Spectral Geometry
Total Page:16
File Type:pdf, Size:1020Kb

Load more
Recommended publications
-
Spectral Geometry Spring 2016
Spectral Geometry Spring 2016 Michael Magee Lecture 5: The resolvent. Last time we proved that the Laplacian has a closure whose domain is the Sobolev space H2(M)= u L2(M): u L2(M), ∆u L2(M) . { 2 kr k2 2 } We view the Laplacian as an unbounded densely defined self-adjoint operator on L2 with domain H2. We also stated the spectral theorem. We’ll now ask what is the nature of the pro- jection valued measure associated to the Laplacian (its ‘spectral decomposition’). Until otherwise specified, suppose M is a complete Riemannian manifold. Definition 1. The resolvent set of the Laplacian is the set of λ C such that λI ∆isa bijection of H2 onto L2 with a boundedR inverse (with respect to L2 norms)2 − 1 2 2 R(λ) (λI ∆)− : L (M) H (M). ⌘ − ! The operator R(λ) is called the resolvent of the Laplacian at λ. We write spec(∆) = C −R and call it the spectrum. Note that the projection valued measure of ∆is supported in R 0. Otherwise one can find a function in H2(M)where ∆ , is negative, but since can be≥ approximated in Sobolev norm by smooth functions, thish will contradicti ∆ , = g( , )Vol 0. (1) h i ˆ r r ≥ Note then the spectral theorem implies that the resolvent set contains C R 0 (use the Borel functional calculus). − ≥ Theorem 2 (Stone’s formula [1, VII.13]). Let P be the projection valued measure on R associated to the Laplacan by the spectral theorem. The following formula relates the resolvent to P b 1 1 lim(2⇡i)− [R(λ + i✏) R(λ i✏)] dλ = P[a,b] + P(a,b) . -
Isospectralization, Or How to Hear Shape, Style, and Correspondence
Isospectralization, or how to hear shape, style, and correspondence Luca Cosmo Mikhail Panine Arianna Rampini University of Venice Ecole´ Polytechnique Sapienza University of Rome [email protected] [email protected] [email protected] Maks Ovsjanikov Michael M. Bronstein Emanuele Rodola` Ecole´ Polytechnique Imperial College London / USI Sapienza University of Rome [email protected] [email protected] [email protected] Initialization Reconstruction Abstract opt. target The question whether one can recover the shape of a init. geometric object from its Laplacian spectrum (‘hear the shape of the drum’) is a classical problem in spectral ge- ometry with a broad range of implications and applica- tions. While theoretically the answer to this question is neg- ative (there exist examples of iso-spectral but non-isometric manifolds), little is known about the practical possibility of using the spectrum for shape reconstruction and opti- mization. In this paper, we introduce a numerical proce- dure called isospectralization, consisting of deforming one shape to make its Laplacian spectrum match that of an- other. We implement the isospectralization procedure using modern differentiable programming techniques and exem- plify its applications in some of the classical and notori- Without isospectralization With isospectralization ously hard problems in geometry processing, computer vi- sion, and graphics such as shape reconstruction, pose and Figure 1. Top row: Mickey-from-spectrum: we recover the shape of Mickey Mouse from its first 20 Laplacian eigenvalues (shown style transfer, and dense deformable correspondence. in red in the leftmost plot) by deforming an initial ellipsoid shape; the ground-truth target embedding is shown as a red outline on top of our reconstruction. -
Arxiv:1908.09677V4 [Math.AG] 13 Jul 2021 6 Hr Ro Fterm12.1 Theorem of Proof Third a 16
