ 2 sign and just write α1α2. Since (V ) is a graded ideal, the exterior algebra inherits a grading I (V )= k(V ) ∧ ∧ k Z M∈ where k(V ) is the image of T k(V ) under the quotient map. Clearly, 0(V )∧ = K and 1(V ) = V so that we can think of V as a subspace of ∧(V ). We may thus∧ think of (V ) as the associative algebra linearly gener- ated∧ by V , subject to the relations∧ vw + wv = 0. We will write φ = k if φ k(V ). The exterior algebra is commutative | | ∈∧ (in the graded sense). That is, for φ k1 (V ) and φ k2 (V ), 1 ∈∧ 2 ∈∧ [φ , φ ] := φ φ + ( 1)k1k2 φ φ = 0. 1 2 1 2 − 2 1 k If V has finite dimension, with basis e1,...,en, the space (V ) has basis ∧ e = e e I i1 · · · ik for all ordered subsets I = i1,...,ik of 1,...,n . (If k = 0, we put { } k { n } e = 1.) In particular, we see that dim (V )= k , and ∅ ∧ n n dim (V )= = 2n. -
Pin Groups in General Relativity
PHYSICAL REVIEW D 101, 021702(R) (2020) Rapid Communications Pin groups in general relativity Bas Janssens Institute of Applied Mathematics, Delft University of Technology, 2628 XE Delft, Netherlands (Received 16 September 2019; published 28 January 2020) There are eight possible Pin groups that can be used to describe the transformation behavior of fermions under parity and time reversal. We show that only two of these are compatible with general relativity, in the sense that the configuration space of fermions coupled to gravity transforms appropriately under the space- time diffeomorphism group. DOI: 10.1103/PhysRevD.101.021702 I. INTRODUCTION We derive these restrictions in the “universal spinor ” For bosons, the space-time transformation behavior is bundle approach for fermions coupled to gravity, as – – governed by the Lorentz group Oð3; 1Þ, which comprises developed in [3 5] for the Riemannian and in [6 9] for four connected components. Rotations and boosts are the Lorentzian case. However, our results remain valid in contained in the connected component of unity, the proper other formulations that are covariant under infinitesimal “ ” orthochronous Lorentz group SO↑ 3; 1 . Parity (P) and time space-time diffeomorphisms, such as the global approach ð Þ – reversal (T) are encoded in the other three connected of [2,10 12]. To underline this point, we highlight the role components of the Lorentz group, the translates of of the space-time diffeomorphism group in restricting the ↑ admissible Pin groups. SO ð3; 1Þ by P, T and PT. For fermions, the space-time transformation behavior is Selecting the correct Pin groups is important from a 3 1 fundamental point of view—it determines the transforma- governed by a double cover of Oð ; Þ. -
Geometric (Clifford) Algebra and Its Applications
Geometric (Clifford) algebra and its applications Douglas Lundholm F01, KTH January 23, 2006∗ Abstract In this Master of Science Thesis I introduce geometric algebra both from the traditional geometric setting of vector spaces, and also from a more combinatorial view which simplifies common relations and opera- tions. This view enables us to define Clifford algebras with scalars in arbitrary rings and provides new suggestions for an infinite-dimensional approach. Furthermore, I give a quick review of classic results regarding geo- metric algebras, such as their classification in terms of matrix algebras, the connection to orthogonal and Spin groups, and their representation theory. A number of lower-dimensional examples are worked out in a sys- tematic way using so called norm functions, while general applications of representation theory include normed division algebras and vector fields on spheres. I also consider examples in relativistic physics, where reformulations in terms of geometric algebra give rise to both computational and conceptual simplifications. arXiv:math/0605280v1 [math.RA] 10 May 2006 ∗Corrected May 2, 2006. Contents 1 Introduction 1 2 Foundations 3 2.1 Geometric algebra ( , q)...................... 3 2.2 Combinatorial CliffordG V algebra l(X,R,r)............. 6 2.3 Standardoperations .........................C 9 2.4 Vectorspacegeometry . 13 2.5 Linearfunctions ........................... 16 2.6 Infinite-dimensional Clifford algebra . 19 3 Isomorphisms 23 4 Groups 28 4.1 Group actions on .......................... 28 4.2 TheLipschitzgroupG ......................... 30 4.3 PropertiesofPinandSpingroups . 31 4.4 Spinors ................................ 34 5 A study of lower-dimensional algebras 36 5.1 (R1) ................................. 36 G 5.2 (R0,1) =∼ C -Thecomplexnumbers . 36 5.3 G(R0,0,1)............................... -
