A Geometric Formulation of Linear Elasticity Based on Discrete Exterior Calculus
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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. -
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. -
Discrete Calculus with Cubic Cells on Discrete Manifolds 3
DISCRETE CALCULUS WITH CUBIC CELLS ON DISCRETE MANIFOLDS LEONARDO DE CARLO Abstract. This work is thought as an operative guide to ”discrete exterior calculus” (DEC), but at the same time with a rigorous exposition. We present a version of (DEC) on ”cubic” cell, defining it for discrete manifolds. An example of how it works, it is done on the discrete torus, where usual Gauss and Stokes theorems are recovered. Keywords: Discrete Calculus, discrete differential geometry AMS 2010 Subject Classification: 97N70 1. Introduction Discrete exterior calculus (DEC) is motivated by potential applications in com- putational methods for field theories (elasticity, fluids, electromagnetism) and in areas of computer vision/graphics, for these applications see for example [DDT15], [DMTS14] or [BSSZ08]. DEC is developing as an alternative approach for com- putational science to the usual discretizing process from the continuous theory. It considers the discrete mesh as the only thing given and develops an entire calcu- lus using only discrete combinatorial and geometric operations. The derivations may require that the objects on the discrete mesh, but not the mesh itself, are interpolated as if they come from a continuous model. Therefore DEC could have interesting applications in fields where there isn’t any continuous underlying struc- ture as Graph theory [GP10] or problems that are inherently discrete since they are defined on a lattice [AO05] and it can stand in its own right as theory that parallels the continuous one. The language of DEC is founded on the concept of discrete differential form, this characteristic allows to preserve in the discrete con- text some of the usual geometric and topological structures of continuous models, arXiv:1906.07054v2 [cs.DM] 8 Jul 2019 in particular the Stokes’theorem dω = ω. -
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. -
High Order Gradient, Curl and Divergence Conforming Spaces, with an Application to NURBS-Based Isogeometric Analysis
High order gradient, curl and divergence conforming spaces, with an application to compatible NURBS-based IsoGeometric Analysis R.R. Hiemstraa, R.H.M. Huijsmansa, M.I.Gerritsmab aDepartment of Marine Technology, Mekelweg 2, 2628CD Delft bDepartment of Aerospace Technology, Kluyverweg 2, 2629HT Delft Abstract Conservation laws, in for example, electromagnetism, solid and fluid mechanics, allow an exact discrete representation in terms of line, surface and volume integrals. We develop high order interpolants, from any basis that is a partition of unity, that satisfy these integral relations exactly, at cell level. The resulting gradient, curl and divergence conforming spaces have the propertythat the conservationlaws become completely independent of the basis functions. This means that the conservation laws are exactly satisfied even on curved meshes. As an example, we develop high ordergradient, curl and divergence conforming spaces from NURBS - non uniform rational B-splines - and thereby generalize the compatible spaces of B-splines developed in [1]. We give several examples of 2D Stokes flow calculations which result, amongst others, in a point wise divergence free velocity field. Keywords: Compatible numerical methods, Mixed methods, NURBS, IsoGeometric Analyis Be careful of the naive view that a physical law is a mathematical relation between previously defined quantities. The situation is, rather, that a certain mathematical structure represents a given physical structure. Burke [2] 1. Introduction In deriving mathematical models for physical theories, we frequently start with analysis on finite dimensional geometric objects, like a control volume and its bounding surfaces. We assign global, ’measurable’, quantities to these different geometric objects and set up balance statements. -
Discrete Mechanics and Optimal Control: an Analysis ∗
ESAIM: COCV 17 (2011) 322–352 ESAIM: Control, Optimisation and Calculus of Variations DOI: 10.1051/cocv/2010012 www.esaim-cocv.org DISCRETE MECHANICS AND OPTIMAL CONTROL: AN ANALYSIS ∗ Sina Ober-Blobaum¨ 1, Oliver Junge 2 and Jerrold E. Marsden3 Abstract. The optimal control of a mechanical system is of crucial importance in many application areas. Typical examples are the determination of a time-minimal path in vehicle dynamics, a minimal energy trajectory in space mission design, or optimal motion sequences in robotics and biomechanics. In most cases, some sort of discretization of the original, infinite-dimensional optimization problem has to be performed in order to make the problem amenable to computations. The approach proposed in this paper is to directly discretize the variational description of the system’s motion. The resulting optimization algorithm lets the discrete solution directly inherit characteristic structural properties Rapide Not from the continuous one like symmetries and integrals of the motion. We show that the DMOC (Discrete Mechanics and Optimal Control) approach is equivalent to a finite difference discretization of Hamilton’s equations by a symplectic partitioned Runge-Kutta scheme and employ this fact in order to give a proof of convergence. The numerical performance of DMOC and its relationship to other existing optimal control methods are investigated. Mathematics Subject Classification. 49M25, 49N99, 65K10. Received October 8, 2008. Revised September 17, 2009. Highlight Published online March 31, 2010. Introduction In order to solve optimal control problems for mechanical systems, this paper links two important areas of research: optimal control and variational mechanics. The motivation for combining these fields of investigation is twofold. -