AN ANALYTIC VERSION OF THE LANGLANDS CORRESPONDENCE FOR COMPLEX CURVES PAVEL ETINGOF, EDWARD FRENKEL, AND DAVID KAZHDAN In memory of Boris Dubrovin Abstract. The Langlands correspondence for complex curves is traditionally for- mulated in terms of sheaves rather than functions. Recently, Langlands asked whether it is possible to construct a function-theoretic version. In this paper we use the algebra of commuting global differential operators (quantum Hitchin Hamilto- nians and their complex conjugates) on the moduli space of G-bundles of a complex algebraic curve to formulate a function-theoretic correspondence. We conjecture the existence of a canonical self-adjoint extension of the symmetric part of this al- gebra acting on an appropriate Hilbert space and link its spectrum with the set of opers for the Langlands dual group of G satisfying a certain reality condition, as predicted earlier by Teschner. We prove this conjecture for G = GL1 and in the simplest non-abelian case. Contents 1. Introduction 2 Part I 8 2. Differential operators on line bundles 8 3. Differential operators on BunG 11 4. The spectrum and opers 15 5. The abelian case 19 6. Bundles with parabolic structures 26 1 7. The case of SL2 and P with marked points 28 arXiv:1908.09677v4 [math.AG] 13 Jul 2021 8. Proofs of two results 30 Part II 33 9. Darboux operators 34 10. EigenfunctionsandmonodromyforDarbouxoperators 38 11. Essentially self-adjoint algebras of unbounded operators 43 12. The main theorem 49 13. Generalized Sobolev and Schwartz spaces attached to the operator L. 49 14. Proof of Theorem 12.1 60 15. -
Singular Value Decomposition (SVD)
San José State University Math 253: Mathematical Methods for Data Visualization Lecture 5: Singular Value Decomposition (SVD) Dr. Guangliang Chen Outline • Matrix SVD Singular Value Decomposition (SVD) Introduction We have seen that symmetric matrices are always (orthogonally) diagonalizable. That is, for any symmetric matrix A ∈ Rn×n, there exist an orthogonal matrix Q = [q1 ... qn] and a diagonal matrix Λ = diag(λ1, . , λn), both real and square, such that A = QΛQT . We have pointed out that λi’s are the eigenvalues of A and qi’s the corresponding eigenvectors (which are orthogonal to each other and have unit norm). Thus, such a factorization is called the eigendecomposition of A, also called the spectral decomposition of A. What about general rectangular matrices? Dr. Guangliang Chen | Mathematics & Statistics, San José State University3/22 Singular Value Decomposition (SVD) Existence of the SVD for general matrices Theorem: For any matrix X ∈ Rn×d, there exist two orthogonal matrices U ∈ Rn×n, V ∈ Rd×d and a nonnegative, “diagonal” matrix Σ ∈ Rn×d (of the same size as X) such that T Xn×d = Un×nΣn×dVd×d. Remark. This is called the Singular Value Decomposition (SVD) of X: • The diagonals of Σ are called the singular values of X (often sorted in decreasing order). • The columns of U are called the left singular vectors of X. • The columns of V are called the right singular vectors of X. Dr. Guangliang Chen | Mathematics & Statistics, San José State University4/22 Singular Value Decomposition (SVD) * * b * b (n>d) b b b * b = * * = b b b * (n<d) * b * * b b Dr. -
Arxiv:2107.01242V1 [Math.OA] 2 Jul 2021 Euneo Ievle ( Eigenvalues of Sequence a Eetdacrigt T Agbac Utpiiy Y[ by Multiplicity
CONNES’ INTEGRATION AND WEYL’S LAWS RAPHAEL¨ PONGE Abstract. This paper deal with some questions regarding the notion of integral in the frame- work of Connes’s noncommutative geometry. First, we present a purely spectral theoretic con- struction of Connes’ integral. This answers a question of Alain Connes. We also deal with the compatibility of Dixmier traces with Lebesgue’s integral. This answers another question of Alain Connes. We further clarify the relationship of Connes’ integration with Weyl’s laws for compact operators and Birman-Solomyak’s perturbation theory. We also give a ”soft proof” of Birman-Solomyak’s Weyl’s law for negative order pseudodifferential operators on closed mani- fold. This Weyl’s law yields a stronger form of Connes’ trace theorem. Finally, we explain the relationship between Connes’ integral and semiclassical Weyl’s law for Schr¨odinger operators. This is an easy consequence of the Birman-Schwinger principle. We thus get a neat link between noncommutative geometry and semiclassical analysis. 1. Introduction The quantized calculus of Connes [18] aims at translating the main tools of the classical infin- itesimal calculus into the operator theoretic language of quantum mechanics. As an Ansatz the integral in this setup should be a positive trace on the weak trace class L1,∞ (see Section 2). Natural choices are given by the traces Trω of Dixmier [23] (see also [18, 34] and Section 2). These traces are associated with extended limits. Following Connes [18] we say that an operator A L is measurable when the value of Tr (A) is independent of the extended limit. -
