On the Complex Cobordism of Flag Varieties Associated to Loop Groups
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
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. -
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. -
Classification on the Grassmannians
GEOMETRIC FRAMEWORK SOME EMPIRICAL RESULTS COMPRESSION ON G(k, n) CONCLUSIONS Classification on the Grassmannians: Theory and Applications Jen-Mei Chang Department of Mathematics and Statistics California State University, Long Beach [email protected] AMS Fall Western Section Meeting Special Session on Metric and Riemannian Methods in Shape Analysis October 9, 2010 at UCLA GEOMETRIC FRAMEWORK SOME EMPIRICAL RESULTS COMPRESSION ON G(k, n) CONCLUSIONS Outline 1 Geometric Framework Evolution of Classification Paradigms Grassmann Framework Grassmann Separability 2 Some Empirical Results Illumination Illumination + Low Resolutions 3 Compression on G(k, n) Motivations, Definitions, and Algorithms Karcher Compression for Face Recognition GEOMETRIC FRAMEWORK SOME EMPIRICAL RESULTS COMPRESSION ON G(k, n) CONCLUSIONS Architectures Historically Currently single-to-single subspace-to-subspace )2( )3( )1( d ( P,x ) d ( P,x ) d(P,x ) ... P G , , … , x )1( x )2( x( N) single-to-many many-to-many Image Set 1 … … G … … p * P , Grassmann Manifold Image Set 2 * X )1( X )2( … X ( N) q Project Eigenfaces GEOMETRIC FRAMEWORK SOME EMPIRICAL RESULTS COMPRESSION ON G(k, n) CONCLUSIONS Some Approaches • Single-to-Single 1 Euclidean distance of feature points. 2 Correlation. • Single-to-Many 1 Subspace method [Oja, 1983]. 2 Eigenfaces (a.k.a. Principal Component Analysis, KL-transform) [Sirovich & Kirby, 1987], [Turk & Pentland, 1991]. 3 Linear/Fisher Discriminate Analysis, Fisherfaces [Belhumeur et al., 1997]. 4 Kernel PCA [Yang et al., 2000]. GEOMETRIC FRAMEWORK SOME EMPIRICAL RESULTS COMPRESSION ON G(k, n) CONCLUSIONS Some Approaches • Many-to-Many 1 Tangent Space and Tangent Distance - Tangent Distance [Simard et al., 2001], Joint Manifold Distance [Fitzgibbon & Zisserman, 2003], Subspace Distance [Chang, 2004]. -
Arxiv:1711.05949V2 [Math.RT] 27 May 2018 Mials, One Obtains a Sum Whose Summands Are Rational Functions in Ti
RESIDUES FORMULAS FOR THE PUSH-FORWARD IN K-THEORY, THE CASE OF G2=P . ANDRZEJ WEBER AND MAGDALENA ZIELENKIEWICZ Abstract. We study residue formulas for push-forward in the equivariant K-theory of homogeneous spaces. For the classical Grassmannian the residue formula can be obtained from the cohomological formula by a substitution. We also give another proof using sym- plectic reduction and the method involving the localization theorem of Jeffrey–Kirwan. We review formulas for other classical groups, and we derive them from the formulas for the classical Grassmannian. Next we consider the homogeneous spaces for the exceptional group G2. One of them, G2=P2 corresponding to the shorter root, embeds in the Grass- mannian Gr(2; 7). We find its fundamental class in the equivariant K-theory KT(Gr(2; 7)). This allows to derive a residue formula for the push-forward. It has significantly different character comparing to the classical groups case. The factor involving the fundamen- tal class of G2=P2 depends on the equivariant variables. To perform computations more efficiently we apply the basis of K-theory consisting of Grothendieck polynomials. The residue formula for push-forward remains valid for the homogeneous space G2=B as well. 1. Introduction ∗ r Suppose an algebraic torus T = (C ) acts on a complex variety X which is smooth and complete. Let E be an equivariant complex vector bundle over X. Then E defines an element of the equivariant K-theory of X. In our setting there is no difference whether one considers the algebraic K-theory or the topological one. -
Filtering Grassmannian Cohomology Via K-Schur Functions
FILTERING GRASSMANNIAN COHOMOLOGY VIA K-SCHUR FUNCTIONS THE 2020 POLYMATH JR. \Q-B-AND-G" GROUPy Abstract. This paper is concerned with studying the Hilbert Series of the subalgebras of the cohomology rings of the complex Grassmannians and La- grangian Grassmannians. We build upon a previous conjecture by Reiner and Tudose for the complex Grassmannian and present an analogous conjecture for the Lagrangian Grassmannian. Additionally, we summarize several poten- tial approaches involving Gr¨obnerbases, homological algebra, and algebraic topology. Then we give a new interpretation to the conjectural expression for the complex Grassmannian by utilizing k-conjugation. This leads to two conjectural bases that would imply the original conjecture for the Grassman- nian. Finally, we