MAT25 LECTURE 10 NOTES. = a b. > 0, there exists N N such that if n N, then a n a < ɛ

Similar documents
The Real Numbers. Here we show one way to explicitly construct the real numbers R. First we need a definition.

TEST 1 SOLUTIONS MATH 1002

CHARACTERIZATION OF CLOSED CONVEX SUBSETS OF R n

X ln( +1 ) +1 [0 ] Γ( )

Online Shopping Intermediaries: The Strategic Design of Search Environments

25 Increasing and Decreasing Functions

Lecture l(x) 1. (1) x X

Lecture Quantitative Finance Spring Term 2015

then for any deterministic f,g and any other random variable

The Binomial Theorem and Consequences

( ) = R + ª. Similarly, for any set endowed with a preference relation º, we can think of the upper contour set as a correspondance  : defined as

FE 5204 Stochastic Differential Equations

Convergence. Any submartingale or supermartingale (Y, F) converges almost surely if it satisfies E Y n <. STAT2004 Martingale Convergence

Yao s Minimax Principle

Lecture 14: Basic Fixpoint Theorems (cont.)

Appendix for Growing Like China 1

Eliminating Substitution Bias. One eliminate substitution bias by continuously updating the market basket of goods purchased.

8. Propositional Logic Natural deduction - negation. Solved problems

BROWNIAN MOTION II. D.Majumdar

6.207/14.15: Networks Lecture 10: Introduction to Game Theory 2

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 11 10/9/2013. Martingales and stopping times II

Chapter 1 Additional Questions

18.440: Lecture 32 Strong law of large numbers and Jensen s inequality

On Packing Densities of Set Partitions

Outline of Lecture 1. Martin-Löf tests and martingales

II. The Arrow-Debreu Model of Competitive Equilibrium - Definition and Existence A. Existence of General Equilibrium in a simple model

4 Martingales in Discrete-Time

Structural Induction

October An Equilibrium of the First Price Sealed Bid Auction for an Arbitrary Distribution.

6. Martingales. = Zn. Think of Z n+1 as being a gambler s earnings after n+1 games. If the game if fair, then E [ Z n+1 Z n

Lecture 5: Iterative Combinatorial Auctions

CS364A: Algorithmic Game Theory Lecture #14: Robust Price-of-Anarchy Bounds in Smooth Games

Martingales. by D. Cox December 2, 2009

I. The Solow model. Dynamic Macroeconomic Analysis. Universidad Autónoma de Madrid. September 2015

Sublinear Time Algorithms Oct 19, Lecture 1

Building Infinite Processes from Regular Conditional Probability Distributions

1 Precautionary Savings: Prudence and Borrowing Constraints

DEPTH OF BOOLEAN ALGEBRAS SHIMON GARTI AND SAHARON SHELAH

Rational Infinitely-Lived Asset Prices Must be Non-Stationary

Bargaining and Competition Revisited Takashi Kunimoto and Roberto Serrano

Lattices and the Knaster-Tarski Theorem

Last Time. Martingale inequalities Martingale convergence theorem Uniformly integrable martingales. Today s lecture: Sections 4.4.1, 5.

On Existence of Equilibria. Bayesian Allocation-Mechanisms

ONLY AVAILABLE IN ELECTRONIC FORM

Math 167: Mathematical Game Theory Instructor: Alpár R. Mészáros

Microeconomic Theory II Preliminary Examination Solutions

Tug of War Game. William Gasarch and Nick Sovich and Paul Zimand. October 6, Abstract

Assets with possibly negative dividends

Information Acquisition under Persuasive Precedent versus Binding Precedent (Preliminary and Incomplete)

Efficiency in Decentralized Markets with Aggregate Uncertainty

Lecture 5: Tuesday, January 27, Peterson s Algorithm satisfies the No Starvation property (Theorem 1)

Lecture 14: Examples of Martingales and Azuma s Inequality. Concentration

6.4 Solving Linear Inequalities by Using Addition and Subtraction

Notes on Auctions. Theorem 1 In a second price sealed bid auction bidding your valuation is always a weakly dominant strategy.

