1300HF: Smooth Manifolds
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Hodge Decomposition for Higher Order Hochschild Homology
ANNALES SCIENTIFIQUES DE L’É.N.S. TEIMURAZ PIRASHVILI Hodge decomposition for higher order Hochschild homology Annales scientifiques de l’É.N.S. 4e série, tome 33, no 2 (2000), p. 151-179 <http://www.numdam.org/item?id=ASENS_2000_4_33_2_151_0> © Gauthier-Villars (Éditions scientifiques et médicales Elsevier), 2000, tous droits réservés. L’accès aux archives de la revue « Annales scientifiques de l’É.N.S. » (http://www. elsevier.com/locate/ansens) implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/conditions). Toute utilisation commerciale ou impression systé- matique est constitutive d’une infraction pénale. Toute copie ou impression de ce fi- chier doit contenir la présente mention de copyright. Article numérisé dans le cadre du programme Numérisation de documents anciens mathématiques http://www.numdam.org/ Ann. Sclent. EC. Norm. Sup., 4° serie, t 33, 2000, p. 151 a 179. HODGE DECOMPOSITION FOR HIGHER ORDER HOCHSCHILD HOMOLOGY TEIMURAZ PIRASHVILI ABSTRACT. - Let r be the category of finite pointed sets and F be a functor from r to the category of vector spaces over a characteristic zero field. Loday proved that one has the natural decomposition 7TnF(S1) ^ ©n^ Q^^CF), n ^ 0. We show that for any d ^ 1, there exists a similar decomposition for 7^nF(Sd). Here 3d is a simplicial model of the d-dimensional sphere. The striking point is, that the knowledge of the decomposition for 7VnF(S1) (respectively 7TnF(S2)) completely determines the decomposition of 7^nF(Sd) for any odd (respectively even) d. These results can be applied to the cohomology of the mapping space Xs , where X is a c^-connected space. -
High Energy and Smoothness Asymptotic Expansion of the Scattering Amplitude
HIGH ENERGY AND SMOOTHNESS ASYMPTOTIC EXPANSION OF THE SCATTERING AMPLITUDE D.Yafaev Department of Mathematics, University Rennes-1, Campus Beaulieu, 35042, Rennes, France (e-mail : [email protected]) Abstract We find an explicit expression for the kernel of the scattering matrix for the Schr¨odinger operator containing at high energies all terms of power order. It turns out that the same expression gives a complete description of the diagonal singular- ities of the kernel in the angular variables. The formula obtained is in some sense universal since it applies both to short- and long-range electric as well as magnetic potentials. 1. INTRODUCTION d−1 d−1 1. High energy asymptotics of the scattering matrix S(λ): L2(S ) → L2(S ) for the d Schr¨odinger operator H = −∆+V in the space H = L2(R ), d ≥ 2, with a real short-range potential (bounded and satisfying the condition V (x) = O(|x|−ρ), ρ > 1, as |x| → ∞) is given by the Born approximation. To describe it, let us introduce the operator Γ0(λ), −1/2 (d−2)/2 ˆ 1/2 d−1 (Γ0(λ)f)(ω) = 2 k f(kω), k = λ ∈ R+ = (0, ∞), ω ∈ S , (1.1) of the restriction (up to the numerical factor) of the Fourier transform fˆ of a function f to −1 −1 the sphere of radius k. Set R0(z) = (−∆ − z) , R(z) = (H − z) . By the Sobolev trace −r d−1 theorem and the limiting absorption principle the operators Γ0(λ)hxi : H → L2(S ) and hxi−rR(λ + i0)hxi−r : H → H are correctly defined as bounded operators for any r > 1/2 and their norms are estimated by λ−1/4 and λ−1/2, respectively. -
Bertini's Theorem on Generic Smoothness
