Supersymmetric Hilbert Space
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BIPRODUCTS WITHOUT POINTEDNESS 1. Introduction
BIPRODUCTS WITHOUT POINTEDNESS MARTTI KARVONEN Abstract. We show how to define biproducts up to isomorphism in an ar- bitrary category without assuming any enrichment. The resulting notion co- incides with the usual definitions whenever all binary biproducts exist or the category is suitably enriched, resulting in a modest yet strict generalization otherwise. We also characterize when a category has all binary biproducts in terms of an ambidextrous adjunction. Finally, we give some new examples of biproducts that our definition recognizes. 1. Introduction Given two objects A and B living in some category C, their biproduct { according to a standard definition [4] { consists of an object A ⊕ B together with maps p i A A A ⊕ B B B iA pB such that pAiA = idA pBiB = idB pBiA = 0A;B pAiB = 0B;A and idA⊕B = iApA + iBpB. For us to be able to make sense of the equations, we must assume that C is enriched in commutative monoids. One can get a slightly more general definition that only requires zero morphisms but no addition { that is, enrichment in pointed sets { by replacing the last equation with the condition that (A ⊕ B; pA; pB) is a product of A and B and that (A ⊕ B; iA; iB) is their coproduct. We will call biproducts in the first sense additive biproducts and in the second sense pointed biproducts in order to contrast these definitions with our central object of study { a pointless generalization of biproducts that can be applied in any category C, with no assumptions concerning enrichment. This is achieved by replacing the equations referring to zero with the single equation (1.1) iApAiBpB = iBpBiApA, which states that the two canonical idempotents on A ⊕ B commute with one another. -
The Factorization Problem and the Smash Biproduct of Algebras and Coalgebras
Algebras and Representation Theory 3: 19–42, 2000. 19 © 2000 Kluwer Academic Publishers. Printed in the Netherlands. The Factorization Problem and the Smash Biproduct of Algebras and Coalgebras S. CAENEPEEL1, BOGDAN ION2, G. MILITARU3;? and SHENGLIN ZHU4;?? 1University of Brussels, VUB, Faculty of Applied Sciences, Pleinlaan 2, B-1050 Brussels, Belgium 2Department of Mathematics, Princeton University, Fine Hall, Washington Road, Princeton, NJ 08544-1000, U.S.A. 3University of Bucharest, Faculty of Mathematics, Str. Academiei 14, RO-70109 Bucharest 1, Romania 4Institute of Mathematics, Fudan University, Shanghai 200433, China (Received: July 1998) Presented by A. Verschoren Abstract. We consider the factorization problem for bialgebras. Let L and H be algebras and coalgebras (but not necessarily bialgebras) and consider two maps R: H ⊗ L ! L ⊗ H and W: L ⊗ H ! H ⊗ L. We introduce a product K D L W FG R H and we give necessary and sufficient conditions for K to be a bialgebra. Our construction generalizes products introduced by Majid and Radford. Also, some of the pointed Hopf algebras that were recently constructed by Beattie, Dascˇ alescuˇ and Grünenfelder appear as special cases. Mathematics Subject Classification (2000): 16W30. Key words: Hopf algebra, smash product, factorization structure. Introduction The factorization problem for a ‘structure’ (group, algebra, coalgebra, bialgebra) can be roughly stated as follows: under which conditions can an object X be written as a product of two subobjects A and B which have minimal intersection (for example A \ B Df1Xg in the group case). A related problem is that of the construction of a new object (let us denote it by AB) out of the objects A and B.In the constructions of this type existing in the literature ([13, 20, 27]), the object AB factorizes into A and B. -
FUNCTIONAL ANALYSIS 1. Banach and Hilbert Spaces in What
