The Grassmann Manifold

Total Page:16

File Type:pdf, Size:1020Kb

The Grassmann Manifold The Grassmann Manifold 1. For vector spaces V and W denote by L(V; W ) the vector space of linear maps from V to W . Thus L(Rk; Rn) may be identified with the space Rk£n of k £ n matrices. An injective linear map u : Rk ! V is called a k-frame in V . The set k n GFk;n = fu 2 L(R ; R ): rank(u) = kg of k-frames in Rn is called the Stiefel manifold. Note that the special case k = n is the general linear group: k k GLk = fa 2 L(R ; R ) : det(a) 6= 0g: The set of all k-dimensional (vector) subspaces ¸ ½ Rn is called the Grassmann n manifold of k-planes in R and denoted by GRk;n or sometimes GRk;n(R) or n GRk(R ). Let k ¼ : GFk;n ! GRk;n; ¼(u) = u(R ) denote the map which assigns to each k-frame u the subspace u(Rk) it spans. ¡1 For ¸ 2 GRk;n the fiber (preimage) ¼ (¸) consists of those k-frames which form a basis for the subspace ¸, i.e. for any u 2 ¼¡1(¸) we have ¡1 ¼ (¸) = fu ± a : a 2 GLkg: Hence we can (and will) view GRk;n as the orbit space of the group action GFk;n £ GLk ! GFk;n :(u; a) 7! u ± a: The exercises below will prove the following n£k Theorem 2. The Stiefel manifold GFk;n is an open subset of the set R of all n £ k matrices. There is a unique differentiable structure on the Grassmann manifold GRk;n such that the map ¼ is a submersion. n£k 3. Show that GFk;n is an open subset of R . Hint: For any k-element subset I = fi1 < i2 < ¢ ¢ ¢ < ikg ½ f1; 2; : : : ; ng; n k define a linear projection pI : R ! R by pI (x1; x2; : : : ; xn) = (xi1 ; xi2 ; : : : ; xik ): For u 2 L(Rm; Rn) the matrix m k uI = pI ± u 2 L(R ; R ) is obtained from u be deleting the columns not corresponding to elements of I. Let n£k VI = fu 2 R : det(uI ) 6= 0g: ¡n¢ The k sets VI cover GFk;n. 1 4. For any subset I ½ f1; 2; : : : ; ng of indices let I0 = f1; 2; : : : ; ng n I denote its complement. Let UI = ¼(VI ). Show that ¡1 VI = ¼ (UI ); that the matrix ¡1 k£(n¡k) ÁI (¸) = uI0 uI 2 R is independent of the choice of u 2 ¼¡1(¸) used to define it, and that the map k£(n¡k) ÁI : UI ! R is a bijection. The inverse is defined by ¡1 n ÁI (A) = fx 2 R : xI0 = AxI g where we have written xI = ¼I (x) and xI0 = ¼I0 (x). ¡ ¢ n 1 5. Show that the k charts (ÁI ;UI ) form a C atlas. Hint: The condition A 2 ÁI (UI \ UJ ) holds if and only if a certain submatrix of A is invertible. 6. Show that the map ¼ : GFk;n ! GRk;n is locally trivial. More precisely, show that for each k-element subset I there is a unique smooth map σI : UI ! VI such that ¼ ± σI is the identity map of Ui and σI (UI ) = fu 2 GFk;n : uI = 1kg: Here 1k denotes the k £ k identity matrix.) Then show that the map ΦI : UI £ GLk ! VI defined by ΦI (¸; a) = σI (¸) ± a is a diffeomorphism satisfying ¼ ± ΦI (¸; a) = ¸. 7. Suppose E, M1, M2 are smooth manifolds and that pi : E ! Mi are surjec- tive submersions. Suppose that f : M1 ! M2 is a bijection such that f ±p1 = p2. Show that f is a diffeomorphism. n 8. Suppose that ¸0 is a k dimensional subspace of R and let ¹0 be any (vector n space) complement to ¸0 in R i.e., n R = ¸0 © ¹0: For A 2 L(¸0; ¹0) let Graph(A) = fx + Ax : x 2 ¸0g: n and let ¼0 : R ! ¸0 be the projection along ¹0. Show that the following conditions on k dimensional subspace ¸ of Rn are equivalent: (a) ¸ = Graph(A) for some A 2 L(¸0; ¹0); (b) ¸ is transverse to ¹0 (meaning that ¸ \ ¹0 = f0g); (c) ¼0j¸ is an isomorphism. 