Basis (Topology), Basis (Vector Space), Ck, , Change of Variables (Integration), Closed, Closed Form, Closure, Coboundary, Cobou
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De Rham Cohomology
De Rham Cohomology 1. Definition of De Rham Cohomology Let X be an open subset of the plane. If we denote by C0(X) the set of smooth (i. e. infinitely differentiable functions) on X and C1(X), the smooth 1-forms on X (i. e. expressions of the form fdx + gdy where f; g 2 C0(X)), we have natural differntiation map d : C0(X) !C1(X) given by @f @f f 7! dx + dy; @x @y usually denoted by df. The kernel for this map (i. e. set of f with df = 0) is called the zeroth De Rham Cohomology of X and denoted by H0(X). It is clear that these are precisely the set of locally constant functions on X and it is a vector space over R, whose dimension is precisley the number of connected components of X. The image of d is called the set of exact forms on X. The set of pdx + qdy 2 C1(X) @p @q such that @y = @x are called closed forms. It is clear that exact forms and closed forms are vector spaces and any exact form is a closed form. The quotient vector space of closed forms modulo exact forms is called the first De Rham Cohomology and denoted by H1(X). A path for this discussion would mean piecewise smooth. That is, if γ : I ! X is a path (a continuous map), there exists a subdivision, 0 = t0 < t1 < ··· < tn = 1 and γ(t) is continuously differentiable in the open intervals (ti; ti+1) for all i. -
Professor Smith Math 295 Lecture Notes
Professor Smith Math 295 Lecture Notes by John Holler Fall 2010 1 October 29: Compactness, Open Covers, and Subcovers. 1.1 Reexamining Wednesday's proof There seemed to be a bit of confusion about our proof of the generalized Intermediate Value Theorem which is: Theorem 1: If f : X ! Y is continuous, and S ⊆ X is a connected subset (con- nected under the subspace topology), then f(S) is connected. We'll approach this problem by first proving a special case of it: Theorem 1: If f : X ! Y is continuous and surjective and X is connected, then Y is connected. Proof of 2: Suppose not. Then Y is not connected. This by definition means 9 non-empty open sets A; B ⊂ Y such that A S B = Y and A T B = ;. Since f is continuous, both f −1(A) and f −1(B) are open. Since f is surjective, both f −1(A) and f −1(B) are non-empty because A and B are non-empty. But the surjectivity of f also means f −1(A) S f −1(B) = X, since A S B = Y . Additionally we have f −1(A) T f −1(B) = ;. For if not, then we would have that 9x 2 f −1(A) T f −1(B) which implies f(x) 2 A T B = ; which is a contradiction. But then these two non-empty open sets f −1(A) and f −1(B) disconnect X. QED Now we want to show that Theorem 2 implies Theorem 1. This will require the help from a few lemmas, whose proofs are on Homework Set 7: Lemma 1: If f : X ! Y is continuous, and S ⊆ X is any subset of X then the restriction fjS : S ! Y; x 7! f(x) is also continuous. -
Bornologically Isomorphic Representations of Tensor Distributions
Bornologically isomorphic representations of distributions on manifolds E. Nigsch Thursday 15th November, 2018 Abstract Distributional tensor fields can be regarded as multilinear mappings with distributional values or as (classical) tensor fields with distribu- tional coefficients. We show that the corresponding isomorphisms hold also in the bornological setting. 1 Introduction ′ ′ ′r s ′ Let D (M) := Γc(M, Vol(M)) and Ds (M) := Γc(M, Tr(M) ⊗ Vol(M)) be the strong duals of the space of compactly supported sections of the volume s bundle Vol(M) and of its tensor product with the tensor bundle Tr(M) over a manifold; these are the spaces of scalar and tensor distributions on M as defined in [?, ?]. A property of the space of tensor distributions which is fundamental in distributional geometry is given by the C∞(M)-module isomorphisms ′r ∼ s ′ ∼ r ′ Ds (M) = LC∞(M)(Tr (M), D (M)) = Ts (M) ⊗C∞(M) D (M) (1) (cf. [?, Theorem 3.1.12 and Corollary 3.1.15]) where C∞(M) is the space of smooth functions on M. In[?] a space of Colombeau-type nonlinear generalized tensor fields was constructed. This involved handling smooth functions (in the sense of convenient calculus as developed in [?]) in par- arXiv:1105.1642v1 [math.FA] 9 May 2011 ∞ r ′ ticular on the C (M)-module tensor products Ts (M) ⊗C∞(M) D (M) and Γ(E) ⊗C∞(M) Γ(F ), where Γ(E) denotes the space of smooth sections of a vector bundle E over M. In[?], however, only minor attention was paid to questions of topology on these tensor products. -
Math 601 Algebraic Topology Hw 4 Selected Solutions Sketch/Hint
