Lecture 10. δ-sequences and approximation theorems

Similar documents
The function graphed below is continuous everywhere. The function graphed below is NOT continuous everywhere, it is discontinuous at x 2 and

Constructing Approximations to Functions

MATH 409 Advanced Calculus I Lecture 11: More on continuous functions.

Taylor and Maclaurin Series. Approximating functions using Polynomials.

5. Some theorems on continuous functions

1 Functions of many variables.

Math 1B, lecture 15: Taylor Series

Abstract. 2. We construct several transcendental numbers.

Continuous Functions on Metric Spaces

Definitions & Theorems

Mock Final Exam Name. Solve and check the linear equation. 1) (-8x + 8) + 1 = -7(x + 3) A) {- 30} B) {- 6} C) {30} D) {- 28}

Taylor and Maclaurin Series. Approximating functions using Polynomials.

Definition A.1. We say a set of functions A C(X) separates points if for every x, y X, there is a function f A so f(x) f(y).

1.2. Direction Fields: Graphical Representation of the ODE and its Solution Let us consider a first order differential equation of the form dy

Input: A set (x i -yy i ) data. Output: Function value at arbitrary point x. What for x = 1.2?

Limits at Infinity. Horizontal Asymptotes. Definition (Limits at Infinity) Horizontal Asymptotes

MATH 409 Advanced Calculus I Lecture 10: Continuity. Properties of continuous functions.

,... We would like to compare this with the sequence y n = 1 n

1 Definition of the Riemann integral

1 Functions and Graphs

X. Numerical Methods

1. Let A R be a nonempty set that is bounded from above, and let a be the least upper bound of A. Show that there exists a sequence {a n } n N

Chapter 3a Topics in differentiation. Problems in differentiation. Problems in differentiation. LC Abueg: mathematical economics

Green s Functions and Distributions

Analysis Finite and Infinite Sets The Real Numbers The Cantor Set

Numerical Analysis: Solving Nonlinear Equations

Most Continuous Functions are Nowhere Differentiable

defines the. The approximation f(x) L(x) is the. The point x = a is the of the approximation.

Zentrum für Technomathematik Fachbereich 3 Mathematik und Informatik. R, dx and ε. Derivatives and Infinitesimal Numbers

Lecture 5: Functions : Images, Compositions, Inverses

Variational Methods & Optimal Control

g(x) = P (y) Proof. This is true for n = 0. Assume by the inductive hypothesis that g (n) (0) = 0 for some n. Compute g (n) (h) g (n) (0)

DRAFT - Math 101 Lecture Note - Dr. Said Algarni

REAL ANALYSIS I HOMEWORK 4

1 + lim. n n+1. f(x) = x + 1, x 1. and we check that f is increasing, instead. Using the quotient rule, we easily find that. 1 (x + 1) 1 x (x + 1) 2 =

x x 1 x 2 + x 2 1 > 0. HW5. Text defines:

Analysis II - few selective results

AP Calculus AB Winter Break Packet Happy Holidays!

MATH 409 Advanced Calculus I Lecture 12: Uniform continuity. Exponential functions.

Section 3.1. Best Affine Approximations. Difference Equations to Differential Equations

Lecture 7: Sections 2.3 and 2.4 Rational and Exponential Functions. Recall that a power function has the form f(x) = x r where r is a real number.

Infinite Series. Copyright Cengage Learning. All rights reserved.

Math 122 Fall Unit Test 1 Review Problems Set A

There are four irrational roots with approximate values of

Section x7 +

LECTURE 7, WEDNESDAY

Calculus. Weijiu Liu. Department of Mathematics University of Central Arkansas 201 Donaghey Avenue, Conway, AR 72035, USA

e (x y)2 /4kt φ(y) dy, for t > 0. (4)

Indefinite Integration

Chapter 4. Continuous Random Variables

Sec. 14.3: Partial Derivatives. All of the following are ways of representing the derivative. y dx

LEBESGUE INTEGRATION. Introduction

14 EE 2402 Engineering Mathematics III Solutions to Tutorial 3 1. For n =0; 1; 2; 3; 4; 5 verify that P n (x) is a solution of Legendre's equation wit

Mathematics Notes for Class 12 chapter 7. Integrals

TEXT AND OTHER MATERIALS:

Math 3215 Intro. Probability & Statistics Summer 14. Homework 5: Due 7/3/14

VAN DER WAALS FORCES

1 Taylor-Maclaurin Series

Summer Review for Students Taking Calculus in No calculators allowed. To earn credit: Be sure to show all work in the area provided.