2 n 9. Let R = ¸0 © ¹0 be a splitting as in paragraph 8 and let U be the set of all ¸ 2 GRk;n satisfying the equivalent conditions (a), (b), and (c). Define Á : U ! L(¸0; ¹0) by Á¡1(A) = Graph(A): Prove that U is an open dense subset of GRk;n and that Á is a diffeomorphism. 10. The manifold GRk;n is an example of a homogeneous space. Prove this by showing: (1) For each a 2 GLn the induced map GRk;n ! GRk;n : ¸ 7! a(¸) is a diffeomorphism. (2) The map GLn £ GRk;n ! GRk;n :(a; ¸) 7! a(¸) is smooth. 1 (3) For each ¸0 2 GRk;n the map GLn ! GRk;n : a ! a(¸0) is a C surjective map. k Remark 11. If we take ¸0 = R £ 0 in the map a ! a(¸0) the fibers of this map (i.e., inverse images of points) are left cosets a ¢ GLn;k where GLn;k is the k k subgroup of GLn consisting of all a 2 GLn such that a(R £ 0) = R £ 0. Thus we have a bijective correspondence between GRk;n and the space of left cosets GLn=GLn;k: GRk;n ' GLn=GLn;k: (Warning: GLn;k is not a normal subgroup of GLn. Thus GRk;n is not a group in any natural fashion.) ? ? 12. Prove that the map GRk;n ! GRn¡k;n : ¸ ! ¸ (where ¸ denotes the orthogonal complement to ¸ with respect to the usual inner product on Rn) is a C1 diffeomorphism. 3.
Recommended publications
  • Cheap Orthogonal Constraints in Neural Networks: a Simple Parametrization of the Orthogonal and Unitary Group
    Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group Mario Lezcano-Casado 1 David Mart´ınez-Rubio 2 Abstract Recurrent Neural Networks (RNNs). In RNNs the eigenval- ues of the gradient of the recurrent kernel explode or vanish We introduce a novel approach to perform first- exponentially fast with the number of time-steps whenever order optimization with orthogonal and uni- the recurrent kernel does not have unitary eigenvalues (Ar- tary constraints. This approach is based on a jovsky et al., 2016). This behavior is the same as the one parametrization stemming from Lie group theory encountered when computing the powers of a matrix, and through the exponential map. The parametrization results in very slow convergence (vanishing gradient) or a transforms the constrained optimization problem lack of convergence (exploding gradient). into an unconstrained one over a Euclidean space, for which common first-order optimization meth- In the seminal paper (Arjovsky et al., 2016), they note that ods can be used. The theoretical results presented unitary matrices have properties that would solve the ex- are general enough to cover the special orthogo- ploding and vanishing gradient problems. These matrices nal group, the unitary group and, in general, any form a group called the unitary group and they have been connected compact Lie group. We discuss how studied extensively in the fields of Lie group theory and Rie- this and other parametrizations can be computed mannian geometry. Optimization methods over the unitary efficiently through an implementation trick, mak- and orthogonal group have found rather fruitful applications ing numerically complex parametrizations usable in RNNs in recent years (cf.
