Nonlocal Exterior Calculus on Riemannian Manifolds
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Arxiv:1507.07356V2 [Math.AP]
TEN EQUIVALENT DEFINITIONS OF THE FRACTIONAL LAPLACE OPERATOR MATEUSZ KWAŚNICKI Abstract. This article discusses several definitions of the fractional Laplace operator ( ∆)α/2 (α (0, 2)) in Rd (d 1), also known as the Riesz fractional derivative − ∈ ≥ operator, as an operator on Lebesgue spaces L p (p [1, )), on the space C0 of ∈ ∞ continuous functions vanishing at infinity and on the space Cbu of bounded uniformly continuous functions. Among these definitions are ones involving singular integrals, semigroups of operators, Bochner’s subordination and harmonic extensions. We collect and extend known results in order to prove that all these definitions agree: on each of the function spaces considered, the corresponding operators have common domain and they coincide on that common domain. 1. Introduction We consider the fractional Laplace operator L = ( ∆)α/2 in Rd, with α (0, 2) and d 1, 2, ... Numerous definitions of L can be found− in− literature: as a Fourier∈ multiplier with∈{ symbol} ξ α, as a fractional power in the sense of Bochner or Balakrishnan, as the inverse of the−| Riesz| potential operator, as a singular integral operator, as an operator associated to an appropriate Dirichlet form, as an infinitesimal generator of an appropriate semigroup of contractions, or as the Dirichlet-to-Neumann operator for an appropriate harmonic extension problem. Equivalence of these definitions for sufficiently smooth functions is well-known and easy. The purpose of this article is to prove that, whenever meaningful, all these definitions are equivalent in the Lebesgue space L p for p [1, ), ∈ ∞ in the space C0 of continuous functions vanishing at infinity, and in the space Cbu of bounded uniformly continuous functions. -
Combinatorial Laplacian and Rank Aggregation
Combinatorial Laplacian and Rank Aggregation Combinatorial Laplacian and Rank Aggregation Yuan Yao Stanford University ICIAM, Z¨urich,July 16–20, 2007 Joint work with Lek-Heng Lim Combinatorial Laplacian and Rank Aggregation Outline 1 Two Motivating Examples 2 Reflections on Ranking Ordinal vs. Cardinal Global, Local, vs. Pairwise 3 Discrete Exterior Calculus and Combinatorial Laplacian Discrete Exterior Calculus Combinatorial Laplacian Operator 4 Hodge Theory Cyclicity of Pairwise Rankings Consistency of Pairwise Rankings 5 Conclusions and Future Work Combinatorial Laplacian and Rank Aggregation Two Motivating Examples Example I: Customer-Product Rating Example (Customer-Product Rating) m×n m-by-n customer-product rating matrix X ∈ R X typically contains lots of missing values (say ≥ 90%). The first-order statistics, mean score for each product, might suffer from most customers just rate a very small portion of the products different products might have different raters, whence mean scores involve noise due to arbitrary individual rating scales Combinatorial Laplacian and Rank Aggregation Two Motivating Examples From 1st Order to 2nd Order: Pairwise Rankings The arithmetic mean of score difference between product i and j over all customers who have rated both of them, P k (Xkj − Xki ) gij = , #{k : Xki , Xkj exist} is translation invariant. If all the scores are positive, the geometric mean of score ratio over all customers who have rated both i and j, !1/#{k:Xki ,Xkj exist} Y Xkj gij = , Xki k is scale invariant. Combinatorial Laplacian and Rank Aggregation Two Motivating Examples More invariant Define the pairwise ranking gij as the probability that product j is preferred to i in excess of a purely random choice, 1 g = Pr{k : X > X } − . -
Laplacians in Geometric Analysis
LAPLACIANS IN GEOMETRIC ANALYSIS Syafiq Johar syafi[email protected] Contents 1 Trace Laplacian 1 1.1 Connections on Vector Bundles . .1 1.2 Local and Explicit Expressions . .2 1.3 Second Covariant Derivative . .3 1.4 Curvatures on Vector Bundles . .4 1.5 Trace Laplacian . .5 2 Harmonic Functions 6 2.1 Gradient and Divergence Operators . .7 2.2 Laplace-Beltrami Operator . .7 2.3 Harmonic Functions . .8 2.4 Harmonic Maps . .8 3 Hodge Laplacian 9 3.1 Exterior Derivatives . .9 3.2 Hodge Duals . 10 3.3 Hodge Laplacian . 12 4 Hodge Decomposition 13 4.1 De Rham Cohomology . 13 4.2 Hodge Decomposition Theorem . 14 5 Weitzenb¨ock and B¨ochner Formulas 15 5.1 Weitzenb¨ock Formula . 15 5.1.1 0-forms . 15 5.1.2 k-forms . 15 5.2 B¨ochner Formula . 17 1 Trace Laplacian In this section, we are going to present a notion of Laplacian that is regularly used in differential geometry, namely the trace Laplacian (also called the rough Laplacian or connection Laplacian). We recall the definition of connection on vector bundles which allows us to take the directional derivative of vector bundles. 1.1 Connections on Vector Bundles Definition 1.1 (Connection). Let M be a differentiable manifold and E a vector bundle over M. A connection or covariant derivative at a point p 2 M is a map D : Γ(E) ! Γ(T ∗M ⊗ E) 1 with the properties for any V; W 2 TpM; σ; τ 2 Γ(E) and f 2 C (M), we have that DV σ 2 Ep with the following properties: 1. -
1.2 Topological Tensor Calculus
