Lectures on Poisson Geometry
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ADDITIVE MAPS on RANK K BIVECTORS 1. Introduction. Let N 2 Be an Integer and Let F Be a Field. We Denote by M N(F) the Algeb
Electronic Journal of Linear Algebra, ISSN 1081-3810 A publication of the International Linear Algebra Society Volume 36, pp. 847-856, December 2020. ADDITIVE MAPS ON RANK K BIVECTORS∗ WAI LEONG CHOOIy AND KIAM HEONG KWAy V2 Abstract. Let U and V be linear spaces over fields F and K, respectively, such that dim U = n > 2 and jFj > 3. Let U n−1 V2 V2 be the second exterior power of U. Fixing an even integer k satisfying 2 6 k 6 n, it is shown that a map : U! V satisfies (u + v) = (u) + (v) for all rank k bivectors u; v 2 V2 U if and only if is an additive map. Examples showing the indispensability of the assumption on k are given. Key words. Additive maps, Second exterior powers, Bivectors, Ranks, Alternate matrices. AMS subject classifications. 15A03, 15A04, 15A75, 15A86. 1. Introduction. Let n > 2 be an integer and let F be a field. We denote by Mn(F) the algebra of n×n matrices over F. Given a nonempty subset S of Mn(F), a map : Mn(F) ! Mn(F) is called commuting on S (respectively, additive on S) if (A)A = A (A) for all A 2 S (respectively, (A + B) = (A) + (B) for all A; B 2 S). In 2012, using Breˇsar'sresult [1, Theorem A], Franca [3] characterized commuting additive maps on invertible (respectively, singular) matrices of Mn(F). He continued to characterize commuting additive maps on rank k matrices of Mn(F) in [4], where 2 6 k 6 n is a fixed integer, and commuting additive maps on rank one matrices of Mn(F) in [5]. -
Geometric Quantization of Poisson Manifolds Via Symplectic Groupoids
Geometric Quantization of Poisson Manifolds via Symplectic Groupoids Daniel Felipe Bermudez´ Montana~ Advisor: Prof. Alexander Cardona Gu´ıo, PhD. A Dissertation Presented in Candidacy for the Degree of Bachelor of Science in Mathematics Universidad de los Andes Department of Mathematics Bogota,´ Colombia 2019 Abstract The theory of Lie algebroids and Lie groupoids is a convenient framework for study- ing properties of Poisson Manifolds. In this work we approach the problem of geometric quantization of Poisson Manifolds using the theory of symplectic groupoids. We work the obstructions of geometric prequantization and show how they can be understood as Lie algebroid's integrability obstructions. Furthermore, by examples, we explore the complete quantization scheme using polarisations and convolution algebras of Fell line bundles. ii Acknowledgements Words fall short for the the graditute I have for people I will mention. First of all I want to thank my advisors Alexander Cardona Guio and Andres Reyes Lega. Alexander Cardona, his advice and insighfull comments guided through this research. I am extremely thankfull and indebted to him for sharing his expertise and knowledge with me. Andres Reyes, as my advisor has tough me more than I could ever ever give him credit for here. By example he has shown me what a good scientist, educator and mentor should be. To my parents, my fist advisors: Your love, support and caring have provided me with the moral and emotional support to confront every aspect of my life; I owe it all to you. To my brothers: the not that productive time I spent with you have always brought me a lot joy - thank you for allowing me the time to research and write during this work. -
Do Killingâ•Fiyano Tensors Form a Lie Algebra?
