{f 1 (U), U F} is an open cover of A. Since A is compact there is a finite subcover of A, {f 1 (U 1 ),...,f 1 (U n )}, {U 1,...

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "{f 1 (U), U F} is an open cover of A. Since A is compact there is a finite subcover of A, {f 1 (U 1 ),...,f 1 (U n )}, {U 1,..."

Transcription

1 44 CHAPTER 4. CONTINUOUS FUNCTIONS In Calculus we often use arithmetic operations to generate new continuous functions from old ones. In a general metric space we don t have arithmetic, but much of it is recovered if the range space is a normed vector space or the real numbers. The text has some theorems pointing out that the limit of sum is the sum of the limits, and that when they make sense, the limit of a product is the product of the limits and the limit of a quotient is the quotient of the limits. Compactness and connectedness are preserved by continuous functions. Theorem If M 1 is connected and f : M 1 M 2 is continuous, then f(m 1 ) is connected. Proof. Suppose not. Let f(m 1 ) = V 1 V 2. Then M 1 = f 1 (V 1 ) f 1 (V 2 ) is a separation of M 1, which doesn t exist. Theorem Let A be a subset of a metric space (M 1, d 1 ), and suppose f : A (M 2, d 2 ) is a continuous function. If A is compact, then f(a) is compact. Proof. Let F be a family of open sets covering f(a), with U F. Then {f 1 (U), U F} is an open cover of A. Since A is compact there is a finite subcover of A, {f 1 (U 1 ),...,f 1 (U n )}, so is a cover of f(a). {U 1,..., U n } Proposition Let A be a subset of a metric space (S 1, d 1 ), and suppose f : A (S 2, d 2 ) is a continuous function. If A is compact, and f is one-toone, then f 1 is continuous. Proof. Since f : A f(a) is one-to-one we have a well defined function g = f 1 : f(a) A, and g 1 = (f 1 ) 1 = f. Suppose K is closed in A. Since A is compact, so is K. Then g 1 (K) = f(k) is compact in f(a). But this means g 1 (K) is closed, and so g 1 (K) is closed for every closed set K A.

2 4.1. MAPPINGS FROM ONE METRIC SPACE TO ANOTHER 45 Here are some theorems generalizing results used frequently in Calculus. Theorem If K M is compact and f : K R is continuous, then f has a maximum and a minimum on K. Proof. We know that f(k) R is compact, so bounded. Moreover the set f(k) is closed, so it contains sup x K f(x) and inf x K f(x), which is the claim. In the introductory remarks about connectednes we noted that a version of the Intermediate Value Theorem would hold for continuous functions f : M R if (M, d) was a path connected metric space. A somewhat more general result is true. Theorem Suppose (M, d) is a metric space, f : A M R is continuous, and A is connected. If x, y A, then for every real number c between f(x) and f(y) there is some z A with f(z) = c. Proof. The argument is by contradiction. If not, then the range of f : A R is separated by the open sets U = (, c) and V = (c, ). The open sets f 1 (U), f 1 (V ) provide a separation of A, which can t exist. An other concept that we encountered in our earlier analysis course is the notion of uniform continuity. In terms of ǫ δ language the idea is that in some situations we can pick δ independent of x. We say f : M 1 M 2 is uniformly continuous if for every ǫ > 0 there is a δ > 0, independent of x M 1, such that d 2 (f(x), f(y)) < ǫ if d 1 (x, y) < δ. In Calculus this property is commonly seen when we have a function with a bounded continuous derivative, since f(y) f(x) = y It s useful to know that compactness is enough. x f (t) dt y x sup f (t). t Theorem Suppose (M 1, d 1 ) and (M 2, d 2 ) are metric spaces, and f : M 1 M 2 is continuous. If M 1 is compact, then f is uniformly continuous.

3 46 CHAPTER 4. CONTINUOUS FUNCTIONS Proof. Given ǫ > 0 and x M 1, find disks D(x, δ x ) such that d 1 (x, y) < δ x implies d 2 (f(x), f(y)) < ǫ/2. Shrink the radii, looking at sets D(x, δ x /2). This open cover of M 1 has a finite subcover by sets D(x n, δ n /2). Take δ = min n δ n /2. Now suppose d 1 (x, y) < δ. Since every point is in a set of the cover, x is within δ < δ n /2 of one of the previously selected points x n. Then d 1 (x n, y) d 1 (x n, x) + d 1 (x, y) < δ n, so d 2 (f(x), f(y)) d 2 (f(x), f(x n )) + d 2 (f(y), f(x n )) < ǫ. Here are a few words about treating subsets of metric spaces as independent metric spaces. Suppose (M, d) is a metric space and A M. Then the metric subspace generated by A is the metric space (A, d A ), where d A is the restriction of d to A A. Define a set U A to be open in A if U A is open as a subset of the metric space (A, d A ). Define a set F A to be closed in A if F A is closed as a subset of the metric space (A, d A ). Theorem a) U A is open in A if and only if there is an open set U M such that U A = U A. b) F A is closed in A if and only if there is an closed set F M such that F A = F A. Proof. a) First observe that for x A, D A (x, ǫ) = D M (x, ǫ) A. Suppose U A is open in A, and let x U A. Then there is some ǫ > 0 such that D A (x, ǫ) U A. We have U A = x UA D A (x, ǫ x ) = A [ x UA D M (x, ǫ x )] = A U. Suppose U A = A U, where U is open in M. For every x U A there is an ǫ > 0 such that D M (x, ǫ x ) U. But then we have D A (x, ǫ x ) = A D M (x, ǫ x ) A U = U A. b) Suppose F A is closed in A. Then A \ F A is open in A, so by part (a) A \ F A = A U