FUNCTIONAL ANALYSIS PIOTR HAJLASZ 1. Banach and Hilbert spaces In what follows K will denote R of C. Definition. A normed space is a pair (X, k · k), where X is a linear space over K and k · k : X → [0, ∞) is a function, called a norm, such that (1) kx + yk ≤ kxk + kyk for all x, y ∈ X; (2) kαxk = |α|kxk for all x ∈ X and α ∈ K; (3) kxk = 0 if and only if x = 0. Since kx − yk ≤ kx − zk + kz − yk for all x, y, z ∈ X, d(x, y) = kx − yk defines a metric in a normed space. In what follows normed paces will always be regarded as metric spaces with respect to the metric d. A normed space is called a Banach space if it is complete with respect to the metric d. Definition. Let X be a linear space over K (=R or C). The inner product (scalar product) is a function h·, ·i : X × X → K such that (1) hx, xi ≥ 0; (2) hx, xi = 0 if and only if x = 0; (3) hαx, yi = αhx, yi; (4) hx1 + x2, yi = hx1, yi + hx2, yi; (5) hx, yi = hy, xi, for all x, x1, x2, y ∈ X and all α ∈ K. As an obvious corollary we obtain hx, y1 + y2i = hx, y1i + hx, y2i, hx, αyi = αhx, yi , Date: February 12, 2009. 1 2 PIOTR HAJLASZ for all x, y1, y2 ∈ X and α ∈ K. For a space with an inner product we define kxk = phx, xi . Lemma 1.1 (Schwarz inequality). -
Using Functional Analysis and Sobolev Spaces to Solve Poisson’S Equation
USING FUNCTIONAL ANALYSIS AND SOBOLEV SPACES TO SOLVE POISSON'S EQUATION YI WANG Abstract. We study Banach and Hilbert spaces with an eye to- wards defining weak solutions to elliptic PDE. Using Lax-Milgram we prove that weak solutions to Poisson's equation exist under certain conditions. Contents 1. Introduction 1 2. Banach spaces 2 3. Weak topology, weak star topology and reflexivity 6 4. Lower semicontinuity 11 5. Hilbert spaces 13 6. Sobolev spaces 19 References 21 1. Introduction We will discuss the following problem in this paper: let Ω be an open and connected subset in R and f be an L2 function on Ω, is there a solution to Poisson's equation (1) −∆u = f? From elementary partial differential equations class, we know if Ω = R, we can solve Poisson's equation using the fundamental solution to Laplace's equation. However, if we just take Ω to be an open and connected set, the above method is no longer useful. In addition, for arbitrary Ω and f, a C2 solution does not always exist. Therefore, instead of finding a strong solution, i.e., a C2 function which satisfies (1), we integrate (1) against a test function φ (a test function is a Date: September 28, 2016. 1 2 YI WANG smooth function compactly supported in Ω), integrate by parts, and arrive at the equation Z Z 1 (2) rurφ = fφ, 8φ 2 Cc (Ω): Ω Ω So intuitively we want to find a function which satisfies (2) for all test functions and this is the place where Hilbert spaces come into play. -
Arxiv:1908.01212V3 [Math.CT] 3 Nov 2020 Step, One Needs to Apply a For-Loop to Divide a Matrix Into Blocks
Typing Tensor Calculus in 2-Categories Fatimah Ahmadi Department of Computer Science University of Oxford November 4, 2020 Abstract We introduce semiadditive 2-categories, 2-categories with binary 2- biproducts and a zero object, as a suitable framework for typing tensor calculus. Tensors are the generalization of matrices, whose components have more than two indices. 1 Introduction Linear algebra is the primary mathematical toolbox for quantum physicists. Categorical quantum mechanics re-evaluates this toolbox by expressing each tool in the categorical language and leveraging the power of graphical calculus accompanying monoidal categories to gain an alternative insight into quantum features. In the categorical description of quantum mechanics, everything is typed in FHilb; the category whose objects are finite dimensional Hilbert spaces and morphisms are linear maps/matrices. In this category, Hilbert spaces associated with physical systems are typed as objects, and processes between systems as morphisms. MacLane [7] proposed the idea of typing matrices as morphisms while intro- ducing semiadditive categories(categories with well-behaved additions