University of Massachusetts Amherst ScholarWorks@UMass Amherst Physics Department Faculty Publication Series Physics 2007 Do Killing–Yano tensors form a Lie algebra? David Kastor University of Massachusetts - Amherst, [email protected] Sourya Ray University of Massachusetts - Amherst Jennie Traschen University of Massachusetts - Amherst, [email protected] Follow this and additional works at: https://scholarworks.umass.edu/physics_faculty_pubs Part of the Physics Commons Recommended Citation Kastor, David; Ray, Sourya; and Traschen, Jennie, "Do Killing–Yano tensors form a Lie algebra?" (2007). Classical and Quantum Gravity. 1235. Retrieved from https://scholarworks.umass.edu/physics_faculty_pubs/1235 This Article is brought to you for free and open access by the Physics at ScholarWorks@UMass Amherst. It has been accepted for inclusion in Physics Department Faculty Publication Series by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. Do Killing-Yano tensors form a Lie algebra? David Kastor, Sourya Ray and Jennie Traschen Department of Physics University of Massachusetts Amherst, MA 01003 ABSTRACT Killing-Yano tensors are natural generalizations of Killing vec- tors. We investigate whether Killing-Yano tensors form a graded Lie algebra with respect to the Schouten-Nijenhuis bracket. We find that this proposition does not hold in general, but that it arXiv:0705.0535v1 [hep-th] 3 May 2007 does hold for constant curvature spacetimes. We also show that Minkowski and (anti)-deSitter spacetimes have the maximal num- ber of Killing-Yano tensors of each rank and that the algebras of these tensors under the SN bracket are relatively simple exten- sions of the Poincare and (A)dS symmetry algebras. -
Coisotropic Embeddings in Poisson Manifolds 1
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 361, Number 7, July 2009, Pages 3721–3746 S 0002-9947(09)04667-4 Article electronically published on February 10, 2009 COISOTROPIC EMBEDDINGS IN POISSON MANIFOLDS A. S. CATTANEO AND M. ZAMBON Abstract. We consider existence and uniqueness of two kinds of coisotropic embeddings and deduce the existence of deformation quantizations of certain Poisson algebras of basic functions. First we show that any submanifold of a Poisson manifold satisfying a certain constant rank condition, already con- sidered by Calvo and Falceto (2004), sits coisotropically inside some larger cosymplectic submanifold, which is naturally endowed with a Poisson struc- ture. Then we give conditions under which a Dirac manifold can be embedded coisotropically in a Poisson manifold, extending a classical theorem of Gotay. 1. Introduction The following two results in symplectic geometry are well known. First: a sub- manifold C of a symplectic manifold (M,Ω) is contained coisotropically in some symplectic submanifold of M iff the pullback of Ω to C has constant rank; see Marle’s work [17]. Second: a manifold endowed with a closed 2-form ω can be em- bedded coisotropically into a symplectic manifold (M,Ω) so that i∗Ω=ω (where i is the embedding) iff ω has constant rank; see Gotay’s work [15]. In this paper we extend these results to the setting of Poisson geometry. Recall that P is a Poisson manifold if it is endowed with a bivector field Π ∈ Γ(∧2TP) satisfying the Schouten-bracket condition [Π, Π] = 0. A submanifold C of (P, Π) is coisotropic if N ∗C ⊂ TC, where the conormal bundle N ∗C is defined as the anni- ∗ hilator of TC in TP|C and : T P → TP is the contraction with the bivector Π. -
Lectures on the Geometry of Poisson Manifolds, by Izu Vaisman, Progress in Math- Ematics, Vol
BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 33, Number 2, April 1996 Lectures on the geometry of Poisson manifolds, by Izu Vaisman, Progress in Math- ematics, vol. 118, Birkh¨auser, Basel and Boston, 1994, vi + 205 pp., $59.00, ISBN 3-7643-5016-4 For a symplectic manifold M, an important construct is the Poisson bracket, which is defined on the space of all smooth functions and satisfies the following properties: 1. f,g is bilinear with respect to f and g, 2. {f,g} = g,f (skew-symmetry), 3. {h, fg} =−{h, f }g + f h, g (the Leibniz rule of derivation), 4. {f, g,h} {+ g,} h, f { + }h, f,g = 0 (the Jacobi identity). { { }} { { }} { { }} The best-known Poisson bracket is perhaps the one defined on the function space of R2n by: n ∂f ∂g ∂f ∂g (1) f,g = . { } ∂q ∂p − ∂p ∂q i=1 i i i i X Poisson manifolds appear as a natural generalization of symplectic manifolds: a Poisson manifold isasmoothmanifoldwithaPoissonbracketdefinedonits function space. The first three properties of a Poisson bracket imply that in local coordinates a Poisson bracket is of the form given by n ∂f ∂g f,g = πij , { } ∂x ∂x i,j=1 i j X where πij may be seen as the components of a globally defined antisymmetric contravariant 2-tensor π on the manifold, called a Poisson tensor or a Poisson bivector field. The Jacobi identity can then be interpreted as a nonlinear first-order differential equation for π with a natural geometric meaning. The Poisson tensor # π of a Poisson manifold P induces a bundle map π : T ∗P TP in an obvious way. -
Preserivng Pieces of Information in a Given Order in HRR and GA$ C$