MATH 601 ALGEBRAIC TOPOLOGY HW 4 SELECTED SOLUTIONS SKETCH/HINT QINGYUN ZENG 1. The Seifert-van Kampen theorem 1.1. A refinement of the Seifert-van Kampen theorem. We are going to make a refinement of the theorem so that we don't have to worry about that openness problem. We first start with a definition. Definition 1.1 (Neighbourhood deformation retract). A subset A ⊆ X is a neighbourhood defor- mation retract if there is an open set A ⊂ U ⊂ X such that A is a strong deformation retract of U, i.e. there exists a retraction r : U ! A and r ' IdU relA. This is something that is true most of the time, in sufficiently sane spaces. Example 1.2. If Y is a subcomplex of a cell complex, then Y is a neighbourhood deformation retract. Theorem 1.3. Let X be a space, A; B ⊆ X closed subspaces. Suppose that A, B and A \ B are path connected, and A \ B is a neighbourhood deformation retract of A and B. Then for any x0 2 A \ B. π1(X; x0) = π1(A; x0) ∗ π1(B; x0): π1(A\B;x0) This is just like Seifert-van Kampen theorem, but usually easier to apply, since we no longer have to \fatten up" our A and B to make them open. If you know some sheaf theory, then what Seifert-van Kampen theorem really says is that the fundamental groupoid Π1(X) is a cosheaf on X. Here Π1(X) is a category with object pints in X and morphisms as homotopy classes of path in X, which can be regard as a global version of π1(X). -
Solution: the First Element of the Dual Basis Is the Linear Function Α
Homework assignment 7 pp. 105 3 Exercise 2. Let B = f®1; ®2; ®3g be the basis for C defined by ®1 = (1; 0; ¡1) ®2 = (1; 1; 1) ®3 = (2; 2; 0): Find the dual basis of B. Solution: ¤ The first element of the dual basis is the linear function ®1 such ¤ ¤ ¤ that ®1(®1) = 1; ®1(®2) = 0 and ®1(®3) = 0. To describe such a function more explicitly we need to find its values on the standard basis vectors e1, e2 and e3. To do this express e1; e2; e3 through ®1; ®2; ®3 (refer to the solution of Exercise 1 pp. 54-55 from Homework 6). For each i = 1; 2; 3 you will find the numbers ai; bi; ci such that e1 = ai®1 + bi®2 + ci®3 (i.e. the coordinates of ei relative to the ¤ ¤ ¤ basis ®1; ®2; ®3). Then by linearity of ®1 we get that ®1(ei) = ai. Then ®2(ei) = bi, ¤ and ®3(ei) = ci. This is the answer. It can also be reformulated as follows. If P is the transition matrix from the standard basis e1; e2; e3 to ®1; ®2; ®3, i.e. ¡1 t (®1; ®2; ®3) = (e1; e2; e3)P , then (P ) is the transition matrix from the dual basis ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¡1 t e1; e2; e3 to the dual basis ®1; ®2; a3, i.e. (®1; ®2; a3) = (e1; e2; e3)(P ) . Note that this problem is basically the change of coordinates problem: e.g. ¤ 3 the value of ®1 on the vector v 2 C is the first coordinate of v relative to the basis ®1; ®2; ®3. -
Open Sets in Topological Spaces
International Mathematical Forum, Vol. 14, 2019, no. 1, 41 - 48 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/imf.2019.913 ii – Open Sets in Topological Spaces Amir A. Mohammed and Beyda S. Abdullah Department of Mathematics College of Education University of Mosul, Mosul, Iraq Copyright © 2019 Amir A. Mohammed and Beyda S. Abdullah. This article is distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract In this paper, we introduce a new class of open sets in a topological space called 푖푖 − open sets. We study some properties and several characterizations of this class, also we explain the relation of 푖푖 − open sets with many other classes of open sets. Furthermore, we define 푖푤 − closed sets and 푖푖푤 − closed sets and we give some fundamental properties and relations between these classes and other classes such as 푤 − closed and 훼푤 − closed sets. Keywords: 훼 − open set, 푤 − closed set, 푖 − open set 1. Introduction Throughout this paper we introduce and study the concept of 푖푖 − open sets in topological space (푋, 휏). The 푖푖 − open set is defined as follows: A subset 퐴 of a topological space (푋, 휏) is said to be 푖푖 − open if there exist an open set 퐺 in the topology 휏 of X, such that i. 퐺 ≠ ∅,푋 ii. A is contained in the closure of (A∩ 퐺) iii. interior points of A equal G. One of the classes of open sets that produce a topological space is 훼 − open. -
Vectors and Dual Vectors