comment on how this new approach foreshadows the possible existence of analogous k-Schur functions in other Lie types. Contents 1. Introduction2 2. Conjectures3 2.1. The Grassmannian3 2.2. The Lagrangian Grassmannian4 3. A Gr¨obnerBasis Approach 12 3.1. Patterns, Patterns and more Patterns 12 3.2. A Lex-greedy Approach 20 4. An Approach Using Natural Maps Between Rings 23 4.1. Maps Between Grassmannians 23 4.2. Maps Between Lagrangian Grassmannians 24 4.3. Diagrammatic Reformulation for the Two Main Conjectures 24 4.4. Approach via Coefficient-Wise Inequalities 27 4.5. Recursive Formula for Rk;` 27 4.6. A \q-Pascal Short Exact Sequence" 28 n;m 4.7. Recursive Formula for RLG 30 4.8. Doubly Filtered Basis 31 5. Schur and k-Schur Functions 35 5.1. Schur and Pieri for the Grassmannian 36 5.2. Schur and Pieri for the Lagrangian Grassmannian 37 Date: August 2020. -
§4 Grassmannians 73
§4 Grassmannians 4.1 Plücker quadric and Gr(2,4). Let 푉 be 4-dimensional vector space. e set of all 2-di- mensional vector subspaces 푈 ⊂ 푉 is called the grassmannian Gr(2, 4) = Gr(2, 푉). More geomet- rically, the grassmannian Gr(2, 푉) is the set of all lines ℓ ⊂ ℙ = ℙ(푉). Sending 2-dimensional vector subspace 푈 ⊂ 푉 to 1-dimensional subspace 훬푈 ⊂ 훬푉 or, equivalently, sending a line (푎푏) ⊂ ℙ(푉) to 한 ⋅ 푎 ∧ 푏 ⊂ 훬푉 , we get the Plücker embedding 픲 ∶ Gr(2, 4) ↪ ℙ = ℙ(훬 푉). (4-1) Its image consists of all decomposable¹ grassmannian quadratic forms 휔 = 푎∧푏 , 푎, 푏 ∈ 푉. Clearly, any such a form has zero square: 휔 ∧ 휔 = 푎 ∧ 푏 ∧ 푎 ∧ 푏 = 0. Since an arbitrary form 휉 ∈ 훬푉 can be wrien¹ in appropriate basis of 푉 either as 푒 ∧ 푒 or as 푒 ∧ 푒 + 푒 ∧ 푒 and in the laer case 휉 is not decomposable, because of 휉 ∧ 휉 = 2 푒 ∧ 푒 ∧ 푒 ∧ 푒 ≠ 0, we conclude that 휔 ∈ 훬 푉 is decomposable if an only if 휔 ∧ 휔 = 0. us, the image of (4-1) is the Plücker quadric 푃 ≝ { 휔 ∈ 훬푉 | 휔 ∧ 휔 = 0 } (4-2) If we choose a basis 푒, 푒, 푒, 푒 ∈ 푉, the monomial basis 푒 = 푒 ∧ 푒 in 훬 푉, and write 푥 for the homogeneous coordinates along 푒, then straightforward computation 푥 ⋅ 푒 ∧ 푒 ∧ 푥 ⋅ 푒 ∧ 푒 = 2 푥 푥 − 푥 푥 + 푥 푥 ⋅ 푒 ∧ 푒 ∧ 푒 ∧ 푒 < < implies that 푃 is given by the non-degenerated quadratic equation 푥푥 = 푥푥 + 푥푥 . -
8. Grassmannians
66 Andreas Gathmann 8. Grassmannians After having introduced (projective) varieties — the main objects of study in algebraic geometry — let us now take a break in our discussion of the general theory to construct an interesting and useful class of examples of projective varieties. The idea behind this construction is simple: since the definition of projective spaces as the sets of 1-dimensional linear subspaces of Kn turned out to be a very useful concept, let us now generalize this and consider instead the sets of k-dimensional linear subspaces of Kn for an arbitrary k = 0;:::;n. Definition 8.1 (Grassmannians). Let n 2 N>0, and let k 2 N with 0 ≤ k ≤ n. We denote by G(k;n) the set of all k-dimensional linear subspaces of Kn. It is called the Grassmannian of k-planes in Kn. Remark 8.2. By Example 6.12 (b) and Exercise 6.32 (a), the correspondence of Remark 6.17 shows that k-dimensional linear subspaces of Kn are in natural one-to-one correspondence with (k − 1)- n− dimensional linear subspaces of P 1. We can therefore consider G(k;n) alternatively as the set of such projective linear subspaces. As the dimensions k and n are reduced by 1 in this way, our Grassmannian G(k;n) of Definition 8.1 is sometimes written in the literature as G(k − 1;n − 1) instead. Of course, as in the case of projective spaces our goal must again be to make the Grassmannian G(k;n) into a variety — in fact, we will see that it is even a projective variety in a natural way. -
Grassmannians Via Projection Operators and Some of Their Special Submanifolds ∗
GRASSMANNIANS VIA PROJECTION OPERATORS AND SOME OF THEIR SPECIAL SUBMANIFOLDS ∗ IVKO DIMITRIC´ Department of Mathematics, Penn State University Fayette, Uniontown, PA 15401-0519, USA E-mail: [email protected] We study submanifolds of a general Grassmann manifold which are of 1-type in a suitably defined Euclidean space of F−Hermitian matrices (such a submanifold is minimal in some hypersphere of that space and, apart from a translation, the im- mersion is built using eigenfunctions from a single eigenspace of the Laplacian). We show that a minimal 1-type hypersurface of a Grassmannian is mass-symmetric and has the type-eigenvalue which is a specific number in each dimension. We derive some conditions on the mean curvature of a 1-type hypersurface of a Grassmannian and show that such a hypersurface with a nonzero constant mean curvature has at most three constant principal curvatures. At the end, we give some estimates of the first nonzero eigenvalue of the Laplacian on a compact submanifold of a Grassmannian. 