U.F.R. Mathematiques´ et Informatique Universite´ Bordeaux 1 351, Cours de la Liberation´ Master Thesis in Mathematics BERTINI1S THEOREM ON GENERIC SMOOTHNESS Academic year 2011/2012 Supervisor: Candidate: Prof.Qing Liu Andrea Ricolfi ii Introduction Bertini was an Italian mathematician, who lived and worked in the second half of the nineteenth century. The present disser- tation concerns his most celebrated theorem, which appeared for the first time in 1882 in the paper [5], and whose proof can also be found in Introduzione alla Geometria Proiettiva degli Iperspazi (E. Bertini, 1907, or 1923 for the latest edition). The present introduction aims to informally introduce Bertini’s Theorem on generic smoothness, with special attention to its re- cent improvements and its relationships with other kind of re- sults. Just to set the following discussion in an historical perspec- tive, recall that at Bertini’s time the situation was more or less the following: ¥ there were no schemes, ¥ almost all varieties were defined over the complex numbers, ¥ all varieties were embedded in some projective space, that is, they were not intrinsic. On the contrary, this dissertation will cope with Bertini’s the- orem by exploiting the powerful tools of modern algebraic ge- ometry, by working with schemes defined over any field (mostly, but not necessarily, algebraically closed). In addition, our vari- eties will be thought of as abstract varieties (at least when over a field of characteristic zero). This fact does not mean that we are neglecting Bertini’s original work, containing already all the rele- vant ideas: the proof we shall present in this exposition, over the complex numbers, is quite close to the one he gave. -
Jia 84 (1958) 0125-0165
INSTITUTE OF ACTUARIES A MEASURE OF SMOOTHNESS AND SOME REMARKS ON A NEW PRINCIPLE OF GRADUATION BY M. T. L. BIZLEY, F.I.A., F.S.S., F.I.S. [Submitted to the Institute, 27 January 19581 INTRODUCTION THE achievement of smoothness is one of the main purposes of graduation. Smoothness, however, has never been defined except in terms of concepts which themselves defy definition, and there is no accepted way of measuring it. This paper presents an attempt to supply a definition by constructing a quantitative measure of smoothness, and suggests that such a measure may enable us in the future to graduate without prejudicing the process by an arbitrary choice of the form of the relationship between the variables. 2. The customary method of testing smoothness in the case of a series or of a function, by examining successive orders of differences (called hereafter the classical method) is generally recognized as unsatisfactory. Barnett (J.1.A. 77, 18-19) has summarized its shortcomings by pointing out that if we require successive differences to become small, the function must approximate to a polynomial, while if we require that they are to be smooth instead of small we have to judge their smoothness by that of their own differences, and so on ad infinitum. Barnett’s own definition, which he recognizes as being rather vague, is as follows : a series is smooth if it displays a tendency to follow a course similar to that of a simple mathematical function. Although Barnett indicates broadly how the word ‘simple’ is to be interpreted, there is no way of judging decisively between two functions to ascertain which is the simpler, and hence no quantitative or qualitative measure of smoothness emerges; there is a further vagueness inherent in the term ‘similar to’ which would prevent the definition from being satisfactory even if we could decide whether any given function were simple or not. -
Derived Smooth Manifolds
DERIVED SMOOTH MANIFOLDS DAVID I. SPIVAK Abstract. We define a simplicial category called the category of derived man- ifolds. It contains the category of smooth manifolds as a full discrete subcat- egory, and it is closed under taking arbitrary intersections in a manifold. A derived manifold is a space together with a sheaf of local C1-rings that is obtained by patching together homotopy zero-sets of smooth functions on Eu- clidean spaces. We show that derived manifolds come equipped with a stable normal bun- dle and can be imbedded into Euclidean space. We define a cohomology theory called derived cobordism, and use a Pontrjagin-Thom argument to show that the derived cobordism theory is isomorphic to the classical cobordism theory. This allows us to define fundamental classes in cobordism for all derived man- ifolds. In particular, the intersection A \ B of submanifolds