Nelson's Internal Set Theory
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An Introduction to Nonstandard Analysis 11
AN INTRODUCTION TO NONSTANDARD ANALYSIS ISAAC DAVIS Abstract. In this paper we give an introduction to nonstandard analysis, starting with an ultrapower construction of the hyperreals. We then demon- strate how theorems in standard analysis \transfer over" to nonstandard anal- ysis, and how theorems in standard analysis can be proven using theorems in nonstandard analysis. 1. Introduction For many centuries, early mathematicians and physicists would solve problems by considering infinitesimally small pieces of a shape, or movement along a path by an infinitesimal amount. Archimedes derived the formula for the area of a circle by thinking of a circle as a polygon with infinitely many infinitesimal sides [1]. In particular, the construction of calculus was first motivated by this intuitive notion of infinitesimal change. G.W. Leibniz's derivation of calculus made extensive use of “infinitesimal” numbers, which were both nonzero but small enough to add to any real number without changing it noticeably. Although intuitively clear, infinitesi- mals were ultimately rejected as mathematically unsound, and were replaced with the common -δ method of computing limits and derivatives. However, in 1960 Abraham Robinson developed nonstandard analysis, in which the reals are rigor- ously extended to include infinitesimal numbers and infinite numbers; this new extended field is called the field of hyperreal numbers. The goal was to create a system of analysis that was more intuitively appealing than standard analysis but without losing any of the rigor of standard analysis. In this paper, we will explore the construction and various uses of nonstandard analysis. In section 2 we will introduce the notion of an ultrafilter, which will allow us to do a typical ultrapower construction of the hyperreal numbers. -
Actual Infinitesimals in Leibniz's Early Thought
Actual Infinitesimals in Leibniz’s Early Thought By RICHARD T. W. ARTHUR (HAMILTON, ONTARIO) Abstract Before establishing his mature interpretation of infinitesimals as fictions, Gottfried Leibniz had advocated their existence as actually existing entities in the continuum. In this paper I trace the development of these early attempts, distinguishing three distinct phases in his interpretation of infinitesimals prior to his adopting a fictionalist interpretation: (i) (1669) the continuum consists of assignable points separated by unassignable gaps; (ii) (1670-71) the continuum is composed of an infinity of indivisible points, or parts smaller than any assignable, with no gaps between them; (iii) (1672- 75) a continuous line is composed not of points but of infinitely many infinitesimal lines, each of which is divisible and proportional to a generating motion at an instant (conatus). In 1676, finally, Leibniz ceased to regard infinitesimals as actual, opting instead for an interpretation of them as fictitious entities which may be used as compendia loquendi to abbreviate mathematical reasonings. Introduction Gottfried Leibniz’s views on the status of infinitesimals are very subtle, and have led commentators to a variety of different interpretations. There is no proper common consensus, although the following may serve as a summary of received opinion: Leibniz developed the infinitesimal calculus in 1675-76, but during the ensuing twenty years was content to refine its techniques and explore the richness of its applications in co-operation with Johann and Jakob Bernoulli, Pierre Varignon, de l’Hospital and others, without worrying about the ontic status of infinitesimals. Only after the criticisms of Bernard Nieuwentijt and Michel Rolle did he turn himself to the question of the foundations of the calculus and 2 Richard T. -
Kernel and Image
Math 217 Worksheet 1 February: x3.1 Professor Karen E Smith (c)2015 UM Math Dept licensed under a Creative Commons By-NC-SA 4.0 International License. T Definitions: Given a linear transformation V ! W between vector spaces, we have 1. The source or domain of T is V ; 2. The target of T is W ; 3. The image of T is the subset of the target f~y 2 W j ~y = T (~x) for some x 2 Vg: 4. The kernel of T is the subset of the source f~v 2 V such that T (~v) = ~0g. Put differently, the kernel is the pre-image of ~0. Advice to the new mathematicians from an old one: In encountering new definitions and concepts, n m please keep in mind concrete examples you already know|in this case, think about V as R and W as R the first time through. How does the notion of a linear transformation become more concrete in this special case? Think about modeling your future understanding on this case, but be aware that there are other important examples and there are important differences (a linear map is not \a matrix" unless *source and target* are both \coordinate spaces" of column vectors). The goal is to become comfortable with the abstract idea of a vector space which embodies many n features of R but encompasses many other kinds of set-ups. A. For each linear transformation below, determine the source, target, image and kernel. 2 3 x1 3 (a) T : R ! R such that T (4x25) = x1 + x2 + x3. -