Vectors By now you have a pretty good experience with \vectors". Usually, a vector is defined as a quantity that has a direction and a magnitude, such as a position vector, velocity vector, acceleration vector, etc. However, the notion of a vector has a considerably wider realm of applicability than these examples might suggest. The set of all real numbers forms a vector space, as does the set of all complex numbers. The set of functions on a set (e.g., functions of one variable, f(x)) form a vector space. Solutions of linear homogeneous equations form a vector space. We begin by giving the abstract rules for forming a space of vectors, also known as a vector space. A vector space V is a set equipped with an operation of \addition" and an additive identity. The elements of the set are called vectors, which we shall denote as ~u, ~v, ~w, etc. For now, you can think of them as position vectors in order to keep yourself sane. Addition, is an operation in which two vectors, say ~u and ~v, can be combined to make another vector, say, ~w. We denote this operation by the symbol \+": ~u + ~v = ~w: (1) Do not be fooled by this simple notation. The \addition" of vectors may be quite a different operation than ordinary arithmetic addition. For example, if we view position vectors in the x-y plane as \arrows" drawn from the origin, the addition of vectors is defined by the parallelogram rule. Clearly this rule is quite different than ordinary \addition". -
Base-Paracompact Spaces
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Topology and its Applications 128 (2003) 145–156 www.elsevier.com/locate/topol Base-paracompact spaces John E. Porter Department of Mathematics and Statistics, Faculty Hall Room 6C, Murray State University, Murray, KY 42071-3341, USA Received 14 June 2001; received in revised form 21 March 2002 Abstract A topological space is said to be totally paracompact if every open base of it has a locally finite subcover. It turns out this property is very restrictive. In fact, the irrationals are not totally paracompact. Here we give a generalization, base-paracompact, to the notion of totally paracompactness, and study which paracompact spaces satisfy this generalization. 2002 Elsevier Science B.V. All rights reserved. Keywords: Paracompact; Base-paracompact; Totally paracompact 1. Introduction A topological space X is paracompact if every open cover of X has a locally finite open refinement. A topological space is said to be totally paracompact if every open base of it has a locally finite subcover. Corson, McNinn, Michael, and Nagata announced [3] that the irrationals have a basis which does not have a locally finite subcover, and hence are not totally paracompact. Ford [6] was the first to give the definition of totally paracompact spaces. He used this property to show the equivalence of the small and large inductive dimension in metric spaces. Ford also showed paracompact, locally compact spaces are totally paracompact. Lelek [9] studied totally paracompact and totally metacompact separable metric spaces. He showed that these two properties are equivalent in separable metric spaces. -
Duality, Part 1: Dual Bases and Dual Maps Notation
Duality, part 1: Dual Bases and Dual Maps Notation F denotes either R or C. V and W denote vector spaces over F. Define ': R3 ! R by '(x; y; z) = 4x − 5y + 2z. Then ' is a linear functional on R3. n n Fix (b1;:::; bn) 2 C . Define ': C ! C by '(z1;:::; zn) = b1z1 + ··· + bnzn: Then ' is a linear functional on Cn. Define ': P(R) ! R by '(p) = 3p00(5) + 7p(4). Then ' is a linear functional on P(R). R 1 Define ': P(R) ! R by '(p) = 0 p(x) dx. Then ' is a linear functional on P(R). Examples: Linear Functionals Definition: linear functional A linear functional on V is a linear map from V to F. In other words, a linear functional is an element of L(V; F). n n Fix (b1;:::; bn) 2 C . Define ': C ! C by '(z1;:::; zn) = b1z1 + ··· + bnzn: Then ' is a linear functional on Cn. Define ': P(R) ! R by '(p) = 3p00(5) + 7p(4). Then ' is a linear functional on P(R). R 1 Define ': P(R) ! R by '(p) = 0 p(x) dx. Then ' is a linear functional on P(R). Linear Functionals Definition: linear functional A linear functional on V is a linear map from V to F. In other words, a linear functional is an element of L(V; F). Examples: Define ': R3 ! R by '(x; y; z) = 4x − 5y + 2z. Then ' is a linear functional on R3. Define ': P(R) ! R by '(p) = 3p00(5) + 7p(4). Then ' is a linear functional on P(R). R 1 Define ': P(R) ! R by '(p) = 0 p(x) dx. -
MTH 304: General Topology Semester 2, 2017-2018