    [Show full text]
  • 2 Hilbert Spaces You Should Have Seen Some Examples Last Semester
    2 Hilbert spaces You should have seen some examples last semester. The simplest (finite-dimensional) ex- C • A complex Hilbert space H is a complete normed space over whose norm is derived from an ample is Cn with its standard inner product. It’s worth recalling from linear algebra that if V is inner product. That is, we assume that there is a sesquilinear form ( , ): H H C, linear in · · × → an n-dimensional (complex) vector space, then from any set of n linearly independent vectors we the first variable and conjugate linear in the second, such that can manufacture an orthonormal basis e1, e2,..., en using the Gram-Schmidt process. In terms of this basis we can write any v V in the form (f ,д) = (д, f ), ∈ v = a e , a = (v, e ) (f , f ) 0 f H, and (f , f ) = 0 = f = 0. i i i i ≥ ∀ ∈ ⇒ ∑ The norm and inner product are related by which can be derived by taking the inner product of the equation v = aiei with ei. We also have n ∑ (f , f ) = f 2. ∥ ∥ v 2 = a 2. ∥ ∥ | i | i=1 We will always assume that H is separable (has a countable dense subset). ∑ Standard infinite-dimensional examples are l2(N) or l2(Z), the space of square-summable As usual for a normed space, the distance on H is given byd(f ,д) = f д = (f д, f д). • • ∥ − ∥ − − sequences, and L2(Ω) where Ω is a measurable subset of Rn. The Cauchy-Schwarz and triangle inequalities, • √ (f ,д) f д , f + д f + д , | | ≤ ∥ ∥∥ ∥ ∥ ∥ ≤ ∥ ∥ ∥ ∥ 2.1 Orthogonality can be derived fairly easily from the inner product.
    [Show full text]
  • Building Deep Networks on Grassmann Manifolds
    Building Deep Networks on Grassmann Manifolds Zhiwu Huangy, Jiqing Wuy, Luc Van Goolyz yComputer Vision Lab, ETH Zurich, Switzerland zVISICS, KU Leuven, Belgium fzhiwu.huang, jiqing.wu, [email protected] Abstract The popular applications of Grassmannian data motivate Learning representations on Grassmann manifolds is popular us to build a deep neural network architecture for Grassman- in quite a few visual recognition tasks. In order to enable deep nian representation learning. To this end, the new network learning on Grassmann manifolds, this paper proposes a deep architecture is designed to accept Grassmannian data di- network architecture by generalizing the Euclidean network rectly as input, and learns new favorable Grassmannian rep- paradigm to Grassmann manifolds. In particular, we design resentations that are able to improve the final visual recog- full rank mapping layers to transform input Grassmannian nition tasks. In other words, the new network aims to deeply data to more desirable ones, exploit re-orthonormalization learn Grassmannian features on their underlying Rieman- layers to normalize the resulting matrices, study projection nian manifolds in an end-to-end learning architecture. In pooling layers to reduce the model complexity in the Grass- summary, two main contributions are made by this paper: mannian context, and devise projection mapping layers to re- spect Grassmannian geometry and meanwhile achieve Eu- • We explore a novel deep network architecture in the con- clidean forms for regular output layers. To train the Grass- text of Grassmann manifolds, where it has not been possi- mann networks, we exploit a stochastic gradient descent set- ble to apply deep neural networks.
    [Show full text]
  • On Manifolds of Tensors of Fixed Tt-Rank
    ON MANIFOLDS OF TENSORS OF FIXED TT-RANK SEBASTIAN HOLTZ, THORSTEN ROHWEDDER, AND REINHOLD SCHNEIDER Abstract. Recently, the format of TT tensors [19, 38, 34, 39] has turned out to be a promising new format for the approximation of solutions of high dimensional problems. In this paper, we prove some new results for the TT representation of a tensor U ∈ Rn1×...×nd and for the manifold of tensors of TT-rank r. As a first result, we prove that the TT (or compression) ranks ri of a tensor U are unique and equal to the respective seperation ranks of U if the components of the TT decomposition are required to fulfil a certain maximal rank condition. We then show that d the set T of TT tensors of fixed rank r forms an embedded manifold in Rn , therefore preserving the essential theoretical properties of the Tucker format, but often showing an improved scaling behaviour. Extending a similar approach for matrices [7], we introduce certain gauge conditions to obtain a unique representation of the tangent space TU T of T and deduce a local parametrization of the TT manifold. The parametrisation of TU T is often crucial for an algorithmic treatment of high-dimensional time-dependent PDEs and minimisation problems [33]. We conclude with remarks on those applications and present some numerical examples. 1. Introduction The treatment of high-dimensional problems, typically of problems involving quantities from Rd for larger dimensions d, is still a challenging task for numerical approxima- tion. This is owed to the principal problem that classical approaches for their treatment normally scale exponentially in the dimension d in both needed storage and computa- tional time and thus quickly become computationally infeasable for sensible discretiza- tions of problems of interest.