Heat Kernels and Function Theory on Metric Measure Spaces
Heat kernels and function theory on metric measure spaces Alexander Grigor’yan Imperial College London SW7 2AZ United Kingdom and Institute of Control Sciences of RAS Moscow, Russia email: [email protected] Contents 1 Introduction 2 2 Definition of a heat kernel and examples 5 3 Volume of balls 8 4 Energy form 10 5 Besov spaces and energy domain 13 5.1 Besov spaces in Rn ............................ 13 5.2 Besov spaces in a metric measure space . 14 5.3 Identification of energy domains and Besov spaces . 15 5.4 Subordinated heat kernel ......................... 19 6 Intrinsic characterization of walk dimension 22 7 Inequalities for walk dimension 23 8 Embedding of Besov spaces into H¨olderspaces 29 9 Bessel potential spaces 30 1 1 Introduction The classical heat kernel in Rn is the fundamental solution to the heat equation, which is given by the following formula 1 x y 2 pt (x, y) = exp | − | . (1.1) n/2 t (4πt) − 4 ! x y 2 It is worth observing that the Gaussian term exp | − | does not depend in n, − 4t n/2 whereas the other term (4πt)− reflects the dependence of the heat kernel on the underlying space via its dimension. The notion of heat kernel extends to any Riemannian manifold M. In this case, the heat kernel pt (x, y) is the minimal positive fundamental solution to the heat ∂u equation ∂t = ∆u where ∆ is the Laplace-Beltrami operator on M, and it always exists (see [11], [13], [16]). Under certain assumptions about M, the heat kernel can be estimated similarly to (1.1). -
WEYL's LAW on RIEMANNIAN MANIFOLDS Contents 1. the Role of Weyl's Law in the Ultraviolet Catastrophe 1 2. Riemannian Manifol
WEYL'S LAW ON RIEMANNIAN MANIFOLDS SETH MUSSER Abstract. We motivate Weyl's law by demonstrating the relevance of the distribution of eigenvalues of the Laplacian to the ultraviolet catastrophe of physics. We then introduce Riemannian geometry, define the Laplacian on a general Riemannian manifold, and find geometric analogs of various concepts in Rn, along the way using S2 as a clarifying example. Finally, we use analogy with Rn and the results we have built up to prove Weyl's law, making sure at each step to demonstrate the physical significance of any major ideas. Contents 1. The Role of Weyl's Law in the Ultraviolet Catastrophe 1 2. Riemannian Manifolds 3 3. Weyl's Law 11 4. Acknowledgements 20 References 20 1. The Role of Weyl's Law in the Ultraviolet Catastrophe In the year 1900 Lord Rayleigh used the equipartition theorem of thermodynamics to de- duce the famous Rayleigh-Jeans law of radiation. Rayleigh began with an idealized physical concept of the blackbody; a body which is a perfect absorber of electromagnetic radiation and which radiates all the energy it absorbs independent of spatial direction. We sketch his proof to find the amount of radiation energy emitted by the blackbody at a given frequency. Take a blackbody in the shape of a cube for definiteness, and assume it is made of con- ducting material and is at a temperature T . We will denote the cube by D = [0;L]3 ⊆ R3. Inside this cube we have electromagnetic radiation that obeys Maxwell's equations bouncing around. The equipartition theorem of thermodynamics says that every wave which has con- stant spatial structure with oscillating amplitude, i.e. -
Heat Kernel Analysis on Graphs