between objects). This line of research, further, has been explored by Macedo and Oliveira in the pursuit of avoiding the cumbersome indexed-based operations of matrices [6]. They address the quest for shifting the traditional perspective of formal methods in software development[10]. To observe why index-level operations do not offer an optimal approach, take the divide-and-conquer algorithm to multiplicate two matrices. In each arXiv:1908.01212v3 [math.CT] 3 Nov 2020 step, one needs to apply a for-loop to divide a matrix into blocks. While the division happens automatically if one takes matrices whose sources or targets are biproducts of objects. -
UNIVERSITY of CALIFORNIA RIVERSIDE the Grothendieck
UNIVERSITY OF CALIFORNIA RIVERSIDE The Grothendieck Construction in Categorical Network Theory A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Mathematics by Joseph Patrick Moeller December 2020 Dissertation Committee: Dr. John C. Baez, Chairperson Dr. Wee Liang Gan Dr. Carl Mautner Copyright by Joseph Patrick Moeller 2020 The Dissertation of Joseph Patrick Moeller is approved: Committee Chairperson University of California, Riverside Acknowledgments First of all, I owe all of my achievements to my wife, Paola. I couldn't have gotten here without my parents: Daniel, Andrea, Tonie, Maria, and Luis, or my siblings: Danielle, Anthony, Samantha, David, and Luis. I would like to thank my advisor, John Baez, for his support, dedication, and his unique and brilliant style of advising. I could not have become the researcher I am under another's instruction. I would also like to thank Christina Vasilakopoulou, whose kindness, energy, and expertise cultivated a deeper appreciation of category theory in me. My expe- rience was also greatly enriched by my academic siblings: Daniel Cicala, Kenny Courser, Brandon Coya, Jason Erbele, Jade Master, Franciscus Rebro, and Christian Williams, and by my cohort: Justin Davis, Ethan Kowalenko, Derek Lowenberg, Michel Manrique, and Michael Pierce. I would like to thank the UCR math department. Professors from whom I learned a ton of algebra, topology, and category theory include Julie Bergner, Vyjayanthi Chari, Wee-Liang Gan, Jos´eGonzalez, Jacob Greenstein, Carl Mautner, Reinhard Schultz, and Steffano Vidussi. Special thanks goes to the department chair Yat-Sun Poon, as well as Margarita Roman, Randy Morgan, and James Marberry, and many others who keep the whole thing together. -
Hilbert Space Methods for Partial Differential Equations
Hilbert Space Methods for Partial Differential Equations R. E. Showalter Electronic Journal of Differential Equations Monograph 01, 1994. i Preface This book is an outgrowth of a course which we have given almost pe- riodically over the last eight years. It is addressed to beginning graduate students of mathematics, engineering, and the physical sciences. Thus, we have attempted to present it while presupposing a minimal background: the reader is assumed to have some prior acquaintance with the concepts of “lin- ear” and “continuous” and also to believe L2 is complete. An undergraduate mathematics training through Lebesgue integration is an ideal background but we dare not assume it without turning away many of our best students. The formal prerequisite consists of a good advanced calculus course and a motivation to study partial differential equations. A problem is called well-posed if for each set of data there exists exactly one solution and this dependence of the solution on the data is continuous. To make this precise we must indicate the space from which the solution is obtained, the space from which the data may come, and the correspond- ing notion of continuity. Our goal in this book is to show that various types of problems are well-posed. These include boundary value problems for (stationary) elliptic partial differential equations and initial-boundary value problems for (time-dependent) equations of parabolic, hyperbolic, and pseudo-parabolic types. Also, we consider some nonlinear elliptic boundary value problems, variational or uni-lateral problems, and some methods of numerical approximation of solutions. We briefly describe the contents of the various chapters. -