    [Show full text]
  • Orthogonal Complements (Revised Version)
    Orthogonal Complements (Revised Version) Math 108A: May 19, 2010 John Douglas Moore 1 The dot product You will recall that the dot product was discussed in earlier calculus courses. If n x = (x1: : : : ; xn) and y = (y1: : : : ; yn) are elements of R , we define their dot product by x · y = x1y1 + ··· + xnyn: The dot product satisfies several key axioms: 1. it is symmetric: x · y = y · x; 2. it is bilinear: (ax + x0) · y = a(x · y) + x0 · y; 3. and it is positive-definite: x · x ≥ 0 and x · x = 0 if and only if x = 0. The dot product is an example of an inner product on the vector space V = Rn over R; inner products will be treated thoroughly in Chapter 6 of [1]. Recall that the length of an element x 2 Rn is defined by p jxj = x · x: Note that the length of an element x 2 Rn is always nonnegative. Cauchy-Schwarz Theorem. If x 6= 0 and y 6= 0, then x · y −1 ≤ ≤ 1: (1) jxjjyj Sketch of proof: If v is any element of Rn, then v · v ≥ 0. Hence (x(y · y) − y(x · y)) · (x(y · y) − y(x · y)) ≥ 0: Expanding using the axioms for dot product yields (x · x)(y · y)2 − 2(x · y)2(y · y) + (x · y)2(y · y) ≥ 0 or (x · x)(y · y)2 ≥ (x · y)2(y · y): 1 Dividing by y · y, we obtain (x · y)2 jxj2jyj2 ≥ (x · y)2 or ≤ 1; jxj2jyj2 and (1) follows by taking the square root.
    [Show full text]
  • LINEAR ALGEBRA FALL 2007/08 PROBLEM SET 9 SOLUTIONS In
    MATH 110: LINEAR ALGEBRA FALL 2007/08 PROBLEM SET 9 SOLUTIONS In the following V will denote a finite-dimensional vector space over R that is also an inner product space with inner product denoted by h ·; · i. The norm induced by h ·; · i will be denoted k · k. 1. Let S be a subset (not necessarily a subspace) of V . We define the orthogonal annihilator of S, denoted S?, to be the set S? = fv 2 V j hv; wi = 0 for all w 2 Sg: (a) Show that S? is always a subspace of V . ? Solution. Let v1; v2 2 S . Then hv1; wi = 0 and hv2; wi = 0 for all w 2 S. So for any α; β 2 R, hαv1 + βv2; wi = αhv1; wi + βhv2; wi = 0 ? for all w 2 S. Hence αv1 + βv2 2 S . (b) Show that S ⊆ (S?)?. Solution. Let w 2 S. For any v 2 S?, we have hv; wi = 0 by definition of S?. Since this is true for all v 2 S? and hw; vi = hv; wi, we see that hw; vi = 0 for all v 2 S?, ie. w 2 S?. (c) Show that span(S) ⊆ (S?)?. Solution. Since (S?)? is a subspace by (a) (it is the orthogonal annihilator of S?) and S ⊆ (S?)? by (b), we have span(S) ⊆ (S?)?. ? ? (d) Show that if S1 and S2 are subsets of V and S1 ⊆ S2, then S2 ⊆ S1 . ? Solution. Let v 2 S2 . Then hv; wi = 0 for all w 2 S2 and so for all w 2 S1 (since ? S1 ⊆ S2).
    [Show full text]
  • INTRODUCTION to ALGEBRAIC GEOMETRY 1. Preliminary Of
    INTRODUCTION TO ALGEBRAIC GEOMETRY WEI-PING LI 1. Preliminary of Calculus on Manifolds 1.1. Tangent Vectors. What are tangent vectors we encounter in Calculus? 2 0 (1) Given a parametrised curve α(t) = x(t); y(t) in R , α (t) = x0(t); y0(t) is a tangent vector of the curve. (2) Given a surface given by a parameterisation x(u; v) = x(u; v); y(u; v); z(u; v); @x @x n = × is a normal vector of the surface. Any vector @u @v perpendicular to n is a tangent vector of the surface at the corresponding point. (3) Let v = (a; b; c) be a unit tangent vector of R3 at a point p 2 R3, f(x; y; z) be a differentiable function in an open neighbourhood of p, we can have the directional derivative of f in the direction v: @f @f @f D f = a (p) + b (p) + c (p): (1.1) v @x @y @z In fact, given any tangent vector v = (a; b; c), not necessarily a unit vector, we still can define an operator on the set of functions which are differentiable in open neighbourhood of p as in (1.1) Thus we can take the viewpoint that each tangent vector of R3 at p is an operator on the set of differential functions at p, i.e. @ @ @ v = (a; b; v) ! a + b + c j ; @x @y @z p or simply @ @ @ v = (a; b; c) ! a + b + c (1.2) @x @y @z 3 with the evaluation at p understood.