Heat Kernel Analysis On Graphs Xiao Bai Submitted for the degree of Doctor of Philosophy Department of Computer Science June 7, 2007 Abstract In this thesis we aim to develop a framework for graph characterization by com- bining the methods from spectral graph theory and manifold learning theory. The algorithms are applied to graph clustering, graph matching and object recogni- tion. Spectral graph theory has been widely applied in areas such as image recog- nition, image segmentation, motion tracking, image matching and etc. The heat kernel is an important component of spectral graph theory since it can be viewed as describing the flow of information across the edges of the graph with time. Our first contribution is to investigate how to extract useful and stable invari- ants from the graph heat kernel as a means of clustering graphs. The best set of invariants are the heat kernel trace, the zeta function and its derivative at the origin. We also study heat content invariants. The polynomial co-efficients can be computed from the Laplacian eigensystem. Graph clustering is performed by applying principal components analysis to vectors constructed from the invari- ants or simply based on the unitary features extracted from the graph heat kernel. We experiment with the algorithms on the COIL and Oxford-Caltech databases. We further investigate the heat kernel as a means of graph embedding. The second contribution of the thesis is the introduction of two graph embedding methods. The first of these uses the Euclidean distance between graph nodes. To do this we equate the spectral and parametric forms of the heat kernel to com- i pute an approximate Euclidean distance between nodes. -
Spectral Geometry for Structural Pattern Recognition
Spectral Geometry for Structural Pattern Recognition HEWAYDA EL GHAWALBY Ph.D. Thesis This thesis is submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy. Department of Computer Science United Kingdom May 2011 Abstract Graphs are used pervasively in computer science as representations of data with a network or relational structure, where the graph structure provides a flexible representation such that there is no fixed dimensionality for objects. However, the analysis of data in this form has proved an elusive problem; for instance, it suffers from the robustness to structural noise. One way to circumvent this problem is to embed the nodes of a graph in a vector space and to study the properties of the point distribution that results from the embedding. This is a problem that arises in a number of areas including manifold learning theory and graph-drawing. In this thesis, our first contribution is to investigate the heat kernel embed- ding as a route to computing geometric characterisations of graphs. The reason for turning to the heat kernel is that it encapsulates information concerning the distribution of path lengths and hence node affinities on the graph. The heat ker- nel of the graph is found by exponentiating the Laplacian eigensystem over time. The matrix of embedding co-ordinates for the nodes of the graph is obtained by performing a Young-Householder decomposition on the heat kernel. Once the embedding of its nodes is to hand we proceed to characterise a graph in a geometric manner. With the embeddings to hand, we establish a graph character- ization based on differential geometry by computing sets of curvatures associated ii Abstract iii with the graph nodes, edges and triangular faces. -
Inverse Spectral Problems in Riemannian Geometry
Inverse Spectral Problems in Riemannian Geometry Peter A. Perry Department of Mathematics, University of Kentucky, Lexington, Kentucky 40506-0027, U.S.A. 1 Introduction Over twenty years ago, Marc Kac posed what is arguably one of the simplest inverse problems in pure mathematics: "Can one hear the shape of a drum?" [19]. Mathematically, the question is formulated as follows. Let /2 be a simply connected, plane domain (the drumhead) bounded by a smooth curve 7, and consider the wave equation on /2 with Dirichlet boundary condition on 7 (the drumhead is clamped at the boundary): Au(z,t) = ~utt(x,t) in/2, u(z, t) = 0 on 7. The function u(z,t) is the displacement of the drumhead, as it vibrates, at position z and time t. Looking for solutions of the form u(z, t) = Re ei~tv(z) (normal