Spectral Sequences
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The Leray-Serre Spectral Sequence
THE LERAY-SERRE SPECTRAL SEQUENCE REUBEN STERN Abstract. Spectral sequences are a powerful bookkeeping tool, used to handle large amounts of information. As such, they have become nearly ubiquitous in algebraic topology and algebraic geometry. In this paper, we take a few results on faith (i.e., without proof, pointing to books in which proof may be found) in order to streamline and simplify the exposition. From the exact couple formulation of spectral sequences, we ∗ n introduce a special case of the Leray-Serre spectral sequence and use it to compute H (CP ; Z). Contents 1. Spectral Sequences 1 2. Fibrations and the Leray-Serre Spectral Sequence 4 3. The Gysin Sequence and the Cohomology Ring H∗(CPn; R) 6 References 8 1. Spectral Sequences The modus operandi of algebraic topology is that \algebra is easy; topology is hard." By associating to n a space X an algebraic invariant (the (co)homology groups Hn(X) or H (X), and the homotopy groups πn(X)), with which it is more straightforward to prove theorems and explore structure. For certain com- putations, often involving (co)homology, it is perhaps difficult to determine an invariant directly; one may side-step this computation by approximating it to increasing degrees of accuracy. This approximation is bundled into an object (or a series of objects) known as a spectral sequence. Although spectral sequences often appear formidable to the uninitiated, they provide an invaluable tool to the working topologist, and show their faces throughout algebraic geometry and beyond. ∗;∗ ∗;∗ Loosely speaking, a spectral sequence fEr ; drg is a collection of bigraded modules or vector spaces Er , equipped with a differential map dr (i.e., dr ◦ dr = 0), such that ∗;∗ ∗;∗ Er+1 = H(Er ; dr): That is, a bigraded module in the sequence comes from the previous one by taking homology. -
Spectral Sequences: Friend Or Foe?
SPECTRAL SEQUENCES: FRIEND OR FOE? RAVI VAKIL Spectral sequences are a powerful book-keeping tool for proving things involving com- plicated commutative diagrams. They were introduced by Leray in the 1940's at the same time as he introduced sheaves. They have a reputation for being abstruse and difficult. It has been suggested that the name `spectral' was given because, like spectres, spectral sequences are terrifying, evil, and dangerous. I have heard no one disagree with this interpretation, which is perhaps not surprising since I just made it up. Nonetheless, the goal of this note is to tell you enough that you can use spectral se- quences without hesitation or fear, and why you shouldn't be frightened when they come up in a seminar. What is different in this presentation is that we will use spectral sequence to prove things that you may have already seen, and that you can prove easily in other ways. This will allow you to get some hands-on experience for how to use them. We will also see them only in a “special case” of double complexes (which is the version by far the most often used in algebraic geometry), and not in the general form usually presented (filtered complexes, exact couples, etc.). See chapter 5 of Weibel's marvelous book for more detailed information if you wish. If you want to become comfortable with spectral sequences, you must try the exercises. For concreteness, we work in the category vector spaces over a given field. However, everything we say will apply in any abelian category, such as the category ModA of A- modules. -
The Adams-Novikov Spectral Sequence for the Spheres
BULLETIN OF THE AMERICAN MATHEMATICAL SOCIETY Volume 77, Number 1, January 1971 THE ADAMS-NOVIKOV SPECTRAL SEQUENCE FOR THE SPHERES BY RAPHAEL ZAHLER1 Communicated by P. E. Thomas, June 17, 1970 The Adams spectral sequence has been an important tool in re search on the stable homotopy of the spheres. In this note we outline new information about a variant of the Adams sequence which was introduced by Novikov [7]. We develop simplified techniques of computation which allow us to discover vanishing lines and periodic ity near the edge of the E2-term, interesting elements in E^'*, and a counterexample to one of Novikov's conjectures. In this way we obtain independently the values of many low-dimensional stems up to group extension. The new methods stem from a deeper under standing of the Brown-Peterson cohomology theory, due largely to Quillen [8]; see also [4]. Details will appear elsewhere; or see [ll]. When p is odd, the p-primary part of the Novikov sequence be haves nicely in comparison with the ordinary Adams sequence. Com puting the £2-term seems to be as easy, and the Novikov sequence has many fewer nonzero differentials (in stems ^45, at least, if p = 3), and periodicity near the edge. The case p = 2 is sharply different. Computing E2 is more difficult. There are also hordes of nonzero dif ferentials dz, but they form a regular pattern, and no nonzero differ entials outside the pattern have been found. Thus the diagram of £4 ( =£oo in dimensions ^17) suggests a vanishing line for Ew much lower than that of £2 of the classical Adams spectral sequence [3]. -