Orthogonal Bases
Orthogonal bases • Recall: Suppose that v1 , , vn are nonzero and (pairwise) orthogonal. Then v1 , , vn are independent. Definition 1. A basis v1 , , vn of a vector space V is an orthogonal basis if the vectors are (pairwise) orthogonal. 1 1 0 3 Example 2. Are the vectors − 1 , 1 , 0 an orthogonal basis for R ? 0 0 1 Solution. Note that we do not need to check that the three vectors are independent. That follows from their orthogonality. Example 3. Suppose v1 , , vn is an orthogonal basis of V, and that w is in V. Find c1 , , cn such that w = c1 v1 + + cnvn. Solution. Take the dot product of v1 with both sides: If v1 , , vn is an orthogonal basis of V, and w is in V, then w · v j w = c1 v1 + + cnvn with cj = . v j · v j Armin Straub 1 [email protected] 3 1 1 0 Example 4. Express 7 in terms of the basis − 1 , 1 , 0 . 4 0 0 1 Solution. Definition 5. A basis v1 , , vn of a vector space V is an orthonormal basis if the vectors are orthogonal and have length 1 . If v1 , , vn is an orthonormal basis of V, and w is in V, then w = c1 v1 + + cnvn with cj = v j · w. 1 1 0 Example 6. Is the basis − 1 , 1 , 0 orthonormal? If not, normalize the vectors 0 0 1 to produce an orthonormal basis. Solution. Armin Straub 2 [email protected] Orthogonal projections Definition 7. The orthogonal projection of vector x onto vector y is x · y xˆ = y. -
Basics of Euclidean Geometry
This is page 162 Printer: Opaque this 6 Basics of Euclidean Geometry Rien n'est beau que le vrai. |Hermann Minkowski 6.1 Inner Products, Euclidean Spaces In a±ne geometry it is possible to deal with ratios of vectors and barycen- ters of points, but there is no way to express the notion of length of a line segment or to talk about orthogonality of vectors. A Euclidean structure allows us to deal with metric notions such as orthogonality and length (or distance). This chapter and the next two cover the bare bones of Euclidean ge- ometry. One of our main goals is to give the basic properties of the transformations that preserve the Euclidean structure, rotations and re- ections, since they play an important role in practice. As a±ne geometry is the study of properties invariant under bijective a±ne maps and projec- tive geometry is the study of properties invariant under bijective projective maps, Euclidean geometry is the study of properties invariant under certain a±ne maps called rigid motions. Rigid motions are the maps that preserve the distance between points. Such maps are, in fact, a±ne and bijective (at least in the ¯nite{dimensional case; see Lemma 7.4.3). They form a group Is(n) of a±ne maps whose corresponding linear maps form the group O(n) of orthogonal transformations. The subgroup SE(n) of Is(n) corresponds to the orientation{preserving rigid motions, and there is a corresponding 6.1. Inner Products, Euclidean Spaces 163 subgroup SO(n) of O(n), the group of rotations. -
[Math.CA] 18 Sep 2001 Lmnso Ieradra Analysis Real and Linear of Elements Tpe Semmes Stephen Oso,Texas Houston, Ieuniversity Rice Preface
Elements of Linear and Real Analysis Stephen Semmes Rice University Houston, Texas arXiv:math/0108030v5 [math.CA] 18 Sep 2001 Preface This book deals with some basic themes in mathematical analysis along the lines of classical norms on functions and sequences, general normed vector spaces, inner product spaces, linear operators, some maximal and square- function operators, interpolation of operators, and quasisymmetric mappings between metric spaces. Aspects of the broad area of harmonic analysis are entailed in particular, involving famous work of M. Riesz, Hardy, Littlewood, Paley, Calder´on, and Zygmund. However, instead of working with arbitrary continuous or integrable func- tions, we shall often be ready to use only step functions on an interval, i.e., functions which are piecewise-constant. Similarly, instead of infinite- dimensional Hilbert or Banach spaces, we shall frequently restrict our atten- tion to finite-dimensional inner product or normed vector spaces. We shall, however, be interested in quantitative matters. We do not attempt to be exhaustive in any way, and there are many re- lated and very interesting subjects that are not addressed. The bibliography lists a number of books and articles with further information. The formal prerequisites for this book are quite limited. Much of what we do is connected to the notion of integration, but for step functions ordinary integrals reduce to finite sums. A sufficient background should be provided by standard linear algebra of real and complex finite-dimensional vector spaces and some knowledge of beginning analysis, as in the first few chapters of Rudin’s celebrated Principles of Mathematical Analysis [Rud1]. -