modes) leads to an eigenvalue problem for the Dirichlet Laplacian on B: v(x) = 0 on 7 (1) where A = ~2/c2. We write the infinite sequence of Dirichlet eigenvalues for this problem as {A,(/2)}n=l,c¢ or simply { A-},~=1 co it" the choice of domain/2 is clear in context. Kac's question means the following: is it possible to distinguish "drums" /21 and/22 with distinct (modulo isometrics) bounding curves 71 and 72, simply by "hearing" all of the eigenvalues of the Dirichlet Laplacian? Another way of asking the question is this. What is the geometric content of the eigenvalues of the Laplacian? Is there sufficient geometric information to determine the bounding curve 7 uniquely? In what follows we will call two domains isospectral if all of their Dirichlet eigenvalues are the same. -
Isospectral Towers of Riemannian Manifolds
New York Journal of Mathematics New York J. Math. 18 (2012) 451{461. Isospectral towers of Riemannian manifolds Benjamin Linowitz Abstract. In this paper we construct, for n ≥ 2, arbitrarily large fam- ilies of infinite towers of compact, orientable Riemannian n-manifolds which are isospectral but not isometric at each stage. In dimensions two and three, the towers produced consist of hyperbolic 2-manifolds and hyperbolic 3-manifolds, and in these cases we show that the isospectral towers do not arise from Sunada's method. Contents 1. Introduction 451 2. Genera of quaternion orders 453 3. Arithmetic groups derived from quaternion algebras 454 4. Isospectral towers and chains of quaternion orders 454 5. Proof of Theorem 4.1 456 5.1. Orders in split quaternion algebras over local fields 456 5.2. Proof of Theorem 4.1 457 6. The Sunada construction 458 References 461 1. Introduction Let M be a closed Riemannian n-manifold. The eigenvalues of the La- place{Beltrami operator acting on the space L2(M) form a discrete sequence of nonnegative real numbers in which each value occurs with a finite mul- tiplicity. This collection of eigenvalues is called the spectrum of M, and two Riemannian n-manifolds are said to be isospectral if their spectra coin- cide. Inverse spectral geometry asks the extent to which the geometry and topology of M is determined by its spectrum. Whereas volume and scalar curvature are spectral invariants, the isometry class is not. Although there is a long history of constructing Riemannian manifolds which are isospectral but not isometric, we restrict our discussion to those constructions most Received February 4, 2012. -
Notes on the Spectral Theorem
Math 108b: Notes on the Spectral Theorem From section 6.3, we know that every linear operator T on a finite dimensional inner prod- uct space V has an adjoint. (T ∗ is defined as the unique linear operator on V such that hT (x), yi = hx, T ∗(y)i for every x, y ∈ V – see Theroems 6.8 and 6.9.) When V is infinite dimensional, the adjoint T ∗ may or may not exist. One useful fact (Theorem 6.10) is that if β is an orthonormal basis for a finite dimen- ∗ ∗ sional inner product space V , then [T ]β = [T ]β. That is, the matrix representation of the operator T ∗ is equal to the complex conjugate of the matrix representation for T . For a general vector space V , and a linear operator T , we have already asked the ques- tion “when is there a basis of V consisting only of eigenvectors of T ?” – this is exactly when T is diagonalizable. Now, for an inner product space V , we know how to check whether vec- tors are orthogonal, and we know how to define the norms of vectors, so we can ask “when is there an orthonormal basis of V consisting only of eigenvectors of T ?” Clearly, if there is such a basis, T is diagonalizable – and moreover, eigenvectors with distinct eigenvalues must be orthogonal. Definitions Let V be an inner product space. Let T ∈ L(V ). (a) T is normal if T ∗T = TT ∗ (b) T is self-adjoint if T ∗ = T For the next two definitions, assume V is finite-dimensional: Then, (c) T is unitary if F = C and kT (x)k = kxk for every x ∈ V (d) T is orthogonal if F = R and kT (x)k = kxk for every x ∈ V Notes 1.