3. Hilbert Spaces
3. Hilbert spaces In this section we examine a special type of Banach spaces. We start with some algebraic preliminaries. Definition. Let K be either R or C, and let Let X and Y be vector spaces over K. A map φ : X × Y → K is said to be K-sesquilinear, if • for every x ∈ X, then map φx : Y 3 y 7−→ φ(x, y) ∈ K is linear; • for every y ∈ Y, then map φy : X 3 y 7−→ φ(x, y) ∈ K is conjugate linear, i.e. the map X 3 x 7−→ φy(x) ∈ K is linear. In the case K = R the above properties are equivalent to the fact that φ is bilinear. Remark 3.1. Let X be a vector space over C, and let φ : X × X → C be a C-sesquilinear map. Then φ is completely determined by the map Qφ : X 3 x 7−→ φ(x, x) ∈ C. This can be see by computing, for k ∈ {0, 1, 2, 3} the quantity k k k k k k k Qφ(x + i y) = φ(x + i y, x + i y) = φ(x, x) + φ(x, i y) + φ(i y, x) + φ(i y, i y) = = φ(x, x) + ikφ(x, y) + i−kφ(y, x) + φ(y, y), which then gives 3 1 X (1) φ(x, y) = i−kQ (x + iky), ∀ x, y ∈ X. 4 φ k=0 The map Qφ : X → C is called the quadratic form determined by φ. The identity (1) is referred to as the Polarization Identity. -
An Aop Approach to Typed Linear Algebra
An AoP approach to typed linear algebra J.N. Oliveira (joint work with Hugo Macedo) Dept. Inform´atica, Universidade do Minho Braga, Portugal IFIP WG2.1 meeting #65 27th January 2010 Braga, Portugal Motivation Matrices = arrows Abelian category Abide laws Divide & conquer Vectorization References Context and Motivation • The advent of on-chip parallelism poses many challenges to current programming languages. • Traditional approaches (compiler + hand-coded optimization are giving place to trendy DSL-based generative techniques. • In areas such as scientific computing, image/video processing, the bulk of the work performed by so-called kernel functions. • Examples of kernels are matrix-matrix multiplication (MMM), the discrete Fourier transform (DFT), etc. • Kernel optimization is becoming very difficult due to the complexity of current computing platforms. Motivation Matrices = arrows Abelian category Abide laws Divide & conquer Vectorization References Teaching computers to write fast numerical code In the Spiral Group (CMU), a DSL has been defined (OL) (Franchetti et al., 2009) to specify kernels in a data-independent way. • Divide-and-conquer algorithms are described as OL breakdown rules. • By recursively applying these rules a space of algorithms for a desired kernel can be generated. Rationale behind Spiral: • Target imperative code is too late for numeric processing kernel optimization. • Such optimization can be elegantly and efficiently performed well above in the design chain once the maths themselves are expressed in an index-free style. Motivation Matrices = arrows Abelian category Abide laws Divide & conquer Vectorization References Starting point Synergy: • Parallel between the pointfree notation of OL and relational algebra (relations are Boolean matrices) • Rich calculus of algebraic rules. -
Notes for Functional Analysis
Notes for Functional Analysis Wang Zuoqin (typed by Xiyu Zhai) November 13, 2015 1 Lecture 18 1.1 Characterizations of reflexive spaces Recall that a Banach space X is reflexive if the inclusion X ⊂ X∗∗ is a Banach space isomorphism. The following theorem of Kakatani give us a very useful creteria of reflexive spaces: Theorem 1.1. A Banach space X is reflexive iff the closed unit ball B = fx 2 X : kxk ≤ 1g is weakly compact. Proof. First assume X is reflexive. Then B is the closed unit ball in X∗∗. By the Banach- Alaoglu theorem, B is compact with respect to the weak-∗ topology of X∗∗. But by definition, in