4 4.1. MAPPINGS FROM ONE METRIC SPACE TO ANOTHER 47 for some open U in M. Taking complements in A gives F A = A \ (A U) = A [M \ U], which shows that F A = A F for a closed set F M. Suppose that F A = A F for a closed set F M. Then and F A is closed in A. A \ F A = A [M \ F] = A U,

5 48 CHAPTER 4. CONTINUOUS FUNCTIONS

6 Chapter 5 Uniform Convergence 5.1 Uniform Convergence Power series provide one example of the following situation. We represent a function f(x) by a sequence or series of elementary functions. The elementary functions have good properties, like ease of integration or differentiation, and we want these operations to extend to the limit function. Usually this is handled with the concept of uniform convergence. We distinguish two definitions. Suppose A is a set and (M 1, d) is a metric space. Assume f n : A M is a sequence of functions. The sequence {f n } converges pointwise to f : A M if for every ǫ > 0 and every x A there is an N(x, ǫ) such that d(f(x), f n (x)) < ǫ, n N. The sequence {f n } converges uniformly to f : A M if for every ǫ > 0 and every x S there is an N(ǫ) such that d(f(x), f n (x)) < ǫ, n N, x S. Draw an epsilon tube about f to illustrate the definitions. Very little can be said about the pointwise limits of functions. For instance we can find sliding tents f n : [0, 1] R such that f n 0 pointwise, but 1 f n = 1, 0 49

7 50 CHAPTER 5. UNIFORM CONVERGENCE or 1 f n. 0 Similarly, we can construct a sequence of infinitely differentiable functions g n (x) = tan 1 (nx), which converges pointwise to the function g = π/2, x > 0, π/2, x < 0, 0, x = 0.

Metric Spaces. Chapter 1

Metric Spaces. Chapter 1 Chapter 1 Metric Spaces Many of the arguments you have seen in several variable calculus are almost identical to the corresponding arguments in one variable calculus, especially arguments concerning convergence

More information

SOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 3. Spaces with special properties

SOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 3. Spaces with special properties SOLUTIONS TO EXERCISES FOR MATHEMATICS 205A Part 3 Fall 2008 III. Spaces with special properties III.1 : Compact spaces I Problems from Munkres, 26, pp. 170 172 3. Show that a finite union of compact subspaces

More information

9 More on differentiation

9 More on differentiation Tel Aviv University, 2013 Measure and category 75 9 More on differentiation 9a Finite Taylor expansion............... 75 9b Continuous and nowhere differentiable..... 78 9c Differentiable and nowhere monotone......

More information

Lectures 5-6: Taylor Series

Lectures 5-6: Taylor Series Math 1d Instructor: Padraic Bartlett Lectures 5-: Taylor Series Weeks 5- Caltech 213 1 Taylor Polynomials and Series As we saw in week 4, power series are remarkably nice objects to work with. In particular,

More information

Basic Concepts of Point Set Topology Notes for OU course Math 4853 Spring 2011

Basic Concepts of Point Set Topology Notes for OU course Math 4853 Spring 2011 Basic Concepts of Point Set Topology Notes for OU course Math 4853 Spring 2011 A. Miller 1. Introduction. The definitions of metric space and topological space were developed in the early 1900 s, largely

More information

4. Expanding dynamical systems

4. Expanding dynamical systems 4.1. Metric definition. 4. Expanding dynamical systems Definition 4.1. Let X be a compact metric space. A map f : X X is said to be expanding if there exist ɛ > 0 and L > 1 such that d(f(x), f(y)) Ld(x,

More information

THE CENTRAL LIMIT THEOREM TORONTO

THE CENTRAL LIMIT THEOREM TORONTO THE CENTRAL LIMIT THEOREM DANIEL RÜDT UNIVERSITY OF TORONTO MARCH, 2010 Contents 1 Introduction 1 2 Mathematical Background 3 3 The Central Limit Theorem 4 4 Examples 4 4.1 Roulette......................................

More information

MA651 Topology. Lecture 6. Separation Axioms.

MA651 Topology. Lecture 6. Separation Axioms. MA651 Topology. Lecture 6. Separation Axioms. This text is based on the following books: Fundamental concepts of topology by Peter O Neil Elements of Mathematics: General Topology by Nicolas Bourbaki Counterexamples

More information

ALMOST COMMON PRIORS 1. INTRODUCTION

ALMOST COMMON PRIORS 1. INTRODUCTION ALMOST COMMON PRIORS ZIV HELLMAN ABSTRACT. What happens when priors are not common? We introduce a measure for how far a type space is from having a common prior, which we term prior distance. If a type

More information

CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e.

CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e. CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e. This chapter contains the beginnings of the most important, and probably the most subtle, notion in mathematical analysis, i.e.,

More information

Critical points of once continuously differentiable functions are important because they are the only points that can be local maxima or minima.