CERF THEORY for GRAPHS Allen Hatcher and Karen Vogtmann
CERF THEORY FOR GRAPHS Allen Hatcher and Karen Vogtmann ABSTRACT. We develop a deformation theory for k-parameter families of pointed marked graphs with xed fundamental group Fn. Applications include a simple geometric proof of stability of the rational homology of Aut(Fn), compu- tations of the rational homology in small dimensions, proofs that various natural complexes of free factorizations of Fn are highly connected, and an improvement on the stability range for the integral homology of Aut(Fn). §1. Introduction It is standard practice to study groups by studying their actions on well-chosen spaces. In the case of the outer automorphism group Out(Fn) of the free group Fn on n generators, a good space on which this group acts was constructed in [3]. In the present paper we study an analogous space on which the automorphism group Aut(Fn) acts. This space, which we call An, is a sort of Teichmuller space of nite basepointed graphs with fundamental group Fn. As often happens, specifying basepoints has certain technical advantages. In particu- lar, basepoints give rise to a nice ltration An,0 An,1 of An by degree, where we dene the degree of a nite basepointed graph to be the number of non-basepoint vertices, counted “with multiplicity” (see section 3). The main technical result of the paper, the Degree Theorem, implies that the pair (An, An,k)isk-connected, so that An,k behaves something like the k-skeleton of An. From the Degree Theorem we derive these applications: (1) We show the natural stabilization map Hi(Aut(Fn); Z) → Hi(Aut(Fn+1); Z)isan isomorphism for n 2i + 3, a signicant improvement on the quadratic dimension range obtained in [5]. -
Some Extremely Brief Notes on the Leray Spectral Sequence Greg Friedman
Some extremely brief notes on the Leray spectral sequence Greg Friedman Intro. As a motivating example, consider the long exact homology sequence. We know that if we have a short exact sequence of chain complexes 0 ! C∗ ! D∗ ! E∗ ! 0; then this gives rise to a long exact sequence of homology groups @∗ ! Hi(C∗) ! Hi(D∗) ! Hi(E∗) ! Hi−1(C∗) ! : While we may learn the proof of this, including the definition of the boundary map @∗ in beginning algebraic topology and while this may be useful to know from time to time, we are often happy just to know that there is an exact homology sequence. Also, in many cases the long exact sequence might not be very useful because the interactions from one group to the next might be fairly complicated. However, in especially nice circumstances, we will have reason to know that certain homology groups vanish, or we might be able to compute some of the maps here or there, and then we can derive some consequences. For example, if ∼ we have reason to know that H∗(D∗) = 0, then we learn that H∗(E∗) = H∗−1(C∗). Similarly, to preserve sanity, it is usually worth treating spectral sequences as \black boxes" without looking much under the hood. While the exact details do sometimes come in handy to specialists, spectral sequences can be remarkably useful even without knowing much about how they work. What spectral sequences look like. In long exact sequence above, we have homology groups Hi(C∗) (of course there are other ways to get exact sequences). -
Stable Homology of Automorphism Groups of Free Groups
Annals of Mathematics 173 (2011), 705{768 doi: 10.4007/annals.2011.173.2.3 Stable homology of automorphism groups of free groups By Søren Galatius Abstract Homology of the group Aut(Fn) of automorphisms of a free group on n generators is known to be independent of n in a certain stable range. Using tools from homotopy theory, we prove that in this range it agrees with homology of symmetric groups. In particular we confirm the conjecture that stable rational homology of Aut(Fn) vanishes. Contents 1. Introduction 706 1.1. Results 706 1.2. Outline of proof 708 1.3. Acknowledgements 710 2. The sheaf of graphs 710 2.1. Definitions 710 2.2. Point-set topological properties 714 3. Homotopy types of graph spaces 718 3.1. Graphs in compact sets 718 3.2. Spaces of graph embeddings 721 3.3. Abstract graphs 727 3.4. BOut(Fn) and the graph spectrum 730 4. The graph cobordism category 733 4.1. Poset model of the graph cobordism category 735 4.2. The positive boundary subcategory 743 5. Homotopy type of the graph spectrum 750 5.1. A pushout diagram 751 5.2. A homotopy colimit decomposition 755 5.3. Hatcher's splitting 762 6. Remarks on manifolds 764 References 766 S. Galatius was supported by NSF grants DMS-0505740 and DMS-0805843, and the Clay Mathematics Institute. 705 706 SØREN GALATIUS 1. Introduction 1.1. Results. Let F = x ; : : : ; x be the free group on n generators n h 1 ni and let Aut(Fn) be its automorphism group. -