this case the weak-∗ topology on X∗∗ coincides with the weak topology on X (since both are the weakest topology on X = X∗∗ making elements in X∗ continuous). So B is compact with respect to the weak topology on X. Conversly suppose B is weakly compact. Let ι : X,! X∗∗ be the canonical inclusion map that sends x to evx. Then we've seen that ι preserves the norm, and thus is continuous (with respect to the norm topologies). By PSet 8-1 Problem 3, ∗∗ ι : X(weak) ,! X(weak) is continuous. Since the weak-∗ topology on X∗∗ is weaker than the weak topology on X∗∗ (since X∗∗∗ = (X∗)∗∗ ⊃ X∗. OH MY GOD!) we thus conclude that ∗∗ ι : X(weak) ,! X(weak-∗) is continuous. But by assumption B ⊂ X(weak) is compact, so the image ι(B) is compact in ∗∗ X(weak-∗). In particular, ι(B) is weak-∗ closed. -
The Riesz Representation Theorem
The Riesz Representation Theorem MA 466 Kurt Bryan Let H be a Hilbert space over lR or Cl, and T a bounded linear functional on H (a bounded operator from H to the field, lR or Cl, over which H is defined). The following is called the Riesz Representation Theorem: Theorem 1 If T is a bounded linear functional on a Hilbert space H then there exists some g 2 H such that for every f 2 H we have T (f) =< f; g > : Moreover, kT k = kgk (here kT k denotes the operator norm of T , while kgk is the Hilbert space norm of g. Proof: Let's assume that H is separable for now. It's not really much harder to prove for any Hilbert space, but the separable case makes nice use of the ideas we developed regarding Fourier analysis. Also, let's just work over lR. Since H is separable we can choose an orthonormal basis ϕj, j ≥ 1, for 2 H. Let T be a bounded linear functionalP and set aj = T (ϕj). Choose f H, n let cj =< f; ϕj >, and define fn = j=1 cjϕj. Since the ϕj form a basis we know that kf − fnk ! 0 as n ! 1. Since T is linear we have Xn T (fn) = ajcj: (1) j=1 Since T is bounded, say with norm kT k < 1, we have jT (f) − T (fn)j ≤ kT kkf − fnk: (2) Because kf − fnk ! 0 as n ! 1 we conclude from equations (1) and (2) that X1 T (f) = lim T (fn) = ajcj: (3) n!1 j=1 In fact, the sequence aj must itself be square-summable. -
Double Cross Biproduct and Bi-Cycle Bicrossproduct Lie Bialgebras
Ashdin Publishing Journal of Generalized Lie Theory and Applications Vol. 4 (2010), Article ID S090602, 16 pages doi:10.4303/jglta/S090602 Double cross biproduct and bi-cycle bicrossproduct Lie bialgebras Tao ZHANG College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, Henan Province, China Email: [email protected] Abstract We construct double cross biproduct and bi-cycle bicrossproduct Lie bialgebras from braided Lie bialgebras. The main results generalize Majid's matched pair of Lie algebras and Drinfeld's quantum double and Masuoka's cross product Lie bialgebras. 2000 MSC: 17B62, 18D35 1 Introduction As an infinitesimal or semiclassical structures underlying the theory of quantum groups, the notion of Lie bialgebras was introduced by Drinfeld in his remarkable report [3], where he also introduced the double Lie bialgebra D(g) as an important construction. Some years later, the theory of matched pairs of Lie algebras (g; m) was introduced by Majid in [4]. Its bicrossed product (or double cross sum) m ./g is more general than Drinfeld's classical double D(g) because g and m need not have the same dimension and the actions need not be strictly coadjoint ones. Since then it was found that many other structures in Hopf algebras can be constructed in the infinitesimal setting, see [5] and the references cited therein. Also in [6], Majid in- troduced the concept of braided Lie bialgebras and proved the bosonisation theorem (see Theorem 4.4) associating braided Lie bialgebras to ordinary Lie bialgebras. Examples of braided Lie bialgebras were also given there. On the other hand, there is a close relation between extension theory and cross product Lie bialgebras, see Masuoka [7].