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. -
On a Class of Einstein Space-Time Manifolds
Publ. Math. Debrecen 67/3-4 (2005), 471–480 On a class of Einstein space-time manifolds By ADELA MIHAI (Bucharest) and RADU ROSCA (Paris) Abstract. We deal with a general space-time (M,g) with usual differen- tiability conditions and hyperbolic metric g of index 1, which carries 3 skew- symmetric Killing vector fields X, Y , Z having as generative the unit time-like vector field e of the hyperbolic metric g. It is shown that such a space-time (M,g) is an Einstein manifold of curvature −1, which is foliated by space-like hypersur- faces Ms normal to e and the immersion x : Ms → M is pseudo-umbilical. In addition, it is proved that the vector fields X, Y , Z and e are exterior concur- rent vector fields and X, Y , Z define a commutative Killing triple, M admits a Lorentzian transformation which is in an orthocronous Lorentz group and the distinguished spatial 3-form of M is a relatively integral invariant of the vector fields X, Y and Z. 0. Introduction Let (M,g) be a general space-time with usual differentiability condi- tions and hyperbolic metric g of index 1. We assume in this paper that (M,g) carries 3 skew-symmetric Killing vector fields (abbr. SSK) X, Y , Z having as generative the unit time-like vector field e of the hyperbolic metric g (see[R1],[MRV].Therefore,if∇ is the Levi–Civita connection and ∧ means the wedge product of vector Mathematics Subject Classification: 53C15, 53D15, 53C25. Key words and phrases: space-time, skew-symmetric Killing vector field, exterior con- current vector field, orthocronous Lorentz group. -
Discrete Differential Geometry: an Applied Introduction
DISCRETE DIFFERENTIAL GEOMETRY: AN APPLIED INTRODUCTION Keenan Crane Last updated: April 13, 2020 Contents Chapter 1. Introduction 3 1.1. Disclaimer 6 1.2. Copyright 6 1.3. Acknowledgements 6 Chapter 2. Combinatorial Surfaces 7 2.1. Abstract Simplicial Complex 8 2.2. Anatomy of a Simplicial Complex: Star, Closure, and Link 10 2.3. Simplicial Surfaces 14 2.4. Adjacency Matrices 16 2.5. Halfedge Mesh 17 2.6. Written Exercises 21 2.7. Coding Exercises 26 Chapter 3. A Quick and Dirty Introduction to Differential Geometry 28 3.1. The Geometry of Surfaces 28 3.2. Derivatives and Tangent Vectors 31 3.3. The Geometry of Curves 34 3.4. Curvature of Surfaces 37 3.5. Geometry in Coordinates 41 Chapter 4. A Quick and Dirty Introduction to Exterior Calculus 45 4.1. Exterior Algebra 46 4.2. Examples of Wedge and Star in Rn 52 4.3. Vectors and 1-Forms 54 4.4. Differential Forms and the Wedge Product 58 4.5. Hodge Duality 62 4.6. Differential Operators 67 4.7. Integration and Stokes’ Theorem 73 4.8. Discrete Exterior Calculus 77 Chapter 5. Curvature of Discrete Surfaces 84 5.1. Vector Area 84 5.2. Area Gradient 87 5.3. Volume Gradient 89 5.4. Other Definitions 91 5.5. Gauss-Bonnet 94 5.6. Numerical Tests and Convergence 95 Chapter 6. The Laplacian 100 6.1. Basic Properties 100 6.2. Discretization via FEM 103 6.3. Discretization via DEC 107 1 CONTENTS 2 6.4. Meshes and Matrices 110 6.5. The Poisson Equation 112 6.6. -
VISUALIZING EXTERIOR CALCULUS Contents Introduction 1 1. Exterior
VISUALIZING EXTERIOR CALCULUS GREGORY BIXLER Abstract. Exterior calculus, broadly, is the structure of differential forms. These are usually presented algebraically, or as computational tools to simplify Stokes' Theorem. But geometric objects like forms should be visualized! Here, I present visualizations of forms that attempt to capture their geometry. Contents Introduction 1 1. Exterior algebra 2 1.1. Vectors and dual vectors 2 1.2. The wedge of two vectors 3 1.3. Defining the exterior algebra 4 1.4. The wedge of two dual vectors 5 1.5. R3 has no mixed wedges 6 1.6. Interior multiplication 7 2. Integration 8 2.1. Differential forms 9 2.2. Visualizing differential forms 9 2.3. Integrating differential forms over manifolds 9 3. Differentiation 11 3.1. Exterior Derivative 11 3.2. Visualizing the exterior derivative 13 3.3. Closed and exact forms 14 3.4. Future work: Lie derivative 15 Acknowledgments 16 References 16 Introduction What's the natural way to integrate over a smooth manifold? Well, a k-dimensional manifold is (locally) parametrized by k coordinate functions. At each point we get k tangent vectors, which together form an infinitesimal k-dimensional volume ele- ment. And how should we measure this element? It seems reasonable to ask for a function f taking k vectors, so that • f is linear in each vector • f is zero for degenerate elements (i.e. when vectors are linearly dependent) • f is smooth These are the properties of a differential form. 1 2 GREGORY BIXLER Introducing structure to a manifold usually follows a certain pattern: • Create it in Rn.