Proceedings of the Federated Conference on ISBN 978-83-60810-22-4 Computer Science and Information Systems pp. 213–220 Preserivng pieces of information in a given order in HRR and GAc Agnieszka Patyk-Ło´nska Abstract—Geometric Analogues of Holographic Reduced Rep- Secondly, this method of encoding will not detect similarity resentations (GA HRR or GAc—the continuous version of between eye and yeye. discrete GA described in [16]) employ role-filler binding based A quantum-like attempt to tackle the problem of information on geometric products. Atomic objects are real-valued vectors in n-dimensional Euclidean space and complex statements belong to ordering was made in [1]—a version of semantic analysis, a hierarchy of multivectors. A property of GAc and HRR studied reformulated in terms of a Hilbert-space problem, is compared here is the ability to store pieces of information in a given order with structures known from quantum mechanics. In particular, by means of trajectory association. We describe results of an an LSA matrix representation [1], [10] is rewritten by the experiment: finding the alignment of items in a sequence without means of quantum notation. Geometric algebra has also been the precise knowledge of trajectory vectors. Index Terms—distributed representations, geometric algebra, used extensively in quantum mechanics ([2], [4], [3]) and so HRR, BSC, word order, trajectory associations, bag of words. there seems to be a natural connection between LSA and GAc, which is the ground for fututre work on the problem I. INTRODUCTION of preserving pieces of information in a given order. VER the years several attempts have been made to As far as convolutions are concerned, the most interesting O preserve the order in which the objects are to be remem- approach to remembering information in a given order has bered with the help of binding and superposition. -
8 Rank of a Matrix
8 Rank of a matrix We already know how to figure out that the collection (v1;:::; vk) is linearly dependent or not if each n vj 2 R . Recall that we need to form the matrix [v1 j ::: j vk] with the given vectors as columns and see whether the row echelon form of this matrix has any free variables. If these vectors linearly independent (this is an abuse of language, the correct phrase would be \if the collection composed of these vectors is linearly independent"), then, due to the theorems we proved, their span is a subspace of Rn of dimension k, and this collection is a basis of this subspace. Now, what if they are linearly dependent? Still, their span will be a subspace of Rn, but what is the dimension and what is a basis? Naively, I can answer this question by looking at these vectors one by one. In particular, if v1 =6 0 then I form B = (v1) (I know that one nonzero vector is linearly independent). Next, I add v2 to this collection. If the collection (v1; v2) is linearly dependent (which I know how to check), I drop v2 and take v3. If independent then I form B = (v1; v2) and add now third vector v3. This procedure will lead to the vectors that form a basis of the span of my original collection, and their number will be the dimension. Can I do it in a different, more efficient way? The answer if \yes." As a side result we'll get one of the most important facts of the basic linear algebra. -
2.2 Kernel and Range of a Linear Transformation
2.2 Kernel and Range of a Linear Transformation Performance Criteria: 2. (c) Determine whether a given vector is in the kernel or range of a linear trans- formation. Describe the kernel and range of a linear transformation. (d) Determine whether a transformation is one-to-one; determine whether a transformation is onto. When working with transformations T : Rm → Rn in Math 341, you found that any linear transformation can be represented by multiplication by a matrix. At some point after that you were introduced to the concepts of the null space and column space of a matrix. In this section we present the analogous ideas for general vector spaces. Definition 2.4: Let V and W be vector spaces, and let T : V → W be a transformation. We will call V the domain of T , and W is the codomain of T . Definition 2.5: Let V and W be vector spaces, and let T : V → W be a linear transformation. • The set of all vectors v ∈ V for which T v = 0 is a subspace of V . It is called the kernel of T , And we will denote it by ker(T ). • The set of all vectors w ∈ W such that w = T v for some v ∈ V is called the range of T . It is a subspace of W , and is denoted ran(T ). It is worth making a few comments about the above: • The kernel and range “belong to” the transformation, not the vector spaces V and W . If we had another linear transformation S : V → W , it would most likely have a different kernel and range. -
23. Kernel, Rank, Range
23. Kernel, Rank, Range We now study linear transformations in more detail. First, we establish some important vocabulary. The range of a linear transformation f : V ! W is the set of vectors the linear transformation maps to. This set is also often called the image of f, written ran(f) = Im(f) = L(V ) = fL(v)jv 2 V g ⊂ W: The domain of a linear transformation is often called the pre-image of f. We can also talk about the pre-image of any subset of vectors U 2 W : L−1(U) = fv 2 V jL(v) 2 Ug ⊂ V: A linear transformation f is one-to-one if for any x 6= y 2 V , f(x) 6= f(y). In other words, different vector in V always map to different vectors in W . One-to-one transformations are also known as injective transformations. Notice that injectivity is a condition on the pre-image of f. A linear transformation f is onto if for every w 2 W , there exists an x 2 V such that f(x) = w. In other words, every vector in W is the image of some vector in V . An onto transformation is also known as an surjective transformation. Notice that surjectivity is a condition on the image of f. 1 Suppose L : V ! W is not injective. Then we can find v1 6= v2 such that Lv1 = Lv2. Then v1 − v2 6= 0, but L(v1 − v2) = 0: Definition Let L : V ! W be a linear transformation. The set of all vectors v such that Lv = 0W is called the kernel of L: ker L = fv 2 V jLv = 0g: 1 The notions of one-to-one and onto can be generalized to arbitrary functions on sets. -