The Grothendieck Spectral Sequence (Minicourse on Spectral Sequences, UT Austin, May 2017)
The Grothendieck Spectral Sequence (Minicourse on Spectral Sequences, UT Austin, May 2017) Richard Hughes May 12, 2017 1 Preliminaries on derived functors. 1.1 A computational definition of right derived functors. We begin by recalling that a functor between abelian categories F : A!B is called left exact if it takes short exact sequences (SES) in A 0 ! A ! B ! C ! 0 to exact sequences 0 ! FA ! FB ! FC in B. If in fact F takes SES in A to SES in B, we say that F is exact. Question. Can we measure the \failure of exactness" of a left exact functor? The answer to such an obviously leading question is, of course, yes: the right derived functors RpF , which we will define below, are in a precise sense the unique extension of F to an exact functor. Recall that an object I 2 A is called injective if the functor op HomA(−;I): A ! Ab is exact. An injective resolution of A 2 A is a quasi-isomorphism in Ch(A) A ! I• = (I0 ! I1 ! I2 !··· ) where all of the Ii are injective, and where we think of A as a complex concentrated in degree zero. If every A 2 A embeds into some injective object, we say that A has enough injectives { in this situation it is a theorem that every object admits an injective resolution. So, for A 2 A choose an injective resolution A ! I• and define the pth right derived functor of F applied to A by RpF (A) := Hp(F (I•)): Remark • You might worry about whether or not this depends upon our choice of injective resolution for A { it does not, up to canonical isomorphism. -
Part III 3-Manifolds Lecture Notes C Sarah Rasmussen, 2019
Part III 3-manifolds Lecture Notes c Sarah Rasmussen, 2019 Contents Lecture 0 (not lectured): Preliminaries2 Lecture 1: Why not ≥ 5?9 Lecture 2: Why 3-manifolds? + Intro to Knots and Embeddings 13 Lecture 3: Link Diagrams and Alexander Polynomial Skein Relations 17 Lecture 4: Handle Decompositions from Morse critical points 20 Lecture 5: Handles as Cells; Handle-bodies and Heegard splittings 24 Lecture 6: Handle-bodies and Heegaard Diagrams 28 Lecture 7: Fundamental group presentations from handles and Heegaard Diagrams 36 Lecture 8: Alexander Polynomials from Fundamental Groups 39 Lecture 9: Fox Calculus 43 Lecture 10: Dehn presentations and Kauffman states 48 Lecture 11: Mapping tori and Mapping Class Groups 54 Lecture 12: Nielsen-Thurston Classification for Mapping class groups 58 Lecture 13: Dehn filling 61 Lecture 14: Dehn Surgery 64 Lecture 15: 3-manifolds from Dehn Surgery 68 Lecture 16: Seifert fibered spaces 69 Lecture 17: Hyperbolic 3-manifolds and Mostow Rigidity 70 Lecture 18: Dehn's Lemma and Essential/Incompressible Surfaces 71 Lecture 19: Sphere Decompositions and Knot Connected Sum 74 Lecture 20: JSJ Decomposition, Geometrization, Splice Maps, and Satellites 78 Lecture 21: Turaev torsion and Alexander polynomial of unions 81 Lecture 22: Foliations 84 Lecture 23: The Thurston Norm 88 Lecture 24: Taut foliations on Seifert fibered spaces 89 References 92 1 2 Lecture 0 (not lectured): Preliminaries 0. Notation and conventions. Notation. @X { (the manifold given by) the boundary of X, for X a manifold with boundary. th @iX { the i connected component of @X. ν(X) { a tubular (or collared) neighborhood of X in Y , for an embedding X ⊂ Y . -
Notes on Elementary Martingale Theory 1 Conditional Expectations
. Notes on Elementary Martingale Theory by John B. Walsh 1 Conditional Expectations 1.1 Motivation Probability is a measure of ignorance. When new information decreases that ignorance, it changes our probabilities. Suppose we roll a pair of dice, but don't look immediately at the outcome. The result is there for anyone to see, but if we haven't yet looked, as far as we are concerned, the probability that a two (\snake eyes") is showing is the same as it was before we rolled the dice, 1/36. Now suppose that we happen to see that one of the two dice shows a one, but we do not see the second. We reason that we have rolled a two if|and only if|the unseen die is a one. This has probability 1/6. The extra information has changed the probability that our roll is a two from 1/36 to 1/6. It has given us a new probability, which we call a conditional probability. In general, if A and B are events, we say the conditional probability that B occurs given that A occurs is the conditional probability of B given A. This is given by the well-known formula P A B (1) P B A = f \ g; f j g P A f g providing P A > 0. (Just to keep ourselves out of trouble if we need to apply this to a set f g of probability zero, we make the convention that P B A = 0 if P A = 0.) Conditional probabilities are familiar, but that doesn't stop themf fromj g giving risef tog many of the most puzzling paradoxes in probability. -
The Theory of Filtrations of Subalgebras, Standardness and Independence