A; B ⊂ X exists on the categorical level in our theory, and a cup product formula [A] ^ [B] = [A \ B] holds, even if the submanifolds are not transverse. One can thus consider the theory of derived manifolds as a categorification of intersection theory. Contents 1. Introduction 1 2. The axioms 8 3. Main results 14 4. Layout for the construction of dMan 21 5. C1-rings 23 6. Local C1-ringed spaces and derived manifolds 26 7. Cotangent Complexes 32 8. Proofs of technical results 39 9. Derived manifolds are good for doing intersection theory 50 10. Relationship to similar work 52 References 55 1. Introduction Let Ω denote the unoriented cobordism ring (though what we will say applies to other cobordism theories as well, e.g. -
Lecture 2 — February 25Th 2.1 Smooth Optimization
Statistical machine learning and convex optimization 2016 Lecture 2 — February 25th Lecturer: Francis Bach Scribe: Guillaume Maillard, Nicolas Brosse This lecture deals with classical methods for convex optimization. For a convex function, a local minimum is a global minimum and the uniqueness is assured in the case of strict convexity. In the sequel, g is a convex function on Rd. The aim is to find one (or the) minimum θ Rd and the value of the function at this minimum g(θ ). Some key additional ∗ ∗ properties will∈ be assumed for g : Lipschitz continuity • d θ R , θ 6 D g′(θ) 6 B. ∀ ∈ k k2 ⇒k k2 Smoothness • d 2 (θ , θ ) R , g′(θ ) g′(θ ) 6 L θ θ . ∀ 1 2 ∈ k 1 − 2 k2 k 1 − 2k2 Strong convexity • d 2 µ 2 (θ , θ ) R ,g(θ ) > g(θ )+ g′(θ )⊤(θ θ )+ θ θ . ∀ 1 2 ∈ 1 2 2 1 − 2 2 k 1 − 2k2 We refer to Lecture 1 of this course for additional information on these properties. We point out 2 key references: [1], [2]. 2.1 Smooth optimization If g : Rd R is a convex L-smooth function, we remind that for all θ, η Rd: → ∈ g′(θ) g′(η) 6 L θ η . k − k k − k Besides, if g is twice differentiable, it is equivalent to: 0 4 g′′(θ) 4 LI. Proposition 2.1 (Properties of smooth convex functions) Let g : Rd R be a con- → vex L-smooth function. Then, we have the following inequalities: L 2 1. -
Chapter 16 the Tangent Space and the Notion of Smoothness
Chapter 16 The tangent space and the notion of smoothness We will always assume K algebraically closed. In this chapter we follow the approach of ˇ n Safareviˇc[S]. We define the tangent space TX,P at a point P of an affine variety X A as ⇢ the union of the lines passing through P and “ touching” X at P .Itresultstobeanaffine n subspace of A .Thenwewillfinda“local”characterizationofTX,P ,thistimeinterpreted as a vector space, the direction of T ,onlydependingonthelocalring :thiswill X,P OX,P allow to define the tangent space at a point of any quasi–projective variety. 16.1 Tangent space to an affine variety Assume first that X An is closed and P = O =(0,...,0). Let L be a line through P :if ⇢ A(a1,...,an)isanotherpointofL,thenageneralpointofL has coordinates (ta1,...,tan), t K.IfI(X)=(F ,...,F ), then the intersection X L is determined by the following 2 1 m \ system of equations in the indeterminate t: F (ta ,...,ta )= = F (ta ,...,ta )=0. 1 1 n ··· m 1 n The solutions of this system of equations are the roots of the greatest common divisor G(t)of the polynomials F1(ta1,...,tan),...,Fm(ta1,...,tan)inK[t], i.e. the generator of the ideal they generate. We may factorize G(t)asG(t)=cte(t ↵ )e1 ...(t ↵ )es ,wherec K, − 1 − s 2 ↵ ,...,↵ =0,e, e ,...,e are non-negative, and e>0ifandonlyifP X L.The 1 s 6 1 s 2 \ number e is by definition the intersection multiplicity at P of X and L. -
Math 395: Category Theory Northwestern University, Lecture Notes