Math 120 Homework 3 Solutions
Math 120 Homework 3 Solutions Xiaoyu He, with edits by Prof. Church April 21, 2018 [Note from Prof. Church: solutions to starred problems may not include all details or all portions of the question.] 1.3.1* Let σ be the permutation 1 7! 3; 2 7! 4; 3 7! 5; 4 7! 2; 5 7! 1 and let τ be the permutation 1 7! 5; 2 7! 3; 3 7! 2; 4 7! 4; 5 7! 1. Find the cycle decompositions of each of the following permutations: σ; τ; σ2; στ; τσ; τ 2σ. The cycle decompositions are: σ = (135)(24) τ = (15)(23)(4) σ2 = (153)(2)(4) στ = (1)(2534) τσ = (1243)(5) τ 2σ = (135)(24): 1.3.7* Write out the cycle decomposition of each element of order 2 in S4. Elements of order 2 are also called involutions. There are six formed from a single transposition, (12); (13); (14); (23); (24); (34), and three from pairs of transpositions: (12)(34); (13)(24); (14)(23). 3.1.6* Define ' : R× ! {±1g by letting '(x) be x divided by the absolute value of x. Describe the fibers of ' and prove that ' is a homomorphism. The fibers of ' are '−1(1) = (0; 1) = fall positive realsg and '−1(−1) = (−∞; 0) = fall negative realsg. 3.1.7* Define π : R2 ! R by π((x; y)) = x + y. Prove that π is a surjective homomorphism and describe the kernel and fibers of π geometrically. The map π is surjective because e.g. π((x; 0)) = x. The kernel of π is the line y = −x through the origin. -
Discrete Topological Transformations for Image Processing Michel Couprie, Gilles Bertrand
Discrete Topological Transformations for Image Processing Michel Couprie, Gilles Bertrand To cite this version: Michel Couprie, Gilles Bertrand. Discrete Topological Transformations for Image Processing. Brimkov, Valentin E. and Barneva, Reneta P. Digital Geometry Algorithms, 2, Springer, pp.73-107, 2012, Lecture Notes in Computational Vision and Biomechanics, 978-94-007-4174-4. 10.1007/978-94- 007-4174-4_3. hal-00727377 HAL Id: hal-00727377 https://hal-upec-upem.archives-ouvertes.fr/hal-00727377 Submitted on 3 Sep 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Chapter 3 Discrete Topological Transformations for Image Processing Michel Couprie and Gilles Bertrand Abstract Topology-based image processing operators usually aim at trans- forming an image while preserving its topological characteristics. This chap- ter reviews some approaches which lead to efficient and exact algorithms for topological transformations in 2D, 3D and grayscale images. Some transfor- mations which modify topology in a controlled manner are also described. Finally, based on the framework of critical kernels, we show how to design a topologically sound parallel thinning algorithm guided by a priority function. 3.1 Introduction Topology-preserving operators, such as homotopic thinning and skeletoniza- tion, are used in many applications of image analysis to transform an object while leaving unchanged its topological characteristics. -
The Consistency of Arithmetic
The Consistency of Arithmetic Timothy Y. Chow July 11, 2018 In 2010, Vladimir Voevodsky, a Fields Medalist and a professor at the Insti- tute for Advanced Study, gave a lecture entitled, “What If Current Foundations of Mathematics Are Inconsistent?” Voevodsky invited the audience to consider seriously the possibility that first-order Peano arithmetic (or PA for short) was inconsistent. He briefly discussed two of the standard proofs of the consistency of PA (about which we will say more later), and explained why he did not find either of them convincing. He then said that he was seriously suspicious that an inconsistency in PA might someday be found. About one year later, Voevodsky might have felt vindicated when Edward Nelson, a professor of mathematics at Princeton University, announced that he had a proof not only that PA was inconsistent, but that a small fragment of primitive recursive arithmetic (PRA)—a system that is widely regarded as implementing a very modest and “safe” subset of mathematical reasoning—was inconsistent [11]. However, a fatal error in his proof was soon detected by Daniel Tausk and (independently) Terence Tao. Nelson withdrew his claim, remarking that the consistency of PA remained “an open problem.” For mathematicians without much training in formal logic, these claims by Voevodsky and Nelson may seem bewildering. While the consistency of some axioms of infinite set theory might be debatable, is the consistency of PA really “an open problem,” as Nelson claimed? Are the existing proofs of the con- sistency of PA suspect, as Voevodsky claimed? If so, does this mean that we cannot be sure that even basic mathematical reasoning is consistent? This article is an expanded version of an answer that I posted on the Math- Overflow website in response to the question, “Is PA consistent? do we know arXiv:1807.05641v1 [math.LO] 16 Jul 2018 it?” Since the question of the consistency of PA seems to come up repeat- edly, and continues to generate confusion, a more extended discussion seems worthwhile. -