    [Show full text]
  • DIFFERENTIAL GEOMETRY COURSE NOTES 1.1. Review of Topology. Definition 1.1. a Topological Space Is a Pair (X,T ) Consisting of A
    DIFFERENTIAL GEOMETRY COURSE NOTES KO HONDA 1. REVIEW OF TOPOLOGY AND LINEAR ALGEBRA 1.1. Review of topology. Definition 1.1. A topological space is a pair (X; T ) consisting of a set X and a collection T = fUαg of subsets of X, satisfying the following: (1) ;;X 2 T , (2) if Uα;Uβ 2 T , then Uα \ Uβ 2 T , (3) if Uα 2 T for all α 2 I, then [α2I Uα 2 T . (Here I is an indexing set, and is not necessarily finite.) T is called a topology for X and Uα 2 T is called an open set of X. n Example 1: R = R × R × · · · × R (n times) = f(x1; : : : ; xn) j xi 2 R; i = 1; : : : ; ng, called real n-dimensional space. How to define a topology T on Rn? We would at least like to include open balls of radius r about y 2 Rn: n Br(y) = fx 2 R j jx − yj < rg; where p 2 2 jx − yj = (x1 − y1) + ··· + (xn − yn) : n n Question: Is T0 = fBr(y) j y 2 R ; r 2 (0; 1)g a valid topology for R ? n No, so you must add more open sets to T0 to get a valid topology for R . T = fU j 8y 2 U; 9Br(y) ⊂ Ug: Example 2A: S1 = f(x; y) 2 R2 j x2 + y2 = 1g. A reasonable topology on S1 is the topology induced by the inclusion S1 ⊂ R2. Definition 1.2. Let (X; T ) be a topological space and let f : Y ! X.
    [Show full text]
  • Manifold Reconstruction in Arbitrary Dimensions Using Witness Complexes Jean-Daniel Boissonnat, Leonidas J
    Manifold Reconstruction in Arbitrary Dimensions using Witness Complexes Jean-Daniel Boissonnat, Leonidas J. Guibas, Steve Oudot To cite this version: Jean-Daniel Boissonnat, Leonidas J. Guibas, Steve Oudot. Manifold Reconstruction in Arbitrary Dimensions using Witness Complexes. Discrete and Computational Geometry, Springer Verlag, 2009, pp.37. hal-00488434 HAL Id: hal-00488434 https://hal.archives-ouvertes.fr/hal-00488434 Submitted on 2 Jun 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Manifold Reconstruction in Arbitrary Dimensions using Witness Complexes Jean-Daniel Boissonnat Leonidas J. Guibas Steve Y. Oudot INRIA, G´eom´etrica Team Dept. Computer Science Dept. Computer Science 2004 route des lucioles Stanford University Stanford University 06902 Sophia-Antipolis, France Stanford, CA 94305 Stanford, CA 94305 [email protected] [email protected] [email protected]∗ Abstract It is a well-established fact that the witness complex is closely related to the restricted Delaunay triangulation in low dimensions. Specifically, it has been proved that the witness complex coincides with the restricted Delaunay triangulation on curves, and is still a subset of it on surfaces, under mild sampling conditions. In this paper, we prove that these results do not extend to higher-dimensional manifolds, even under strong sampling conditions such as uniform point density.