Math 395: Category Theory Northwestern University, Lecture Notes Written by Santiago Can˜ez These are lecture notes for an undergraduate seminar covering Category Theory, taught by the author at Northwestern University. The book we roughly follow is “Category Theory in Context” by Emily Riehl. These notes outline the specific approach we’re taking in terms the order in which topics are presented and what from the book we actually emphasize. We also include things we look at in class which aren’t in the book, but otherwise various standard definitions and examples are left to the book. Watch out for typos! Comments and suggestions are welcome. Contents Introduction to Categories 1 Special Morphisms, Products 3 Coproducts, Opposite Categories 7 Functors, Fullness and Faithfulness 9 Coproduct Examples, Concreteness 12 Natural Isomorphisms, Representability 14 More Representable Examples 17 Equivalences between Categories 19 Yoneda Lemma, Functors as Objects 21 Equalizers and Coequalizers 25 Some Functor Properties, An Equivalence Example 28 Segal’s Category, Coequalizer Examples 29 Limits and Colimits 29 More on Limits/Colimits 29 More Limit/Colimit Examples 30 Continuous Functors, Adjoints 30 Limits as Equalizers, Sheaves 30 Fun with Squares, Pullback Examples 30 More Adjoint Examples 30 Stone-Cech 30 Group and Monoid Objects 30 Monads 30 Algebras 30 Ultrafilters 30 Introduction to Categories Category theory provides a framework through which we can relate a construction/fact in one area of mathematics to a construction/fact in another. The goal is an ultimate form of abstraction, where we can truly single out what about a given problem is specific to that problem, and what is a reflection of a more general phenomenom which appears elsewhere. -
Convergence Theorems for Gradient Descent
Convergence Theorems for Gradient Descent Robert M. Gower. October 5, 2018 Abstract Here you will find a growing collection of proofs of the convergence of gradient and stochastic gradient descent type method on convex, strongly convex and/or smooth functions. Important disclaimer: Theses notes do not compare to a good book or well prepared lecture notes. You should only read these notes if you have sat through my lecture on the subject and would like to see detailed notes based on my lecture as a reminder. Under any other circumstances, I highly recommend reading instead the first few chapters of the books [3] and [1]. 1 Assumptions and Lemmas 1.1 Convexity We say that f is convex if d f(tx + (1 − t)y) ≤ tf(x) + (1 − t)f(y); 8x; y 2 R ; t 2 [0; 1]: (1) If f is differentiable and convex then every tangent line to the graph of f lower bounds the function values, that is d f(y) ≥ f(x) + hrf(x); y − xi ; 8x; y 2 R : (2) We can deduce (2) from (1) by dividing by t and re-arranging f(y + t(x − y)) − f(y) ≤ f(x) − f(y): t Now taking the limit t ! 0 gives hrf(y); x − yi ≤ f(x) − f(y): If f is twice differentiable, then taking a directional derivative in the v direction on the point x in (2) gives 2 2 d 0 ≥ hrf(x); vi + r f(x)v; y − x − hrf(x); vi = r f(x)v; y − x ; 8x; y; v 2 R : (3) Setting y = x − v then gives 2 d 0 ≤ r f(x)v; v ; 8x; v 2 R : (4) 1 2 d The above is equivalent to saying the r f(x) 0 is positive semi-definite for every x 2 R : An analogous property to (2) holds even when the function is not differentiable. -
Infinite Dimensional Lie Theory from the Point of View of Functional
Infinite dimensional Lie Theory from the point of view of Functional Analysis Josef Teichmann F¨urmeine Eltern, Michael, Michaela und Hannah Zsofia Hazel. Abstract. Convenient analysis is enlarged by a powerful theory of Hille-Yosida type. More precisely asymptotic spectral properties of bounded operators on a convenient vector space are related to the existence of smooth semigroups in a necessary and sufficient way. An approximation theorem of Trotter-type is proved, too. This approximation theorem is in fact an existence theorem for smooth right evolutions of non-autonomous differential equations on convenient locally convex spaces and crucial for the following applications. To enlighten the generically "unsolved" (even though H. Omori et al. gave interesting and concise conditions for regularity) question of the existence of product integrals on convenient Lie groups, we provide by the given approximation formula some simple criteria. On