Kernel Methodsmethods Simple Idea of Data Fitting
KernelKernel MethodsMethods Simple Idea of Data Fitting Given ( xi,y i) i=1,…,n xi is of dimension d Find the best linear function w (hyperplane) that fits the data Two scenarios y: real, regression y: {-1,1}, classification Two cases n>d, regression, least square n<d, ridge regression New sample: x, < x,w> : best fit (regression), best decision (classification) 2 Primary and Dual There are two ways to formulate the problem: Primary Dual Both provide deep insight into the problem Primary is more traditional Dual leads to newer techniques in SVM and kernel methods 3 Regression 2 w = arg min ∑(yi − wo − ∑ xij w j ) W i j w = arg min (y − Xw )T (y − Xw ) W d(y − Xw )T (y − Xw ) = 0 dw ⇒ XT (y − Xw ) = 0 w = [w , w ,L, w ]T , ⇒ T T o 1 d X Xw = X y L T x = ,1[ x1, , xd ] , T −1 T ⇒ w = (X X) X y y = [y , y ,L, y ]T ) 1 2 n T −1 T xT y =< x (, X X) X y > 1 xT X = 2 M xT 4 n n×xd Graphical Interpretation ) y = Xw = Hy = X(XT X)−1 XT y = X(XT X)−1 XT y d X= n FICA Income X is a n (sample size) by d (dimension of data) matrix w combines the columns of X to best approximate y Combine features (FICA, income, etc.) to decisions (loan) H projects y onto the space spanned by columns of X Simplify the decisions to fit the features 5 Problem #1 n=d, exact solution n>d, least square, (most likely scenarios) When n < d, there are not enough constraints to determine coefficients w uniquely d X= n W 6 Problem #2 If different attributes are highly correlated (income and FICA) The columns become dependent Coefficients -
0.999… = 1 an Infinitesimal Explanation Bryan Dawson
0 1 2 0.9999999999999999 0.999… = 1 An Infinitesimal Explanation Bryan Dawson know the proofs, but I still don’t What exactly does that mean? Just as real num- believe it.” Those words were uttered bers have decimal expansions, with one digit for each to me by a very good undergraduate integer power of 10, so do hyperreal numbers. But the mathematics major regarding hyperreals contain “infinite integers,” so there are digits This fact is possibly the most-argued- representing not just (the 237th digit past “Iabout result of arithmetic, one that can evoke great the decimal point) and (the 12,598th digit), passion. But why? but also (the Yth digit past the decimal point), According to Robert Ely [2] (see also Tall and where is a negative infinite hyperreal integer. Vinner [4]), the answer for some students lies in their We have four 0s followed by a 1 in intuition about the infinitely small: While they may the fifth decimal place, and also where understand that the difference between and 1 is represents zeros, followed by a 1 in the Yth less than any positive real number, they still perceive a decimal place. (Since we’ll see later that not all infinite nonzero but infinitely small difference—an infinitesimal hyperreal integers are equal, a more precise, but also difference—between the two. And it’s not just uglier, notation would be students; most professional mathematicians have not or formally studied infinitesimals and their larger setting, the hyperreal numbers, and as a result sometimes Confused? Perhaps a little background information wonder . -
Connes on the Role of Hyperreals in Mathematics
Found Sci DOI 10.1007/s10699-012-9316-5 Tools, Objects, and Chimeras: Connes on the Role of Hyperreals in Mathematics Vladimir Kanovei · Mikhail G. Katz · Thomas Mormann © Springer Science+Business Media Dordrecht 2012 Abstract We examine some of Connes’ criticisms of Robinson’s infinitesimals starting in 1995. Connes sought to exploit the Solovay model S as ammunition against non-standard analysis, but the model tends to boomerang, undercutting Connes’ own earlier work in func- tional analysis. Connes described the hyperreals as both a “virtual theory” and a “chimera”, yet acknowledged that his argument relies on the transfer principle. We analyze Connes’ “dart-throwing” thought experiment, but reach an opposite conclusion. In S, all definable sets of reals are Lebesgue measurable, suggesting that Connes views a theory as being “vir- tual” if it is not definable in a suitable model of ZFC. If so, Connes’ claim that a theory of the hyperreals is “virtual” is refuted by the existence of a definable model of the hyperreal field due to Kanovei and Shelah. Free ultrafilters aren’t definable, yet Connes exploited such ultrafilters both in his own earlier work on the classification of factors in the 1970s and 80s, and in Noncommutative Geometry, raising the question whether the latter may not be vulnera- ble to Connes’ criticism of virtuality. We analyze the philosophical underpinnings of Connes’ argument based on Gödel’s incompleteness theorem, and detect an apparent circularity in Connes’ logic. We document the reliance on non-constructive foundational material, and specifically on the Dixmier trace − (featured on the front cover of Connes’ magnum opus) V. -