Critical points of once continuously differentiable functions are important because they are the only points that can be local maxima or minima. Lecture 0: Convexity and Optimization We say that if f is a once continuously differentiable function on an interval I, and x is a point in the interior of I that x is a critical point of f if f (x) =

More information

Chapter 5. Banach Spaces

Chapter 5. Banach Spaces 9 Chapter 5 Banach Spaces Many linear equations may be formulated in terms of a suitable linear operator acting on a Banach space. In this chapter, we study Banach spaces and linear operators acting on

More information

Section 3.7. Rolle s Theorem and the Mean Value Theorem. Difference Equations to Differential Equations

Section 3.7. Rolle s Theorem and the Mean Value Theorem. Difference Equations to Differential Equations Difference Equations to Differential Equations Section.7 Rolle s Theorem and the Mean Value Theorem The two theorems which are at the heart of this section draw connections between the instantaneous rate

More information

Mathematics for Econometrics, Fourth Edition

Mathematics for Econometrics, Fourth Edition Mathematics for Econometrics, Fourth Edition Phoebus J. Dhrymes 1 July 2012 1 c Phoebus J. Dhrymes, 2012. Preliminary material; not to be cited or disseminated without the author s permission. 2 Contents

More information

A ProblemText in Advanced Calculus. John M. Erdman Portland State University. Version July 1, 2014

A ProblemText in Advanced Calculus. John M. Erdman Portland State University. Version July 1, 2014 A ProblemText in Advanced Calculus John M. Erdman Portland State University Version July 1, 2014 c 2005 John M. Erdman E-mail address: erdman@pdx.edu. ii To Argentina Contents PREFACE FOR STUDENTS: HOW

More information

t := maxγ ν subject to ν {0,1,2,...} and f(x c +γ ν d) f(x c )+cγ ν f (x c ;d).

t := maxγ ν subject to ν {0,1,2,...} and f(x c +γ ν d) f(x c )+cγ ν f (x c ;d). 1. Line Search Methods Let f : R n R be given and suppose that x c is our current best estimate of a solution to P min x R nf(x). A standard method for improving the estimate x c is to choose a direction

More information

ISU Department of Mathematics. Graduate Examination Policies and Procedures

ISU Department of Mathematics. Graduate Examination Policies and Procedures ISU Department of Mathematics Graduate Examination Policies and Procedures There are four primary criteria to be used in evaluating competence on written or oral exams. 1. Knowledge Has the student demonstrated

More information

THE FUNDAMENTAL THEOREM OF ALGEBRA VIA PROPER MAPS

THE FUNDAMENTAL THEOREM OF ALGEBRA VIA PROPER MAPS THE FUNDAMENTAL THEOREM OF ALGEBRA VIA PROPER MAPS KEITH CONRAD 1. Introduction The Fundamental Theorem of Algebra says every nonconstant polynomial with complex coefficients can be factored into linear

More information

Taylor and Maclaurin Series

Taylor and Maclaurin Series Taylor and Maclaurin Series In the preceding section we were able to find power series representations for a certain restricted class of functions. Here we investigate more general problems: Which functions

More information

(Basic definitions and properties; Separation theorems; Characterizations) 1.1 Definition, examples, inner description, algebraic properties

(Basic definitions and properties; Separation theorems; Characterizations) 1.1 Definition, examples, inner description, algebraic properties Lecture 1 Convex Sets (Basic definitions and properties; Separation theorems; Characterizations) 1.1 Definition, examples, inner description, algebraic properties 1.1.1 A convex set In the school geometry

More information

1 = (a 0 + b 0 α) 2 + + (a m 1 + b m 1 α) 2. for certain elements a 0,..., a m 1, b 0,..., b m 1 of F. Multiplying out, we obtain

1 = (a 0 + b 0 α) 2 + + (a m 1 + b m 1 α) 2. for certain elements a 0,..., a m 1, b 0,..., b m 1 of F. Multiplying out, we obtain Notes on real-closed fields These notes develop the algebraic background needed to understand the model theory of real-closed fields. To understand these notes, a standard graduate course in algebra is

More information

Some stability results of parameter identification in a jump diffusion model

Some stability results of parameter identification in a jump diffusion model Some stability results of parameter identification in a jump diffusion model D. Düvelmeyer Technische Universität Chemnitz, Fakultät für Mathematik, 09107 Chemnitz, Germany Abstract In this paper we discuss

More information

1 The Brownian bridge construction

1 The Brownian bridge construction The Brownian bridge construction The Brownian bridge construction is a way to build a Brownian motion path by successively adding finer scale detail. This construction leads to a relatively easy proof

More information

Further Study on Strong Lagrangian Duality Property for Invex Programs via Penalty Functions 1

Further Study on Strong Lagrangian Duality Property for Invex Programs via Penalty Functions 1 Further Study on Strong Lagrangian Duality Property for Invex Programs via Penalty Functions 1 J. Zhang Institute of Applied Mathematics, Chongqing University of Posts and Telecommunications, Chongqing

More information

2.3 Convex Constrained Optimization Problems

2.3 Convex Constrained Optimization Problems 42 CHAPTER 2. FUNDAMENTAL CONCEPTS IN CONVEX OPTIMIZATION Theorem 15 Let f : R n R and h : R R. Consider g(x) = h(f(x)) for all x R n. The function g is convex if either of the following two conditions

More information

UNIFORMLY DISCONTINUOUS GROUPS OF ISOMETRIES OF THE PLANE

UNIFORMLY DISCONTINUOUS GROUPS OF ISOMETRIES OF THE PLANE UNIFORMLY DISCONTINUOUS GROUPS OF ISOMETRIES OF THE PLANE NINA LEUNG Abstract. This paper discusses 2-dimensional locally Euclidean geometries and how these geometries can describe musical chords. Contents

More information

Random graphs with a given degree sequence

Random graphs with a given degree sequence Sourav Chatterjee (NYU) Persi Diaconis (Stanford) Allan Sly (Microsoft) Let G be an undirected simple graph on n vertices. Let d 1,..., d n be the degrees of the vertices of G arranged in descending order.