the one hand linearization is used, on the other hand remarkable families of right invariant distance functions, which exist on all up to now known Lie groups, are the ingredients: Assuming some natural global conditions regularity can be proved on convenient Lie groups. The existence of product integrals is an essential basis for Lie theory in the convenient setting, since generically differential equations cannot be solved on non-normable locally convex spaces. The relationship between infinite dimensional Lie algebras and Lie groups, which is well under- stood in the regular case, is also reviewed from the point of view of local Lie groups: Namely the question under which conditions the existence of a local Lie group for a given convenient Lie algebra implies the existence of a global Lie group is treated by cohomological methods. -
Online Methods in Machine Learning: Recitation 1
Online Methods in Machine Learning: Recitation 1. As seen in class, many offline machine learning problems can be written as: n 1 X min `(w, (xt, yt)) + λR(w), (1) w∈C n t=1 where C is a subset of Rd, R is a penalty function, and ` is the loss function that measures how well our predictor, w, explains the relationship between x and y. Example: Support Vector λ 2 w Machines with R(w) = ||w|| and `(w, (xt, yt)) = max(0, 1 − ythw, xti) where h , xti is 2 2 ||w||2 the signed distance to the hyperplane {x | hw, xi = 0} used to classify the data. A couple of remarks: 1. In the context of classification, ` is typically a convex surrogate loss function in the sense that (i) w → `(w, (x, y)) is convex for any example (x, y) and (ii) `(w, (x, y)) is larger than the true prediction error given by 1yhw,xi≤0 for any example (x, y). In general, we introduce this surrogate loss function because it would be difficult to solve (1) computationally. On the other hand, convexity enables the development of general- purpose methods (e.g. Gradient Descent) that are fast, easy to implement, and simple to analyze. Moreover, the techniques and the proofs generalize very well to the online setting, where the data (xt, yt) arrive sequentially and we have to make predictions online. This theme will be explored later in the class. 2. If ` is a surrogate loss for a classification problem and we solve (1) (e.g with Gradient Descent), we cannot guarantee that our predictor will perform well with respect to our original classification problem n X min 1yhw,xi≤0 + λR(w), (2) w∈C t=1 hence it is crucial to choose a surrogate loss function that reflects accurately the classifi- cation problem at hand. -
Be a Smooth Manifold, and F : M → R a Function
LECTURE 3: SMOOTH FUNCTIONS 1. Smooth Functions Definition 1.1. Let (M; A) be a smooth manifold, and f : M ! R a function. (1) We say f is smooth at p 2 M if there exists a chart f'α;Uα;Vαg 2 A with −1 p 2 Uα, such that the function f ◦ 'α : Vα ! R is smooth at 'α(p). (2) We say f is a smooth function on M if it is smooth at every x 2 M. −1 Remark. Suppose f ◦ 'α is smooth at '(p). Let f'β;Uβ;Vβg be another chart in A with p 2 Uβ. Then by the compatibility of charts, the function −1 −1 −1 f ◦ 'β = (f ◦ 'α ) ◦ ('α ◦ 'β ) must be smooth at 'β(p). So the smoothness of a function is independent of the choice of charts in the given atlas. Remark. According to the chain rule, it is easy to see that if f : M ! R is smooth at p 2 M, and h : R ! R is smooth at f(p), then h ◦ f is smooth at p. We will denote the set of all smooth functions on M by C1(M). Note that this is a (commutative) algebra, i.e. it is a vector space equipped with a (commutative) bilinear \multiplication operation": If f; g are smooth, so are af + bg and fg; moreover, the multiplication is commutative, associative and satisfies the usual distributive laws. 1 n+1 i n Example. Each coordinate function fi(x ; ··· ; x ) = x is a smooth function on S , since ( 2yi −1 1 n 1+jyj2 ; 1 ≤ i ≤ n fi ◦ '± (y ; ··· ; y ) = 1−|yj2 ± 1+jyj2 ; i = n + 1 are smooth functions on Rn.