DRAFT. 1 exercise in state (S, t), π(s, t) = 0 do not exercise in state (S, t) Review of the Risk Neutral Stock Dynamics

Algebra homework 8 Homomorphisms, isomorphisms

ECON Micro Foundations

THE NUMBER OF UNARY CLONES CONTAINING THE PERMUTATIONS ON AN INFINITE SET

Introduction to Greedy Algorithms: Huffman Codes

The Limiting Distribution for the Number of Symbol Comparisons Used by QuickSort is Nondegenerate (Extended Abstract)

On Toponogov s Theorem

Remarks on Probability

Lecture Notes 1

6.896 Topics in Algorithmic Game Theory February 10, Lecture 3

SEMICENTRAL IDEMPOTENTS IN A RING

Part 3: Trust-region methods for unconstrained optimization. Nick Gould (RAL)

LECTURE 2: MULTIPERIOD MODELS AND TREES

Comparison of proof techniques in game-theoretic probability and measure-theoretic probability

Macroeconomics and finance

Generalising the weak compactness of ω

Sy D. Friedman. August 28, 2001

Econometrica Supplementary Material

Interpolation. 1 What is interpolation? 2 Why are we interested in this?

Tutorial 6. Sampling Distribution. ENGG2450A Tutors. 27 February The Chinese University of Hong Kong 1/6

Notes on the symmetric group

The Theory of Interest

Radner Equilibrium: Definition and Equivalence with Arrow-Debreu Equilibrium

On the Lower Arbitrage Bound of American Contingent Claims

Valuation and Tax Policy

Dynamic Admission and Service Rate Control of a Queue

1 Ricardian Neutrality of Fiscal Policy

ECON 803: MICROECONOMIC THEORY II Arthur J. Robson Fall 2016 Assignment 9 (due in class on November 22)

First Welfare Theorem in Production Economies

CH 39 CREATING THE EQUATION OF A LINE

Essays on Some Combinatorial Optimization Problems with Interval Data

Chapter 10: Mixed strategies Nash equilibria, reaction curves and the equality of payoffs theorem

GAME THEORY. Department of Economics, MIT, Follow Muhamet s slides. We need the following result for future reference.

4: SINGLE-PERIOD MARKET MODELS

Introduction to Probability Theory and Stochastic Processes for Finance Lecture Notes

1 Overview. 2 The Gradient Descent Algorithm. AM 221: Advanced Optimization Spring 2016

CONGRUENCES AND IDEALS IN A DISTRIBUTIVE LATTICE WITH RESPECT TO A DERIVATION

Finite Memory and Imperfect Monitoring

Equivalence between Semimartingales and Itô Processes

I. The Solow model. Dynamic Macroeconomic Analysis. Universidad Autónoma de Madrid. Autumn 2014

Optimal online-list batch scheduling

MTH6154 Financial Mathematics I Interest Rates and Present Value Analysis

Derivative Instruments

MATH 104 Practice Problems for Exam 3

The Value of Information in Central-Place Foraging. Research Report

Harvard School of Engineering and Applied Sciences CS 152: Programming Languages

Transcription:

MAT5 LECTURE 0 NOTES NATHANIEL GALLUP. Algebraic Limit Theorem Theorem : Algebraic Limit Theorem (Abbott Theorem.3.3) Let (a n ) and ( ) be sequences of real numbers such that lim n a n = a and lim n = b. Then the following statements hold. (a) lim n (ca n ) = ca. (b) lim n (a n + ) = a + b. (c) lim n (a n ) = ab. (d) If 0 for all n and b 0, then lim n a n bn = a b. Proof. (a) We consider two cases. (c 0). (Scratch Work). We are trying to show that (ca n ) ca. Hence we are interested in the inequality (b) ca n ca < c(a n a) < a n a < a n a <. (Proof ). Let > 0 be arbitrary. Since (a n ) converges to a, given > 0, there exists N N such that if n N, then a n a <. Then we compute a n a < = a n a < = c(a n a) < = ca n ca < Therefore for any > 0, we have found N N such that if n N, then ca n ca <. Hence (ca n ) converges to (ca). (c = 0). If c = 0, then the sequence (ca n ) is simply the sequence (0, 0, 0,...) which consists only of 0 s. By??, this sequence converges to 0 = ca, as desired. (Scratch Work). We are trying to show that (a n + ) a+b. Hence we are interested in the quantity (a n + ) (a + b) = (a n a) + ( b) () a n a + b Here () follows from the triangle inequality (??). But since (a n ) a and ( ) b, we can make a n a and b as small as we want. (Proof ). Let > 0 be arbitrary. Since (a n ) a, there exists some N N such that if n N, we have that a n a <. Similarly, since () b, there exists some N N such that if n N, we have that b <. Let N = max{n, N }. Then we have that N N and N N, so if n N, then n N and n N, so we have both a n a < and b <. Compute

MAT5 LECTURE 0 NOTES (c) (a n + ) (a + b) = (a n a) + ( b) a n a + b < + Therefore for any > 0, we have found N N such that if n N, then (a n + ) (a + b) <. Hence (a n + ) converges to (a + b). (Scratch Work). We are trying to show that (a n ) ab. Hence we are interested in the quantity a n ab = a n a + a ab () a n a + a ab = a n a + a b. Here () follows from the triangle inequality (??). Since (a n ) a and ( ) b, we can make a n a and b small. The number a is fixed, and since ( ) is a convergent sequence,?? implies that ( ) is bounded. Hence we have some bound M R with M for all n N. (Proof ). Let > 0 be arbitrary. Since ( ) is a convergent sequence,?? implies that ( ) is bounded. Hence we have some bound M R, which we can choose to satisfy M > 0, with M for all n N. Now we consider two cases. (a = 0). Since (a n ) a, there exists some N N such that if n N, we have a n a < M. Then compute a n ab = a n a + a ab a n a + a ab = a n a + a b < M M + 0 (a 0). Since (a n ) a, there exists some N N such that if n N, we have a n a < M. Since ( ) b and a 0, there exists some N N such that if n N, we have b < a. Let N = max{n, N }. Then we have that N N and N N, so if n N, then n N and n N, so we have both a n a < M and b < a. Compute a n ab = a n a + a ab a n a + a ab = a n a + a b < M M + a a Therefore in either case for any > 0, we have found N N such that if n N, then (a n ) (ab) <. Hence (a n ) converges to (ab). ( ) (d) First we show that if 0 for all n N, then b ( ). (Scratch Work). We are trying to show that b. Hence we are interested in the quantity b = b b. We need to make the quantity b bn b small. Since ( ) b, and b is a fixed number, we can make b b small, but to make small, we need a lower bound on the terms. This is make possible by the fact that ( ) b and b is nonzero, hence the terms of are eventually bounded away from 0.