More information

The Heat Equation. Lectures INF2320 p. 1/88

The Heat Equation. Lectures INF2320 p. 1/88 The Heat Equation Lectures INF232 p. 1/88 Lectures INF232 p. 2/88 The Heat Equation We study the heat equation: u t = u xx for x (,1), t >, (1) u(,t) = u(1,t) = for t >, (2) u(x,) = f(x) for x (,1), (3)

More information

Chapter 20. Vector Spaces and Bases

Chapter 20. Vector Spaces and Bases Chapter 20. Vector Spaces and Bases In this course, we have proceeded step-by-step through low-dimensional Linear Algebra. We have looked at lines, planes, hyperplanes, and have seen that there is no limit

More information

Adaptive Online Gradient Descent

Adaptive Online Gradient Descent Adaptive Online Gradient Descent Peter L Bartlett Division of Computer Science Department of Statistics UC Berkeley Berkeley, CA 94709 bartlett@csberkeleyedu Elad Hazan IBM Almaden Research Center 650

More information

F. ABTAHI and M. ZARRIN. (Communicated by J. Goldstein)

F. ABTAHI and M. ZARRIN. (Communicated by J. Goldstein) Journal of Algerian Mathematical Society Vol. 1, pp. 1 6 1 CONCERNING THE l p -CONJECTURE FOR DISCRETE SEMIGROUPS F. ABTAHI and M. ZARRIN (Communicated by J. Goldstein) Abstract. For 2 < p

More information

Tiers, Preference Similarity, and the Limits on Stable Partners

Tiers, Preference Similarity, and the Limits on Stable Partners Tiers, Preference Similarity, and the Limits on Stable Partners KANDORI, Michihiro, KOJIMA, Fuhito, and YASUDA, Yosuke February 7, 2010 Preliminary and incomplete. Do not circulate. Abstract We consider

More information

Chapter 7. Continuity

Chapter 7. Continuity Chapter 7 Continuity There are many processes and eects that depends on certain set of variables in such a way that a small change in these variables acts as small change in the process. Changes of this

More information

The Dirichlet Unit Theorem

The Dirichlet Unit Theorem Chapter 6 The Dirichlet Unit Theorem As usual, we will be working in the ring B of algebraic integers of a number field L. Two factorizations of an element of B are regarded as essentially the same if

More information

The Steepest Descent Algorithm for Unconstrained Optimization and a Bisection Line-search Method

The Steepest Descent Algorithm for Unconstrained Optimization and a Bisection Line-search Method The Steepest Descent Algorithm for Unconstrained Optimization and a Bisection Line-search Method Robert M. Freund February, 004 004 Massachusetts Institute of Technology. 1 1 The Algorithm The problem

More information

Point Set Topology. A. Topological Spaces and Continuous Maps

Point Set Topology. A. Topological Spaces and Continuous Maps Point Set Topology A. Topological Spaces and Continuous Maps Definition 1.1 A topology on a set X is a collection T of subsets of X satisfying the following axioms: T 1.,X T. T2. {O α α I} T = α IO α T.

More information

Notes from Week 1: Algorithms for sequential prediction

Notes from Week 1: Algorithms for sequential prediction CS 683 Learning, Games, and Electronic Markets Spring 2007 Notes from Week 1: Algorithms for sequential prediction Instructor: Robert Kleinberg 22-26 Jan 2007 1 Introduction In this course we will be looking

More information

Convex analysis and profit/cost/support functions

Convex analysis and profit/cost/support functions CALIFORNIA INSTITUTE OF TECHNOLOGY Division of the Humanities and Social Sciences Convex analysis and profit/cost/support functions KC Border October 2004 Revised January 2009 Let A be a subset of R m

More information

DISINTEGRATION OF MEASURES

DISINTEGRATION OF MEASURES DISINTEGRTION OF MESURES BEN HES Definition 1. Let (, M, λ), (, N, µ) be sigma-finite measure spaces and let T : be a measurable map. (T, µ)-disintegration is a collection {λ y } y of measures on M such

More information

Optimal Online-list Batch Scheduling

Optimal Online-list Batch Scheduling Optimal Online-list Batch Scheduling Jacob Jan Paulus a,, Deshi Ye b, Guochuan Zhang b a University of Twente, P.O. box 217, 7500AE Enschede, The Netherlands b Zhejiang University, Hangzhou 310027, China

More information

RANDOM INTERVAL HOMEOMORPHISMS. MICHA L MISIUREWICZ Indiana University Purdue University Indianapolis

RANDOM INTERVAL HOMEOMORPHISMS. MICHA L MISIUREWICZ Indiana University Purdue University Indianapolis RANDOM INTERVAL HOMEOMORPHISMS MICHA L MISIUREWICZ Indiana University Purdue University Indianapolis This is a joint work with Lluís Alsedà Motivation: A talk by Yulij Ilyashenko. Two interval maps, applied