The theory of filtrations of subalgebras, standardness and independence Anatoly M. Vershik∗ 24.01.2017 In memory of my friend Kolya K. (1933{2014) \Independence is the best quality, the best word in all languages." J. Brodsky. From a personal letter. Abstract The survey is devoted to the combinatorial and metric theory of fil- trations, i. e., decreasing sequences of σ-algebras in measure spaces or decreasing sequences of subalgebras of certain algebras. One of the key notions, that of standardness, plays the role of a generalization of the no- tion of the independence of a sequence of random variables. We discuss the possibility of obtaining a classification of filtrations, their invariants, and various links to problems in algebra, dynamics, and combinatorics. Bibliography: 101 titles. Contents 1 Introduction 3 1.1 A simple example and a difficult question . .3 1.2 Three languages of measure theory . .5 1.3 Where do filtrations appear? . .6 1.4 Finite and infinite in classification problems; standardness as a generalization of independence . .9 arXiv:1705.06619v1 [math.DS] 18 May 2017 1.5 A summary of the paper . 12 2 The definition of filtrations in measure spaces 13 2.1 Measure theory: a Lebesgue space, basic facts . 13 2.2 Measurable partitions, filtrations . 14 2.3 Classification of measurable partitions . 16 ∗St. Petersburg Department of Steklov Mathematical Institute; St. Petersburg State Uni- versity Institute for Information Transmission Problems. E-mail: [email protected]. Par- tially supported by the Russian Science Foundation (grant No. 14-11-00581). 1 2.4 Classification of finite filtrations . 17 2.5 Filtrations we consider and how one can define them . -
11/29/2011 Principal Investigator: Margalit, Dan
Final Report: 1122020 Final Report for Period: 09/2010 - 08/2011 Submitted on: 11/29/2011 Principal Investigator: Margalit, Dan . Award ID: 1122020 Organization: Georgia Tech Research Corp Submitted By: Margalit, Dan - Principal Investigator Title: Algebra and topology of the Johnson filtration Project Participants Senior Personnel Name: Margalit, Dan Worked for more than 160 Hours: Yes Contribution to Project: Post-doc Graduate Student Undergraduate Student Technician, Programmer Other Participant Research Experience for Undergraduates Organizational Partners Other Collaborators or Contacts Mladen Bestvina, University of Utah Kai-Uwe Bux, Bielefeld University Tara Brendle, University of Glasgow Benson Farb, University of Chicago Chris Leininger, University of Illinois at Urbana-Champaign Saul Schleimer, University of Warwick Andy Putman, Rice University Leah Childers, Pittsburg State University Allen Hatcher, Cornell University Activities and Findings Research and Education Activities: Proved various theorems, listed below. Wrote papers explaining the results. Wrote involved computer program to help explore the quotient of Outer space by the Torelli group for Out(F_n). Gave lectures on the subject of this Project at a conference for graduate students called 'Examples of Groups' at Ohio State University, June Page 1 of 5 Final Report: 1122020 2008. Mentored graduate students at the University of Utah in the subject of mapping class groups. Interacted with many students about the book I am writing with Benson Farb on mapping class groups. Gave many talks on this work. Taught a graduate course on my research area, using a book that I translated with Djun Kim. Organized a special session on Geometric Group Theory at MathFest. Finished book with Benson Farb, published by Princeton University Press. -
An Application of Probability Theory in Finance: the Black-Scholes Formula
AN APPLICATION OF PROBABILITY THEORY IN FINANCE: THE BLACK-SCHOLES FORMULA EFFY FANG Abstract. In this paper, we start from the building blocks of probability the- ory, including σ-field and measurable functions, and then proceed to a formal introduction of probability theory. Later, we introduce stochastic processes, martingales and Wiener processes in particular. Lastly, we present an appli- cation - the Black-Scholes Formula, a model used to price options in financial markets. Contents 1. The Building Blocks 1 2. Probability 4 3. Martingales 7 4. Wiener Processes 8 5. The Black-Scholes Formula 9 References 14 1. The Building Blocks Consider the set Ω of all possible outcomes of a random process. When the set is small, for example, the outcomes of tossing a coin, we can analyze case by case. However, when the set gets larger, we would need to study the collections of subsets of Ω that may be taken as a suitable set of events. We define such a collection as a σ-field. Definition 1.1. A σ-field on a set Ω is a collection F of subsets of Ω which obeys the following rules or axioms: (a) Ω 2 F (b) if A 2 F, then Ac 2 F 1 S1 (c) if fAngn=1 is a sequence in F, then n=1 An 2 F The pair (Ω; F) is called a measurable space. Proposition 1.2. Let (Ω; F) be a measurable space. Then (1) ; 2 F k SK (2) if fAngn=1 is a finite sequence of F-measurable sets, then n=1 An 2 F 1 T1 (3) if fAngn=1 is a sequence of F-measurable sets, then n=1 An 2 F Date: August 28,2015.