MAT5 LECTURE 0 NOTES 3 (Proof ). Let > 0 be arbitrary. Since ( ) b, there exists N N such that if n N, then b < b. Using the reverse triangle inequality (??), we obtain that b b < b = b < b = 0 < b < () = < b. Here () follows from?? part (e). Furthermore, because ( ) b, there exists N N such that if n N, then b < b. Let N = max{n, N }. Then if n N, we have that b = b b < b b b Therefore for any > 0, we have found N N such that if n N, then converges to b. Since (a n ) a and b ( ) ( ) b, it follows from part (c) that an bn a b, as desired. <. Hence ( ) Exercise : Abbott Exercise.3. Let x n 0 for all n N. (a) If (x n ) 0, show that ( x n ) 0. (b) If (x n ) x, show that ( x n ) x. Exercise : Abbott Exercise.3.4 Let (a n ) 0, and use the Algebraic Limit Theorem (??) to compute the following limits, and prove your result. (Assume that the sequence (a n ) is such that all fractions below are defined). (a) lim n +a n +3a n 4a n (b) lim n (a n+) 4 a n (c) lim n an +3 an +5 Exercise 3: Abbott Exercise.3.7 Give an example of each of the following, or prove that such a request is impossible. (a) Sequences (x n ) and (y n ), which both diverge, but whose sum (x n + y n ) converges. (b) Sequences (x n ) and (y n ), where (x n ) converges, (y n ) diverges, and (x n + y n ) converges. (c) A convergent sequence ( ) with 0 for all n N such that (/ ) diverges. (d) An unbounded sequence (a n ) and a convergent sequence ( ) with (a n ) bounded. (e) Two sequences (a n ) and ( ), where (a n ) and (a n ) converge but ( ) does not.

MAT5 LECTURE 0 NOTES 4 Exercise 4: Abbott Exercise.3.9 (a) Let (a n ) be a bounded (not necessarily convergent) sequence, and assume that lim n = 0. Show that lim n (a n ) = 0. Why are we not allowed to use the Algebraic Limit Theorem (??) to prove this? (b) Can we conclude anything about the convergence of (a n ) if we assume that ( ) converges to some nonzero limit b?. Order Limit Theorem Theorem : Order Limit Theorem (Abbott Theorem.3.4) Let (a n ) and ( ) be sequences of real numbers such that lim n a n = a and lim n = b. Then the following statements hold. (a) If a n 0 for all n N, then a 0. (b) If a n fo all n N, then a b. (c) If there exists c R for which c for all n, then c b. Similarly, if a n c for all n N, then a c. Proof. (a) Suppose, for contradiction, that a < 0. Then let = a, and note that > 0. Since (a n) a, it follows that there exits N N such that if n N, then a n a <. Since N N, we have that a N a < = a N a < a = a N a < a () = a N < a Here () follows by adding a to both sides of the inequality. However a < 0, hence dividing both sides by yields a < 0. Therefore we have that a N < a < 0, contradicting that a n 0 for all n N. (b) Since (a n ) a,?? part (a) implies that ( a n ) a, and therefore?? part (b) implies that ( a n ) b a. Since a n for all n N, it follows that 0 a n for all n N. Therefore applying part (a) of this theorem then yields that 0 b a, which implies that a b, as desired. (c) Let (x n ) be the sequence defined by x n = x for all n N. Then we have that x n for all n. By??, (x n ) c, hence applying part (b) of this proposition yields that c b. A similar argument shows that a c if a n c for all n N. Definition : Eventually 3. Eventuality Let (a n ) be a sequence for which some property of a n holds for all n N for some N N. Then we say that (a n ) eventually has this property.

MAT5 LECTURE 0 NOTES 5 Note : Eventuality Properties of the limit of a convergent sequence, in general, do not depend on the behavior of the beginning of the sequence. In this sense, only eventuality matters for the limit. Example : Eventuality (a) Let c R be a real number. A sequence (a n ) is eventually c if there exists some N N such that a n 0 for all n N. (b) Given two sequences (a n ) and ( ), we say that (a n ) is eventually less than or equal to ( ), if there exists some N N such that a n for all n N. Exercise 5: Eventually Show that the Order Limit Theorem (??) still holds if the conditions are relaxed to being only eventually true. Proof. Homework. 4. Monotone Convergence Theorem Definition : Increasing, Decreasing, and Monotone A sequence (a n ) is increasing if a n a n+ for all n N and decreasing if a n a n+ for all n N. A sequence is monotone if it is either increasing or decreasing. Theorem 3: Monotone Convergence Theorem (Abbott Theorem.4.) If a sequence is monotone and bounded, then it converges.