More information

Geometrical Characterization of RN-operators between Locally Convex Vector Spaces

Geometrical Characterization of RN-operators between Locally Convex Vector Spaces Geometrical Characterization of RN-operators between Locally Convex Vector Spaces OLEG REINOV St. Petersburg State University Dept. of Mathematics and Mechanics Universitetskii pr. 28, 198504 St, Petersburg

More information

Rate of convergence towards Hartree dynamics

Rate of convergence towards Hartree dynamics Rate of convergence towards Hartree dynamics Benjamin Schlein, LMU München and University of Cambridge Universitá di Milano Bicocca, October 22, 2007 Joint work with I. Rodnianski 1. Introduction boson

More information

Lecture 13 Linear quadratic Lyapunov theory

Lecture 13 Linear quadratic Lyapunov theory EE363 Winter 28-9 Lecture 13 Linear quadratic Lyapunov theory the Lyapunov equation Lyapunov stability conditions the Lyapunov operator and integral evaluating quadratic integrals analysis of ARE discrete-time

More information

A new continuous dependence result for impulsive retarded functional differential equations

A new continuous dependence result for impulsive retarded functional differential equations CADERNOS DE MATEMÁTICA 11, 37 47 May (2010) ARTIGO NÚMERO SMA#324 A new continuous dependence result for impulsive retarded functional differential equations M. Federson * Instituto de Ciências Matemáticas

More information

ON COMPLETELY CONTINUOUS INTEGRATION OPERATORS OF A VECTOR MEASURE. 1. Introduction

ON COMPLETELY CONTINUOUS INTEGRATION OPERATORS OF A VECTOR MEASURE. 1. Introduction ON COMPLETELY CONTINUOUS INTEGRATION OPERATORS OF A VECTOR MEASURE J.M. CALABUIG, J. RODRÍGUEZ, AND E.A. SÁNCHEZ-PÉREZ Abstract. Let m be a vector measure taking values in a Banach space X. We prove that

More information

Real Number Computability and Domain Theory

Real Number Computability and Domain Theory Real Number Computability and Domain Theory Pietro Di Gianantonio dipartimento di Matematica e Informatica, Università di Udine via delle Scienze 206 I-33100 Udine Italy E-mail: pietro@dimi.uniud.it Abstract

More information

INTRODUCTION TO ALGEBRAIC GEOMETRY, CLASS 24

INTRODUCTION TO ALGEBRAIC GEOMETRY, CLASS 24 INTRODUCTION TO ALGEBRAIC GEOMETRY, CLASS 24 RAVI VAKIL Contents 1. Degree of a line bundle / invertible sheaf 1 1.1. Last time 1 1.2. New material 2 2. The sheaf of differentials of a nonsingular curve

More information

Linear Algebra I. Ronald van Luijk, 2012

Linear Algebra I. Ronald van Luijk, 2012 Linear Algebra I Ronald van Luijk, 2012 With many parts from Linear Algebra I by Michael Stoll, 2007 Contents 1. Vector spaces 3 1.1. Examples 3 1.2. Fields 4 1.3. The field of complex numbers. 6 1.4.

More information

1 Sufficient statistics

1 Sufficient statistics 1 Sufficient statistics A statistic is a function T = rx 1, X 2,, X n of the random sample X 1, X 2,, X n. Examples are X n = 1 n s 2 = = X i, 1 n 1 the sample mean X i X n 2, the sample variance T 1 =

More information

Fixed Point Theorems in Topology and Geometry

Fixed Point Theorems in Topology and Geometry Fixed Point Theorems in Topology and Geometry A Senior Thesis Submitted to the Department of Mathematics In Partial Fulfillment of the Requirements for the Departmental Honors Baccalaureate By Morgan Schreffler

More information

Surface bundles over S 1, the Thurston norm, and the Whitehead link

Surface bundles over S 1, the Thurston norm, and the Whitehead link Surface bundles over S 1, the Thurston norm, and the Whitehead link Michael Landry August 16, 2014 The Thurston norm is a powerful tool for studying the ways a 3-manifold can fiber over the circle. In

More information

MATH 132: CALCULUS II SYLLABUS

MATH 132: CALCULUS II SYLLABUS MATH 32: CALCULUS II SYLLABUS Prerequisites: Successful completion of Math 3 (or its equivalent elsewhere). Math 27 is normally not a sufficient prerequisite for Math 32. Required Text: Calculus: Early

More information

NOV - 30211/II. 1. Let f(z) = sin z, z C. Then f(z) : 3. Let the sequence {a n } be given. (A) is bounded in the complex plane

NOV - 30211/II. 1. Let f(z) = sin z, z C. Then f(z) : 3. Let the sequence {a n } be given. (A) is bounded in the complex plane Mathematical Sciences Paper II Time Allowed : 75 Minutes] [Maximum Marks : 100 Note : This Paper contains Fifty (50) multiple choice questions. Each question carries Two () marks. Attempt All questions.