Symplectic Operad Geometry and Graph Homology
1 SYMPLECTIC OPERAD GEOMETRY AND GRAPH HOMOLOGY SWAPNEEL MAHAJAN Abstract. A theorem of Kontsevich relates the homology of certain infinite dimensional Lie algebras to graph homology. We formulate this theorem using the language of reversible operads and mated species. All ideas are explained using a pictorial calculus of cuttings and matings. The Lie algebras are con- structed as Hamiltonian functions on a symplectic operad manifold. And graph complexes are defined for any mated species. The general formulation gives us many examples including a graph homology for groups. We also speculate on the role of deformation theory for operads in this setting. Contents 1. Introduction 1 2. Species, operads and reversible operads 6 3. The mating functor 10 4. Overview of symplectic operad geometry 13 5. Cuttings and Matings 15 6. Symplectic operad theory 19 7. Examples motivated by PROPS 23 8. Graph homology 26 9. Graph homology for groups 32 10. Graph cohomology 34 11. The main theorem 38 12. Proof of the main theorem-Part I 39 13. Proof of the main theorem-Part II 43 14. Proof of the main theorem-Part III 47 Appendix A. Deformation quantisation 48 Appendix B. The deformation map on graphs 52 References 54 1. Introduction This paper is my humble tribute to the genius of Maxim Kontsevich. Needless to say, the credit for any new ideas that occur here goes to him, and not me. For how I got involved in this wonderful project, see the historical note (1.3). In the papers [22, 23], Kontsevich defined three Lie algebras and related their homology with classical invariants, including the homology of the group of outer 1 2000 Mathematics Subject Classification. -
CME292: Advanced MATLAB for Scientific Computing
CME292: Advanced MATLAB for Scientific Computing Homework #2 NLA & Opt Due: Tuesday, April 28, 2015 Instructions This problem set will be combined with Homework #3. For this combined problem set, 2 out of the 6 problems are required. You are free to choose the problems you complete. Before completing problem set, please see HomeworkInstructions on Coursework for general homework instructions, grading policy, and number of required problems per problem set. Problem 1 A matrix A P Rmˆn can be compressed using the low-rank approximation property of the SVD. The approximation algorithm is given in Algorithm1. Algorithm 1 Low-Rank Approximation using SVD Input: A P Rmˆn of rank r and approximation rank k (k ¤ r) mˆn Output: Ak P R , optimal rank k approximation to A 1: Compute (thin) SVD of A “ UΣVT , where U P Rmˆr, Σ P Rrˆr, V P Rnˆr T 2: Ak “ Up:; 1 : kqΣp1 : k; 1 : kqVp:; 1 : kq Notice the low-rank approximation, Ak, and the original matrix, A, are the same size. To actually achieve compression, the truncated singular factors, Up:; 1 : kq P Rmˆk, Σp1 : k; 1 : kq P Rkˆk, Vp:; 1 : kq P Rnˆk, should be stored. As Σ is diagonal, the required storage is pm`n`1qk doubles. The original matrix requires mn storing mn doubles. Therefore, the compression is useful only if k ă m`n`1 . Recall from lecture that the SVD is among the most expensive matrix factorizations in numerical linear algebra. Many SVD approximations have been developed; a particularly interesting one from [1] is given in Algorithm2. -
BRST Charge and Poisson Algebrasy
Discrete Mathematics and Theoretical Computer Science 1, 1997, 115–127 BRST Charge and Poisson Algebrasy H. Caprasse D´epartement d’Astrophysique, Universit´edeLi`ege, Institut de Physique B-5, All´ee du 6 aout, Sart-Tilman B-4000, Liege, Belgium E-Mail: [email protected] An elementary introduction to the classical version of gauge theories is made. The shortcomings of the usual gauge fixing process are pointed out. They justify the need to replace it by a global symmetry: the BRST symmetry and its associated BRST charge. The main mathematical steps required to construct it are described. The algebra of constraints is, in general, a nonlinear Poisson algebra. In the nonlinear case the computation of the BRST charge by hand is hard. It is explained how this computation can be made algorithmic. The main features of a recently created BRST computer algebra program are described. It can handle quadratic algebras very easily. Its capability to compute the BRST charge as a formal power series in the generic case of a cubic algebra is illustrated. Keywords: guage theory, BRST symmetry 1 Introduction Gauge invariance plays a key role in the formulation of all basic physical theories: this is true both at the classical and quantum levels. General relativity as well as all field theoretic models which describe the electromagnetic, the weak and the strong interactions are based on it. One feature of gauge invariant theories is that they contain what physicists call ‘non-observable’ quantities. These are objects which are not in one-to-one correspondance with physical measurements.