PH211 Physical Mathematics Fall 2019 1.2 Topological tensor calculus 1.2.1 Tensor fields Finite displacements in Euclidean space can be represented by arrows and have a natural vector space structure, but finite displacements in more general curved spaces, such as on the surface of a sphere, do not. However, an infinitesimal neighborhood of a point in a smooth curved space1 looks like an infinitesimal neighborhood of Euclidean space, and infinitesimal displacements dx~ retain the vector space structure of displacements in Euclidean space. An infinitesimal neighborhood of a point can be infinitely rescaled to generate a finite vector space, called the tangent space, at the point. A vector lives in the tangent space of a point. Note that vectors do not stretch from one point to vector tangent space at p p space Figure 1.2.1: A vector in the tangent space of a point. another, and vectors at different points live in different tangent spaces and so cannot be added. For example, rescaling the infinitesimal displacement dx~ by dividing it by the in- finitesimal scalar dt gives the velocity dx~ ~v = (1.2.1) dt which is a vector. Similarly, we can picture the covector rφ as the infinitesimal contours of φ in a neighborhood of a point, infinitely rescaled to generate a finite covector in the point's cotangent space. More generally, infinitely rescaling the neighborhood of a point generates the tensor space and its algebra at the point. The tensor space contains the tangent and cotangent spaces as a vector subspaces. A tensor field is something that takes tensor values at every point in a space. -
Physics-Compatible Discretization Techniques on Single and Dual Grids, with Application to the Poisson Equation of Volume Forms
Physics-compatible discretization techniques on single and dual grids, with application to the Poisson equation of volume forms Artur Palhaa, Pedro Pinto Rebelob, Rene´ Hiemstrab, Jasper Kreeftc, Marc Gerritsmab,∗ aDelft University of Technology, Faculty of Aerospace Engineering, Wind Energy Group P.O. Box 5058, 2600 GB Delft, The Netherlands bDelft University of Technology, Faculty of Aerospace Engineering, Aerodynamics Group P.O. Box 5058, 2600 GB Delft, The Netherlands cShell Global Solutions, The Netherlands Abstract This paper introduces the basic concepts for physics-compatible discretization techniques. The paper gives a clear distinction between vectors and forms. Based on the difference between forms and pseudo-forms and the ?-operator which switches between the two, a dual grid description and a single grid description are presented. The dual grid method resembles a staggered finite volume method, whereas the single grid approach shows a strong resemblance with a finite element method. Both approaches are compared for the Poisson equation for volume forms. Keywords: Mimetic discretization, differential forms, single grid, dual grid, geometric flexibility. 1. INTRODUCTION Mimetic methods aim to preserve essential physical/mathematical structures in a discrete setting. Many of such structures are topological, i.e. independent of metric, and involve integral relations. Since integration will play an important role and integration of differential forms is a metric-free operation, we will work with differential forms. Formally, differentials forms are linear functionals on multi-vectors, but Flanders, [17, p.1], refers to them as ‘things which occur under integral signs’. Such would not be the case if we were to use vectors, because integration of vector quantities is a metric operation. -
Curl, Divergence and Laplacian
Curl, Divergence and Laplacian What to know: 1. The definition of curl and it two properties, that is, theorem 1, and be able to predict qualitatively how the curl of a vector field behaves from a picture. 2. The definition of divergence and it two properties, that is, if div F~ 6= 0 then F~ can't be written as the curl of another field, and be able to tell a vector field of clearly nonzero,positive or negative divergence from the picture. 3. Know the definition of the Laplace operator 4. Know what kind of objects those operator take as input and what they give as output. The curl operator Let's look at two plots of vector fields: Figure 1: The vector field Figure 2: The vector field h−y; x; 0i: h1; 1; 0i We can observe that the second one looks like it is rotating around the z axis. We'd like to be able to predict this kind of behavior without having to look at a picture. We also promised to find a criterion that checks whether a vector field is conservative in R3. Both of those goals are accomplished using a tool called the curl operator, even though neither of those two properties is exactly obvious from the definition we'll give. Definition 1. Let F~ = hP; Q; Ri be a vector field in R3, where P , Q and R are continuously differentiable. We define the curl operator: @R @Q @P @R @Q @P curl F~ = − ~i + − ~j + − ~k: (1) @y @z @z @x @x @y Remarks: 1. -
Statistical Ranking and Combinatorial Hodge Theory