More information

Persuasion by Cheap Talk - Online Appendix

Persuasion by Cheap Talk - Online Appendix Persuasion by Cheap Talk - Online Appendix By ARCHISHMAN CHAKRABORTY AND RICK HARBAUGH Online appendix to Persuasion by Cheap Talk, American Economic Review Our results in the main text concern the case

More information

On a conjecture by Palis

On a conjecture by Palis 1179 2000 103-108 103 On a conjecture by Palis (Shuhei Hayashi) Introduction Let $M$ be a smooth compact manifold without boundary and let Diff1 $(M)$ be the set of $C^{1}$ diffeomorphisms with the $C^{1}$

More information

Verifying Numerical Convergence Rates

Verifying Numerical Convergence Rates 1 Order of accuracy Verifying Numerical Convergence Rates We consider a numerical approximation of an exact value u. Te approximation depends on a small parameter, suc as te grid size or time step, and

More information

Mathematical Induction

Mathematical Induction Mathematical Induction (Handout March 8, 01) The Principle of Mathematical Induction provides a means to prove infinitely many statements all at once The principle is logical rather than strictly mathematical,

More information

Notes V General Equilibrium: Positive Theory. 1 Walrasian Equilibrium and Excess Demand

Notes V General Equilibrium: Positive Theory. 1 Walrasian Equilibrium and Excess Demand Notes V General Equilibrium: Positive Theory In this lecture we go on considering a general equilibrium model of a private ownership economy. In contrast to the Notes IV, we focus on positive issues such

More information

1. (First passage/hitting times/gambler s ruin problem:) Suppose that X has a discrete state space and let i be a fixed state. Let

1. (First passage/hitting times/gambler s ruin problem:) Suppose that X has a discrete state space and let i be a fixed state. Let Copyright c 2009 by Karl Sigman 1 Stopping Times 1.1 Stopping Times: Definition Given a stochastic process X = {X n : n 0}, a random time τ is a discrete random variable on the same probability space as

More information

1 Formulating The Low Degree Testing Problem

1 Formulating The Low Degree Testing Problem 6.895 PCP and Hardness of Approximation MIT, Fall 2010 Lecture 5: Linearity Testing Lecturer: Dana Moshkovitz Scribe: Gregory Minton and Dana Moshkovitz In the last lecture, we proved a weak PCP Theorem,

More information

Logic, Algebra and Truth Degrees 2008. Siena. A characterization of rst order rational Pavelka's logic

Logic, Algebra and Truth Degrees 2008. Siena. A characterization of rst order rational Pavelka's logic Logic, Algebra and Truth Degrees 2008 September 8-11, 2008 Siena A characterization of rst order rational Pavelka's logic Xavier Caicedo Universidad de los Andes, Bogota Under appropriate formulations,

More information

Sequences and Series

Sequences and Series Sequences and Series Consider the following sum: 2 + 4 + 8 + 6 + + 2 i + The dots at the end indicate that the sum goes on forever. Does this make sense? Can we assign a numerical value to an infinite

More information

Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson

Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson Mathematics for Computer Science/Software Engineering Notes for the course MSM1F3 Dr. R. A. Wilson October 1996 Chapter 1 Logic Lecture no. 1. We introduce the concept of a proposition, which is a statement

More information

Hedging bounded claims with bounded outcomes

Hedging bounded claims with bounded outcomes Hedging bounded claims with bounded outcomes Freddy Delbaen ETH Zürich, Department of Mathematics, CH-892 Zurich, Switzerland Abstract. We consider a financial market with two or more separate components

More information

Estimating the Degree of Activity of jumps in High Frequency Financial Data. joint with Yacine Aït-Sahalia

Estimating the Degree of Activity of jumps in High Frequency Financial Data. joint with Yacine Aït-Sahalia Estimating the Degree of Activity of jumps in High Frequency Financial Data joint with Yacine Aït-Sahalia Aim and setting An underlying process X = (X t ) t 0, observed at equally spaced discrete times

More information

Interpersonal Comparisons of Utility: An Algebraic Characterization of Projective Preorders and Some Welfare Consequences

Interpersonal Comparisons of Utility: An Algebraic Characterization of Projective Preorders and Some Welfare Consequences DISCUSSION PAPER SERIES IZA DP No. 2594 Interpersonal Comparisons of Utility: An Algebraic Characterization of Projective Preorders and Some Welfare Consequences Juan Carlos Candeal Esteban Induráin José

More information

TOPOLOGY: THE JOURNEY INTO SEPARATION AXIOMS

TOPOLOGY: THE JOURNEY INTO SEPARATION AXIOMS TOPOLOGY: THE JOURNEY INTO SEPARATION AXIOMS VIPUL NAIK Abstract. In this journey, we are going to explore the so called separation axioms in greater detail. We shall try to understand how these axioms

More information

Turing Degrees and Definability of the Jump. Theodore A. Slaman. University of California, Berkeley. CJuly, 2005

Turing Degrees and Definability of the Jump. Theodore A. Slaman. University of California, Berkeley. CJuly, 2005 Turing Degrees and Definability of the Jump Theodore A. Slaman University of California, Berkeley CJuly, 2005 Outline Lecture 1 Forcing in arithmetic Coding and decoding theorems Automorphisms of countable

More information

Math/Stats 425 Introduction to Probability. 1. Uncertainty and the axioms of probability

Math/Stats 425 Introduction to Probability. 1. Uncertainty and the axioms of probability Math/Stats 425 Introduction to Probability 1. Uncertainty and the axioms of probability Processes in the real world are random if outcomes cannot be predicted with certainty. Example: coin tossing, stock