17. Inner Product Spaces Definition 17.1. Let V Be a Real Vector Space
17. Inner product spaces Definition 17.1. Let V be a real vector space. An inner product on V is a function h ; i: V × V −! R; which is • symmetric, that is hu; vi = hv; ui: • bilinear, that is linear (in both factors): hλu, vi = λhu; vi; for all scalars λ and hu1 + u2; vi = hu1; vi + hu2; vi; for all vectors u1, u2 and v. • positive that is hv; vi ≥ 0: • non-degenerate that is if hu; vi = 0 for every v 2 V then u = 0. We say that V is a real inner product space. The associated quadratic form is the function Q: V −! R; defined by Q(v) = hv; vi: Example 17.2. Let A 2 Mn;n(R) be a real matrix. We can define a function n n h ; i: R × R −! R; by the rule hu; vi = utAv: The basic rules of matrix multiplication imply that this function is bi- linear. Note that the entries of A are given by t aij = eiAej = hei; eji: In particular, it is symmetric if and only if A is symmetric that is At = A. It is non-degenerate if and only if A is invertible, that is A has rank n. Positivity is a little harder to characterise. Perhaps the canonical example is to take A = In. In this case if t P u = (r1; r2; : : : ; rn) and v = (s1; s2; : : : ; sn) then u Inv = risi. Note 1 P 2 that if we take u = v then we get ri . The square root of this is the Euclidean distance. -
8. Orthogonality
8. Orthogonality In Section 5.3 we introduced the dot product in Rn and extended the basic geometric notions of length and n distance. A set f1, f2, ..., fm of nonzero vectors in R was called an orthogonal set if fi f j = 0 for all i = j, and it{ was proved that} every orthogonal set is independent. In particular, it was observed· that the expansion6 of a vector as a linear combination of orthogonal basis vectors is easy to obtain because formulas exist for the coefficients. Hence the orthogonal bases are the “nice” bases, and much of this chapter is devoted to extending results about bases to orthogonal bases. This leads to some very powerful methods and theorems. Our first task is to show that every subspace of Rn has an orthogonal basis. 8.1 Orthogonal Complements and Projections If v1, ..., vm is linearly independent in a general vector space, and if vm+1 is not in span v1, ..., vm , { } { n } then v1, ..., vm, vm 1 is independent (Lemma 6.4.1). Here is the analog for orthogonal sets in R . { + } Lemma 8.1.1: Orthogonal Lemma n n Let f1, f2, ..., fm be an orthogonal set in R . Given x in R , write { } x f1 x f2 x fm fm+1 = x · 2 f1 · 2 f2 · 2 fm f1 f2 fm − k k − k k −···− k k Then: 1. fm 1 fk = 0 for k = 1, 2, ..., m. + · 2. If x is not in span f1, ..., fm , then fm 1 = 0 and f1, ..., fm, fm 1 is an orthogonal set. -
Bilinear Forms Lecture Notes for MA1212
Chapter 3. Bilinear forms Lecture notes for MA1212 P. Karageorgis [email protected] 1/25 Bilinear forms Definition 3.1 – Bilinear form A bilinear form on a real vector space V is a function f : V V R × → which assigns a number to each pair of elements of V in such a way that f is linear in each variable. A typical example of a bilinear form is the dot product on Rn. We shall usually write x, y instead of f(x, y) for simplicity and we h i shall also identify each 1 1 matrix with its unique entry. × Theorem 3.2 – Bilinear forms on Rn Every bilinear form on Rn has the form t x, y = x Ay = aijxiyj h i Xi,j for some n n matrix A and we also have aij = ei, ej for all i,j. × h i 2/25 Matrix of a bilinear form Definition 3.3 – Matrix of a bilinear form Suppose that , is a bilinear form on V and let v1, v2,..., vn be a h i basis of V . The matrix of the form with respect to this basis is the matrix A whose entries are given by aij = vi, vj for all i,j. h i Theorem 3.4 – Change of basis Suppose that , is a bilinear form on Rn and let A be its matrix with respect to theh standardi basis. Then the matrix of the form with respect t to some other basis v1, v2,..., vn is given by B AB, where B is the matrix whose columns are the vectors v1, v2,..., vn.