STATISTICAL RANKING AND COMBINATORIAL HODGE THEORY XIAOYE JIANG, LEK-HENG LIM, YUAN YAO, AND YINYU YE Abstract. We propose a number of techniques for obtaining a global ranking from data that may be incomplete and imbalanced | characteristics that are almost universal to modern datasets coming from e-commerce and internet applications. We are primarily interested in cardinal data based on scores or ratings though our methods also give specific insights on ordinal data. From raw ranking data, we construct pairwise rankings, represented as edge flows on an appropriate graph. Our statistical ranking method exploits the graph Helmholtzian, which is the graph theoretic analogue of the Helmholtz operator or vector Laplacian, in much the same way the graph Laplacian is an ana- logue of the Laplace operator or scalar Laplacian. We shall study the graph Helmholtzian using combinatorial Hodge theory, which provides a way to un- ravel ranking information from edge flows. In particular, we show that every edge flow representing pairwise ranking can be resolved into two orthogonal components, a gradient flow that represents the l2-optimal global ranking and a divergence-free flow (cyclic) that measures the validity of the global ranking obtained | if this is large, then it indicates that the data does not have a good global ranking. This divergence-free flow can be further decomposed or- thogonally into a curl flow (locally cyclic) and a harmonic flow (locally acyclic but globally cyclic); these provides information on whether inconsistency in the ranking data arises locally or globally. When applied to statistical ranking problems, Hodge decomposition sheds light on whether a given dataset may be globally ranked in a meaningful way or if the data is inherently inconsistent and thus could not have any reasonable global ranking; in the latter case it provides information on the nature of the inconsistencies. -
Space-Time Finite-Element Exterior Calculus and Variational Discretizations of Gauge Field Theories
21st International Symposium on Mathematical Theory of Networks and Systems July 7-11, 2014. Groningen, The Netherlands Space-Time Finite-Element Exterior Calculus and Variational Discretizations of Gauge Field Theories Joe Salamon1, John Moody2, and Melvin Leok3 Abstract— Many gauge field theories can be described using a the space-time covariant (or multi-Dirac) perspective can be multisymplectic Lagrangian formulation, where the Lagrangian found in [1]. density involves space-time differential forms. While there has An example of a gauge symmetry arises in Maxwell’s been much work on finite-element exterior calculus for spatial and tensor product space-time domains, there has been less equations of electromagnetism, which can be expressed in done from the perspective of space-time simplicial complexes. terms of the scalar potential φ, the vector potential A, the One critical aspect is that the Hodge star is now taken with electric field E, and the magnetic field B. respect to a pseudo-Riemannian metric, and this is most natu- @A @ rally expressed in space-time adapted coordinates, as opposed E = −∇φ − ; r2φ + (r · A) = 0; to the barycentric coordinates that the Whitney forms (and @t @t their higher-degree generalizations) are typically expressed in @φ terms of. B = r × A; A + r r · A + = 0; @t We introduce a novel characterization of Whitney forms and their Hodge dual with respect to a pseudo-Riemannian metric where is the d’Alembert (or wave) operator. The following that is independent of the choice of coordinates, and then apply gauge transformation leaves the equations invariant, it to a variational discretization of the covariant formulation of Maxwell’s equations. -
The Language of Differential Forms
Appendix A The Language of Differential Forms This appendix—with the only exception of Sect.A.4.2—does not contain any new physical notions with respect to the previous chapters, but has the purpose of deriving and rewriting some of the previous results using a different language: the language of the so-called differential (or exterior) forms. Thanks to this language we can rewrite all equations in a more compact form, where all tensor indices referred to the diffeomorphisms of the curved space–time are “hidden” inside the variables, with great formal simplifications and benefits (especially in the context of the variational computations). The matter of this appendix is not intended to provide a complete nor a rigorous introduction to this formalism: it should be regarded only as a first, intuitive and oper- ational approach to the calculus of differential forms (also called exterior calculus, or “Cartan calculus”). The main purpose is to quickly put the reader in the position of understanding, and also independently performing, various computations typical of a geometric model of gravity. The readers interested in a more rigorous discussion of differential forms are referred, for instance, to the book [22] of the bibliography. Let us finally notice that in this appendix we will follow the conventions introduced in Chap. 12, Sect. 12.1: latin letters a, b, c,...will denote Lorentz indices in the flat tangent space, Greek letters μ, ν, α,... tensor indices in the curved manifold. For the matter fields we will always use natural units = c = 1. Also, unless otherwise stated, in the first three Sects. -