More information

DIRICHLET S PROBLEM WITH ENTIRE DATA POSED ON AN ELLIPSOIDAL CYLINDER. 1. Introduction

DIRICHLET S PROBLEM WITH ENTIRE DATA POSED ON AN ELLIPSOIDAL CYLINDER. 1. Introduction DIRICHLET S PROBLEM WITH ENTIRE DATA POSED ON AN ELLIPSOIDAL CYLINDER DMITRY KHAVINSON, ERIK LUNDBERG, HERMANN RENDER. Introduction A function u is said to be harmonic if u := n j= 2 u = 0. Given a domain

More information

MINIMUM VERTEX DEGREE CONDITIONS FOR LOOSE HAMILTON CYCLES IN 3-UNIFORM HYPERGRAPHS., where

MINIMUM VERTEX DEGREE CONDITIONS FOR LOOSE HAMILTON CYCLES IN 3-UNIFORM HYPERGRAPHS., where MINIMUM VERTEX DEGREE CONDITIONS FOR LOOSE HAMILTON CYCLES IN -UNIFORM HYPERGRAPHS ENNO BUSS, HIỆP HÀN, AND MATHIAS SCHACHT Abstract. We investigate minimum vertex degree conditions for -uniform hypergraphs

More information

arxiv:0805.1169v3 [math.oc] 12 Oct 2008

arxiv:0805.1169v3 [math.oc] 12 Oct 2008 GEOMETRIC APPROACH TO PONTRYAGIN S MAXIMUM PRINCIPLE arxiv:0805.1169v3 [math.oc] 12 Oct 2008 M. Barbero-Liñán, M. C. Muñoz-Lecanda Departamento de Matemática Aplicada IV Edificio C 3, Campus Norte UPC.

More information

Lebesgue Measure on R n

Lebesgue Measure on R n 8 CHAPTER 2 Lebesgue Measure on R n Our goal is to construct a notion of the volume, or Lebesgue measure, of rather general subsets of R n that reduces to the usual volume of elementary geometrical sets

More information

Date: April 12, 2001. Contents

Date: April 12, 2001. Contents 2 Lagrange Multipliers Date: April 12, 2001 Contents 2.1. Introduction to Lagrange Multipliers......... p. 2 2.2. Enhanced Fritz John Optimality Conditions...... p. 12 2.3. Informative Lagrange Multipliers...........

More information

A simple criterion on degree sequences of graphs

A simple criterion on degree sequences of graphs Discrete Applied Mathematics 156 (2008) 3513 3517 Contents lists available at ScienceDirect Discrete Applied Mathematics journal homepage: www.elsevier.com/locate/dam Note A simple criterion on degree

More information

INTRODUCTORY SET THEORY

INTRODUCTORY SET THEORY M.Sc. program in mathematics INTRODUCTORY SET THEORY Katalin Károlyi Department of Applied Analysis, Eötvös Loránd University H-1088 Budapest, Múzeum krt. 6-8. CONTENTS 1. SETS Set, equal sets, subset,

More information

A power series about x = a is the series of the form

A power series about x = a is the series of the form POWER SERIES AND THE USES OF POWER SERIES Elizabeth Wood Now we are finally going to start working with a topic that uses all of the information from the previous topics. The topic that we are going to

More information

Erdős on polynomials

Erdős on polynomials Erdős on polynomials Vilmos Totik University of Szeged and University of South Florida totik@mail.usf.edu Vilmos Totik (SZTE and USF) Polynomials 1 / * Erdős on polynomials Vilmos Totik (SZTE and USF)

More information

Extension of measure

Extension of measure 1 Extension of measure Sayan Mukherjee Dynkin s π λ theorem We will soon need to define probability measures on infinite and possible uncountable sets, like the power set of the naturals. This is hard.

More information

Reference: Introduction to Partial Differential Equations by G. Folland, 1995, Chap. 3.

Reference: Introduction to Partial Differential Equations by G. Folland, 1995, Chap. 3. 5 Potential Theory Reference: Introduction to Partial Differential Equations by G. Folland, 995, Chap. 3. 5. Problems of Interest. In what follows, we consider Ω an open, bounded subset of R n with C 2

More information

Reading material on the limit set of a Fuchsian group

Reading material on the limit set of a Fuchsian group Reading material on the limit set of a Fuchsian group Recommended texts Many books on hyperbolic geometry and Kleinian and Fuchsian groups contain material about limit sets. The presentation given here

More information

LINEAR PROGRAMMING WITH ONLINE LEARNING

LINEAR PROGRAMMING WITH ONLINE LEARNING LINEAR PROGRAMMING WITH ONLINE LEARNING TATSIANA LEVINA, YURI LEVIN, JEFF MCGILL, AND MIKHAIL NEDIAK SCHOOL OF BUSINESS, QUEEN S UNIVERSITY, 143 UNION ST., KINGSTON, ON, K7L 3N6, CANADA E-MAIL:{TLEVIN,YLEVIN,JMCGILL,MNEDIAK}@BUSINESS.QUEENSU.CA

More information

17.3.1 Follow the Perturbed Leader

17.3.1 Follow the Perturbed Leader CS787: Advanced Algorithms Topic: Online Learning Presenters: David He, Chris Hopman 17.3.1 Follow the Perturbed Leader 17.3.1.1 Prediction Problem Recall the prediction problem that we discussed in class.