Isomorphism Property in Nonstandard Extensions of the ZFC Universe'
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector ANNALS OF PURE AND APPLIED LOGIC Annals of Pure and Applied Logic 88 (1997) l-25 Isomorphism property in nonstandard extensions of the ZFC universe’ Vladimir Kanoveia, *,2, Michael Reekenb aDepartment of Mathematics, Moscow Transport Engineering Institute, Obraztsova 15, Moscow 101475, Russia b Fachbereich Mathematik. Bergische Universitiit GHS Wuppertal, Gauss Strasse 20, Wuppertal 42097, Germany Received 27 April 1996; received in revised form 5 February 1997; accepted 6 February 1997 Communicated by T. Jech Abstract We study models of HST (a nonstandard set theory which includes, in particular, the Re- placement and Separation schemata of ZFC in the language containing the membership and standardness predicates, and Saturation for well-orderable families of internal sets). This theory admits an adequate formulation of the isomorphism property IP, which postulates that any two elementarily equivalent internally presented structures of a well-orderable language are isomor- phic. We prove that IP is independent of HST (using the class of all sets constructible from internal sets) and consistent with HST (using generic extensions of a constructible model of HST by a sufficient number of generic isomorphisms). Keywords: Isomorphism property; Nonstandard set theory; Constructibility; Generic extensions 0. Introduction This article is a continuation of the authors’ series of papers [12-151 devoted to set theoretic foundations of nonstandard mathematics. Our aim is to accomodate an * Corresponding author. E-mail: [email protected]. ’ Expanded version of a talk presented at the Edinburg Meeting on Nonstandard Analysis (Edinburg, August 1996). -
Lecture 25: Ultraproducts
LECTURE 25: ULTRAPRODUCTS CALEB STANFORD First, recall that given a collection of sets and an ultrafilter on the index set, we formed an ultraproduct of those sets. It is important to think of the ultraproduct as a set-theoretic construction rather than a model- theoretic construction, in the sense that it is a product of sets rather than a product of structures. I.e., if Xi Q are sets for i = 1; 2; 3;:::, then Xi=U is another set. The set we use does not depend on what constant, function, and relation symbols may exist and have interpretations in Xi. (There are of course profound model-theoretic consequences of this, but the underlying construction is a way of turning a collection of sets into a new set, and doesn't make use of any notions from model theory!) We are interested in the particular case where the index set is N and where there is a set X such that Q Xi = X for all i. Then Xi=U is written XN=U, and is called the ultrapower of X by U. From now on, we will consider the ultrafilter to be a fixed nonprincipal ultrafilter, and will just consider the ultrapower of X to be the ultrapower by this fixed ultrafilter. It doesn't matter which one we pick, in the sense that none of our results will require anything from U beyond its nonprincipality. The ultrapower has two important properties. The first of these is the Transfer Principle. The second is @0-saturation. 1. The Transfer Principle Let L be a language, X a set, and XL an L-structure on X. -
4. Basic Concepts in This Section We Take X to Be Any Infinite Set Of
401 4. Basic Concepts In this section we take X to be any infinite set of individuals that contains R as a subset and we assume that ∗ : U(X) → U(∗X) is a proper nonstandard extension. The purpose of this section is to introduce three important concepts that are characteristic of arguments using nonstandard analysis: overspill and underspill (consequences of certain sets in U(∗X) not being internal); hy- perfinite sets and hyperfinite sums (combinatorics of hyperfinite objects in U(∗X)); and saturation. Overspill and underspill ∗ ∗ 4.1. Lemma. The sets N, µ(0), and fin( R) are external in U( X). Proof. Every bounded nonempty subset of N has a maximum element. By transfer we conclude that every bounded nonempty internal subset of ∗N ∗ has a maximum element. Since N is a subset of N that is bounded above ∗ (by any infinite element of N) but that has no maximum element, it follows that N is external. Every bounded nonempty subset of R has a least upper bound. By transfer ∗ we conclude that every bounded nonempty internal subset of R has a least ∗ upper bound. Since µ(0) is a bounded nonempty subset of R that has no least upper bound, it follows that µ(0) is external. ∗ ∗ ∗ If fin( R) were internal, so would N = fin( R) ∩ N be internal. Since N is ∗ external, it follows that fin( R) is also external. 4.2. Proposition. (Overspill and Underspill Principles) Let A be an in- ternal set in U(∗X). ∗ (1) (For N) A contains arbitrarily large elements of N if and only if A ∗ contains arbitrarily small infinite elements of N.