Differential Forms Diff Geom II, WS 2015/16
J.M. Sullivan, TU Berlin B: Differential Forms Diff Geom II, WS 2015/16 B. DIFFERENTIAL FORMS instance, if S has k elements this gives a k-dimensional vector space with S as basis. We have already seen one-forms (covector fields) on a Given vector spaces V and W, let F be the free vector space over the set V × W. (This consists of formal sums manifold. In general, a k-form is a field of alternating k- P linear forms on the tangent spaces of a manifold. Forms ai(vi, wi) but ignores all the structure we have on the set are the natural objects for integration: a k-form can be in- V × W.) Now let R ⊂ F be the linear subspace spanned by tegrated over an oriented k-submanifold. We start with ten- all elements of the form: sor products and the exterior algebra of multivectors. (v + v0, w) − (v, w) − (v0, w), (v, w + w0) − (v, w) − (v, w0), (av, w) − a(v, w), (v, aw) − a(v, w). B1. Tensor products These correspond of course to the bilinearity conditions Recall that, if V, W and X are vector spaces, then a map we started with. The quotient vector space F/R will be the b: V × W → X is called bilinear if tensor product V ⊗ W. We have started with all possible v ⊗ w as generators and thrown in just enough relations to b(v + v0, w) = b(v, w) + b(v0, w), make the map (v, w) 7→ v ⊗ w be bilinear. b(v, w + w0) = b(v, w) + b(v, w0), The tensor product is commutative: there is a natural linear isomorphism V⊗W → W⊗V such that v⊗w 7→ w⊗v. -
Vector Calculus and Differential Forms with Applications To
Vector Calculus and Differential Forms with Applications to Electromagnetism Sean Roberson May 7, 2015 PREFACE This paper is written as a final project for a course in vector analysis, taught at Texas A&M University - San Antonio in the spring of 2015 as an independent study course. Students in mathematics, physics, engineering, and the sciences usually go through a sequence of three calculus courses before go- ing on to differential equations, real analysis, and linear algebra. In the third course, traditionally reserved for multivariable calculus, stu- dents usually learn how to differentiate functions of several variable and integrate over general domains in space. Very rarely, as was my case, will professors have time to cover the important integral theo- rems using vector functions: Green’s Theorem, Stokes’ Theorem, etc. In some universities, such as UCSD and Cornell, honors students are able to take an accelerated calculus sequence using the text Vector Cal- culus, Linear Algebra, and Differential Forms by John Hamal Hubbard and Barbara Burke Hubbard. Here, students learn multivariable cal- culus using linear algebra and real analysis, and then they generalize familiar integral theorems using the language of differential forms. This paper was written over the course of one semester, where the majority of the book was covered. Some details, such as orientation of manifolds, topology, and the foundation of the integral were skipped to save length. The paper should still be readable by a student with at least three semesters of calculus, one course in linear algebra, and one course in real analysis - all at the undergraduate level. -
Arxiv:1905.00851V1 [Cs.CV] 2 May 2019 Sibly Nonconvex) Continuous Function on X × Y × R That Is Polyconvex (See Def.2) in the Jacobian Matrix Ξ
Lifting Vectorial Variational Problems: A Natural Formulation based on Geometric Measure Theory and Discrete Exterior Calculus Thomas Möllenhoff and Daniel Cremers Technical University of Munich {thomas.moellenhoff,cremers}@tum.de Abstract works [30, 31, 29, 55, 39,9, 10, 14] we consider the way less explored continuous (infinite-dimensional) setting. Numerous tasks in imaging and vision can be formu- Our motivation partly stems from the fact that formula- lated as variational problems over vector-valued maps. We tions in function space are very general and admit a variety approach the relaxation and convexification of such vecto- of discretizations. Finite difference discretizations of con- rial variational problems via a lifting to the space of cur- tinuous relaxations often lead to models that are reminis- rents. To that end, we recall that functionals with poly- cent of MRFs [70], while piecewise-linear approximations convex Lagrangians can be reparametrized as convex one- are related to discrete-continuous MRFs [71], see [17, 40]. homogeneous functionals on the graph of the function. More recently, for the Kantorovich relaxation in optimal This leads to an equivalent shape optimization problem transport, approximations with deep neural networks were over oriented surfaces in the product space of domain and considered and achieved promising performance, for exam- codomain. A convex formulation is then obtained by relax- ple in generative modeling [2, 54]. ing the search space from oriented surfaces to more gen- We further argue that fractional (non-integer) solutions eral currents. We propose a discretization of the resulting to a careful discretization of the continuous model can infinite-dimensional optimization problem using Whitney implicitly approximate an “integer” continuous solution.