More information

Properties of BMO functions whose reciprocals are also BMO

Properties of BMO functions whose reciprocals are also BMO Properties of BMO functions whose reciprocals are also BMO R. L. Johnson and C. J. Neugebauer The main result says that a non-negative BMO-function w, whose reciprocal is also in BMO, belongs to p> A p,and

More information

Analytic cohomology groups in top degrees of Zariski open sets in P n

Analytic cohomology groups in top degrees of Zariski open sets in P n Analytic cohomology groups in top degrees of Zariski open sets in P n Gabriel Chiriacescu, Mihnea Colţoiu, Cezar Joiţa Dedicated to Professor Cabiria Andreian Cazacu on her 80 th birthday 1 Introduction

More information

CHAPTER 5. Product Measures

CHAPTER 5. Product Measures 54 CHAPTER 5 Product Measures Given two measure spaces, we may construct a natural measure on their Cartesian product; the prototype is the construction of Lebesgue measure on R 2 as the product of Lebesgue

More information

Gambling Systems and Multiplication-Invariant Measures

Gambling Systems and Multiplication-Invariant Measures Gambling Systems and Multiplication-Invariant Measures by Jeffrey S. Rosenthal* and Peter O. Schwartz** (May 28, 997.. Introduction. This short paper describes a surprising connection between two previously

More information

CHAPTER 1 BASIC TOPOLOGY

CHAPTER 1 BASIC TOPOLOGY CHAPTER 1 BASIC TOPOLOGY Topology, sometimes referred to as the mathematics of continuity, or rubber sheet geometry, or the theory of abstract topological spaces, is all of these, but, above all, it is

More information

The Henstock-Kurzweil-Stieltjes type integral for real functions on a fractal subset of the real line

The Henstock-Kurzweil-Stieltjes type integral for real functions on a fractal subset of the real line The Henstock-Kurzweil-Stieltjes type integral for real functions on a fractal subset of the real line D. Bongiorno, G. Corrao Dipartimento di Ingegneria lettrica, lettronica e delle Telecomunicazioni,

More information

The positive minimum degree game on sparse graphs

The positive minimum degree game on sparse graphs The positive minimum degree game on sparse graphs József Balogh Department of Mathematical Sciences University of Illinois, USA jobal@math.uiuc.edu András Pluhár Department of Computer Science University

More information

E3: PROBABILITY AND STATISTICS lecture notes

E3: PROBABILITY AND STATISTICS lecture notes E3: PROBABILITY AND STATISTICS lecture notes 2 Contents 1 PROBABILITY THEORY 7 1.1 Experiments and random events............................ 7 1.2 Certain event. Impossible event............................

More information

LINEAR ALGEBRA W W L CHEN

LINEAR ALGEBRA W W L CHEN LINEAR ALGEBRA W W L CHEN c W W L Chen, 1997, 2008 This chapter is available free to all individuals, on understanding that it is not to be used for financial gain, and may be downloaded and/or photocopied,

More information

LECTURE 15: AMERICAN OPTIONS

LECTURE 15: AMERICAN OPTIONS LECTURE 15: AMERICAN OPTIONS 1. Introduction All of the options that we have considered thus far have been of the European variety: exercise is permitted only at the termination of the contract. These

More information

11 Ideals. 11.1 Revisiting Z

11 Ideals. 11.1 Revisiting Z 11 Ideals The presentation here is somewhat different than the text. In particular, the sections do not match up. We have seen issues with the failure of unique factorization already, e.g., Z[ 5] = O Q(

More information

Cycles and clique-minors in expanders

Cycles and clique-minors in expanders Cycles and clique-minors in expanders Benny Sudakov UCLA and Princeton University Expanders Definition: The vertex boundary of a subset X of a graph G: X = { all vertices in G\X with at least one neighbor

More information

Universal Algorithm for Trading in Stock Market Based on the Method of Calibration

Universal Algorithm for Trading in Stock Market Based on the Method of Calibration Universal Algorithm for Trading in Stock Market Based on the Method of Calibration Vladimir V yugin Institute for Information Transmission Problems, Russian Academy of Sciences, Bol shoi Karetnyi per.

More information

The Convolution Operation

The Convolution Operation The Convolution Operation Convolution is a very natural mathematical operation which occurs in both discrete and continuous modes of various kinds. We often encounter it in the course of doing other operations

More information

ALGEBRAIC APPROACH TO COMPOSITE INTEGER FACTORIZATION

ALGEBRAIC APPROACH TO COMPOSITE INTEGER FACTORIZATION ALGEBRAIC APPROACH TO COMPOSITE INTEGER FACTORIZATION Aldrin W. Wanambisi 1* School of Pure and Applied Science, Mount Kenya University, P.O box 553-50100, Kakamega, Kenya. Shem Aywa 2 Department of Mathematics,

More information

A Uniform Asymptotic Estimate for Discounted Aggregate Claims with Subexponential Tails

A Uniform Asymptotic Estimate for Discounted Aggregate Claims with Subexponential Tails 12th International Congress on Insurance: Mathematics and Economics July 16-18, 2008 A Uniform Asymptotic Estimate for Discounted Aggregate Claims with Subexponential Tails XUEMIAO HAO (Based on a joint

More information