    [Show full text]
  • Stiefel Manifolds and Polygons
    Stiefel Manifolds and Polygons Clayton Shonkwiler Department of Mathematics, Colorado State University; [email protected] Abstract Polygons are compound geometric objects, but when trying to understand the expected behavior of a large collection of random polygons – or even to formalize what a random polygon is – it is convenient to interpret each polygon as a point in some parameter space, essentially trading the complexity of the object for the complexity of the space. In this paper I describe such an interpretation where the parameter space is an abstract but very nice space called a Stiefel manifold and show how to exploit the geometry of the Stiefel manifold both to generate random polygons and to morph one polygon into another. Introduction The goal is to describe an identification of polygons with points in certain nice parameter spaces. For example, every triangle in the plane can be identified with a pair of orthonormal vectors in 3-dimensional 3 3 space R , and hence planar triangle shapes correspond to points in the space St2(R ) of all such pairs, which is an example of a Stiefel manifold. While this space is defined somewhat abstractly and is hard to visualize – for example, it cannot be embedded in Euclidean space of fewer than 5 dimensions [9] – it is easy to visualize and manipulate points in it: they’re just pairs of perpendicular unit vectors. Moreover, this is a familiar and well-understood space in the setting of differential geometry. For example, the group SO(3) of rotations of 3-space acts transitively on it, meaning that there is a rotation of space which transforms any triangle into any other triangle.
    [Show full text]
  • A Guided Tour to the Plane-Based Geometric Algebra PGA
    A Guided Tour to the Plane-Based Geometric Algebra PGA Leo Dorst University of Amsterdam Version 1.15{ July 6, 2020 Planes are the primitive elements for the constructions of objects and oper- ators in Euclidean geometry. Triangulated meshes are built from them, and reflections in multiple planes are a mathematically pure way to construct Euclidean motions. A geometric algebra based on planes is therefore a natural choice to unify objects and operators for Euclidean geometry. The usual claims of `com- pleteness' of the GA approach leads us to hope that it might contain, in a single framework, all representations ever designed for Euclidean geometry - including normal vectors, directions as points at infinity, Pl¨ucker coordinates for lines, quaternions as 3D rotations around the origin, and dual quaternions for rigid body motions; and even spinors. This text provides a guided tour to this algebra of planes PGA. It indeed shows how all such computationally efficient methods are incorporated and related. We will see how the PGA elements naturally group into blocks of four coordinates in an implementation, and how this more complete under- standing of the embedding suggests some handy choices to avoid extraneous computations. In the unified PGA framework, one never switches between efficient representations for subtasks, and this obviously saves any time spent on data conversions. Relative to other treatments of PGA, this text is rather light on the mathematics. Where you see careful derivations, they involve the aspects of orientation and magnitude. These features have been neglected by authors focussing on the mathematical beauty of the projective nature of the algebra.
    [Show full text]
  • Hodge Theory
    HODGE THEORY PETER S. PARK Abstract. This exposition of Hodge theory is a slightly retooled version of the author's Harvard minor thesis, advised by Professor Joe Harris. Contents 1. Introduction 1 2. Hodge Theory of Compact Oriented Riemannian Manifolds 2 2.1. Hodge star operator 2 2.2. The main theorem 3 2.3. Sobolev spaces 5 2.4. Elliptic theory 11 2.5. Proof of the main theorem 14 3. Hodge Theory of Compact K¨ahlerManifolds 17 3.1. Differential operators on complex manifolds 17 3.2. Differential operators on K¨ahlermanifolds 20 3.3. Bott{Chern cohomology and the @@-Lemma 25 3.4. Lefschetz decomposition and the Hodge index theorem 26 Acknowledgments 30 References 30 1. Introduction Our objective in this exposition is to state and prove the main theorems of Hodge theory. In Section 2, we first describe a key motivation behind the Hodge theory for compact, closed, oriented Riemannian manifolds: the observation that the differential forms that satisfy certain par- tial differential equations depending on the choice of Riemannian metric (forms in the kernel of the associated Laplacian operator, or harmonic forms) turn out to be precisely the norm-minimizing representatives of the de Rham cohomology classes. This naturally leads to the statement our first main theorem, the Hodge decomposition|for a given compact, closed, oriented Riemannian manifold|of the space of smooth k-forms into the image of the Laplacian and its kernel, the sub- space of harmonic forms. We then develop the analytic machinery|specifically, Sobolev spaces and the theory of elliptic differential operators|that we use to prove the aforementioned decom- position, which immediately yields as a corollary the phenomenon of Poincar´eduality.
    [Show full text]