MTH 304: General Topology Semester 2, 2017-2018 Dr. Prahlad Vaidyanathan Contents I. Continuous Functions3 1. First Definitions................................3 2. Open Sets...................................4 3. Continuity by Open Sets...........................6 II. Topological Spaces8 1. Definition and Examples...........................8 2. Metric Spaces................................. 11 3. Basis for a topology.............................. 16 4. The Product Topology on X × Y ...................... 18 Q 5. The Product Topology on Xα ....................... 20 6. Closed Sets.................................. 22 7. Continuous Functions............................. 27 8. The Quotient Topology............................ 30 III.Properties of Topological Spaces 36 1. The Hausdorff property............................ 36 2. Connectedness................................. 37 3. Path Connectedness............................. 41 4. Local Connectedness............................. 44 5. Compactness................................. 46 6. Compact Subsets of Rn ............................ 50 7. Continuous Functions on Compact Sets................... 52 8. Compactness in Metric Spaces........................ 56 9. Local Compactness.............................. 59 IV.Separation Axioms 62 1. Regular Spaces................................ 62 2. Normal Spaces................................ 64 3. Tietze's extension Theorem......................... 67 4. Urysohn Metrization Theorem........................ 71 5. Imbedding of Manifolds.......................... -
A Some Basic Rules of Tensor Calculus
A Some Basic Rules of Tensor Calculus The tensor calculus is a powerful tool for the description of the fundamentals in con- tinuum mechanics and the derivation of the governing equations for applied prob- lems. In general, there are two possibilities for the representation of the tensors and the tensorial equations: – the direct (symbolic) notation and – the index (component) notation The direct notation operates with scalars, vectors and tensors as physical objects defined in the three dimensional space. A vector (first rank tensor) a is considered as a directed line segment rather than a triple of numbers (coordinates). A second rank tensor A is any finite sum of ordered vector pairs A = a b + ... +c d. The scalars, vectors and tensors are handled as invariant (independent⊗ from the choice⊗ of the coordinate system) objects. This is the reason for the use of the direct notation in the modern literature of mechanics and rheology, e.g. [29, 32, 49, 123, 131, 199, 246, 313, 334] among others. The index notation deals with components or coordinates of vectors and tensors. For a selected basis, e.g. gi, i = 1, 2, 3 one can write a = aig , A = aibj + ... + cidj g g i i ⊗ j Here the Einstein’s summation convention is used: in one expression the twice re- peated indices are summed up from 1 to 3, e.g. 3 3 k k ik ik a gk ∑ a gk, A bk ∑ A bk ≡ k=1 ≡ k=1 In the above examples k is a so-called dummy index. Within the index notation the basic operations with tensors are defined with respect to their coordinates, e. -
General Topology
General Topology Tom Leinster 2014{15 Contents A Topological spaces2 A1 Review of metric spaces.......................2 A2 The definition of topological space.................8 A3 Metrics versus topologies....................... 13 A4 Continuous maps........................... 17 A5 When are two spaces homeomorphic?................ 22 A6 Topological properties........................ 26 A7 Bases................................. 28 A8 Closure and interior......................... 31 A9 Subspaces (new spaces from old, 1)................. 35 A10 Products (new spaces from old, 2)................. 39 A11 Quotients (new spaces from old, 3)................. 43 A12 Review of ChapterA......................... 48 B Compactness 51 B1 The definition of compactness.................... 51 B2 Closed bounded intervals are compact............... 55 B3 Compactness and subspaces..................... 56 B4 Compactness and products..................... 58 B5 The compact subsets of Rn ..................... 59 B6 Compactness and quotients (and images)............. 61 B7 Compact metric spaces........................ 64 C Connectedness 68 C1 The definition of connectedness................... 68 C2 Connected subsets of the real line.................. 72 C3 Path-connectedness.......................... 76 C4 Connected-components and path-components........... 80 1 Chapter A Topological spaces A1 Review of metric spaces For the lecture of Thursday, 18 September 2014 Almost everything in this section should have been covered in Honours Analysis, with the possible exception of some of the examples. For that reason, this lecture is longer than usual. Definition A1.1 Let X be a set. A metric on X is a function d: X × X ! [0; 1) with the following three properties: • d(x; y) = 0 () x = y, for x; y 2 X; • d(x; y) + d(y; z) ≥ d(x; z) for all x; y; z 2 X (triangle inequality); • d(x; y) = d(y; x) for all x; y 2 X (symmetry).