1. Introduction The Grassmann manifold of k dimensional subspaces of the vector space − Fm over a field F R, C, H can be conveniently defined as the base man- ∈{ } ifold of the principal fibre bundle of the corresponding Stiefel manifold of F unitary frames, where a frame is projected onto the F plane spanned − − by it. As observed already in 1927 by Cartan, all Grassmannians are sym- metric spaces and they have been studied in such context ever since, using the root systems and the Lie algebra techniques. However, the submani- fold geometry in Grassmannians of higher rank is much less developed than the corresponding geometry in rank-1 symmetric spaces, the notable excep- tions being the classification of the maximal totally geodesic submanifolds (the well known results of J.A. -
WEIGHT FILTRATIONS in ALGEBRAIC K-THEORY Daniel R
November 13, 1992 WEIGHT FILTRATIONS IN ALGEBRAIC K-THEORY Daniel R. Grayson University of Illinois at Urbana-Champaign Abstract. We survey briefly some of the K-theoretic background related to the theory of mixed motives and motivic cohomology. 1. Introduction. The recent search for a motivic cohomology theory for varieties, described elsewhere in this volume, has been largely guided by certain aspects of the higher algebraic K-theory developed by Quillen in 1972. It is the purpose of this article to explain the sense in which the previous statement is true, and to explain how it is thought that the motivic cohomology groups with rational coefficients arise from K-theory through the intervention of the Adams operations. We give a basic description of algebraic K-theory and explain how Quillen’s idea [42] that the Atiyah-Hirzebruch spectral sequence of topology may have an algebraic analogue guides the search for motivic cohomology. There are other useful survey articles about algebraic K-theory: [50], [46], [23], [56], and [40]. I thank W. Dwyer, E. Friedlander, S. Lichtenbaum, R. McCarthy, S. Mitchell, C. Soul´e and R. Thomason for frequent and useful consultations. 2. Constructing topological spaces. In this section we explain the considerations from combinatorial topology which give rise to the higher algebraic K-groups. The first principle is simple enough to state, but hard to implement: when given an interesting group (such as the Grothendieck group of a ring) arising from some algebraic situation, try to realize it as a low-dimensional homotopy group (especially π0 or π1) of a space X constructed in some combinatorial way from the same algebraic inputs, and study the homotopy type of the resulting space. -
Equivariant Homology and K-Theory of Affine Grassmannians and Toda Lattices R Bezrukavnikov
University of Massachusetts Amherst ScholarWorks@UMass Amherst Mathematics and Statistics Department Faculty Mathematics and Statistics Publication Series 2005 Equivariant homology and K-theory of affine Grassmannians and Toda lattices R Bezrukavnikov M Finkelberg I Mirkovic University of Massachusetts - Amherst, [email protected] Follow this and additional works at: https://scholarworks.umass.edu/math_faculty_pubs Part of the Physical Sciences and Mathematics Commons Recommended Citation Bezrukavnikov, R; Finkelberg, M; and Mirkovic, I, "Equivariant homology and K-theory of affine asGr smannians and Toda lattices" (2005). COMPOSITIO MATHEMATICA. 727. Retrieved from https://scholarworks.umass.edu/math_faculty_pubs/727 This Article is brought to you for free and open access by the Mathematics and Statistics at ScholarWorks@UMass Amherst. It has been accepted for inclusion in Mathematics and Statistics Department Faculty Publication Series by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. EQUIVARIANT (K-)HOMOLOGY OF AFFINE GRASSMANNIAN AND TODA LATTICE ROMAN BEZRUKAVNIKOV, MICHAEL FINKELBERG, AND IVAN MIRKOVIC´ 1. Introduction 1.1. Let G be an almost simple complex algebraic group, and let GrG be its affine Grassmannian. Recall that if we set O = C[[t]], F = C((t)), then GrG = G(F)/G(O). It is well-known that the subgroup ΩK of polynomial loops into a maximal compact subgroup K G projects isomorphically to GrG; thus GrG acquires the structure of a topological⊂ group. An algebro-geometric counterpart of this structure is provided by the convolution diagram G(F) O Gr Gr . ×G( ) G → G It allows one to define the convolution of two G(O) equivariant geometric objects (such as sheaves, or constrictible functions) on GrG. -
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.