Definition of differential equations and their classification. Methods of solution of first-order differential equations

Relations and Functions (for Math 026 review)

Student Study Session. Theorems

(a) For an accumulation point a of S, the number l is the limit of f(x) as x approaches a, or lim x a f(x) = l, iff

g 2 (x) (1/3)M 1 = (1/3)(2/3)M.

Announcements. Topics: Homework: - sections 4.5 and * Read these sections and study solved examples in your textbook!

Real Analysis Math 131AH Rudin, Chapter #1. Dominique Abdi

Polynomial Functions

Interpolation Theory

Chapter 2 Polynomial and Rational Functions

7.4: Integration of rational functions

1 Question related to polynomials

Introduction to Decision Sciences Lecture 6

Q 0 x if x 0 x x 1. S 1 x if x 1 x x 2. i 0,1,...,n 1, and L x L n 1 x if x n 1 x x n

Chapter 11 - Sequences and Series

Continuity. To handle complicated functions, particularly those for which we have a reasonable formula or formulas, we need a more precise definition.

x n cos 2x dx. dx = nx n 1 and v = 1 2 sin(2x). Andreas Fring (City University London) AS1051 Lecture Autumn / 36

Learning Objectives These show clearly the purpose and extent of coverage for each topic.

Sections 4.1 & 4.2: Using the Derivative to Analyze Functions

Mathematics for Business and Economics - I. Chapter 5. Functions (Lecture 9)

Continuity. Chapter 4

Final Exam A Name. 20 i C) Solve the equation by factoring. 4) x2 = x + 30 A) {-5, 6} B) {5, 6} C) {1, 30} D) {-5, -6} -9 ± i 3 14

Econ Slides from Lecture 2

10. Smooth Varieties. 82 Andreas Gathmann

Preliminary Statistics. Lecture 3: Probability Models and Distributions

FINAL REVIEW Answers and hints Math 311 Fall 2017

An idea how to solve some of the problems. diverges the same must hold for the original series. T 1 p T 1 p + 1 p 1 = 1. dt = lim

Polynomials. Exponents. End Behavior. Writing. Solving Factoring. Graphing. End Behavior. Polynomial Notes. Synthetic Division.

Prelim 2 Math Please show your reasoning and all your work. This is a 90 minute exam. Calculators are not needed or permitted. Good luck!

Section 5.8. Taylor Series

Week 5: Functions and graphs

Assignment. Disguises with Trig Identities. Review Product Rule. Integration by Parts. Manipulating the Product Rule. Integration by Parts 12/13/2010

Part 2 Continuous functions and their properties

COMPLEX MULTIPLICATION: LECTURE 13

MAT137 Calculus! Lecture 6

a x a y = a x+y a x a = y ax y (a x ) r = a rx and log a (xy) = log a (x) + log a (y) log a ( x y ) = log a(x) log a (y) log a (x r ) = r log a (x).

The Intermediate Value Theorem If a function f (x) is continuous in the closed interval [ a,b] then [ ]

Regularity for Poisson Equation

Calculus of Variations Summer Term 2015

Algebra II Vocabulary Alphabetical Listing. Absolute Maximum: The highest point over the entire domain of a function or relation.

Taylor series. Chapter Introduction From geometric series to Taylor polynomials

Transcription:

Lecture 10. δ-sequences and approximation theorems 1 Dirac δ-function In 1930 s a great physicist Paul Dirac introduced the δ-function which has the following properties: δ(x) = 0 for x 0 = for x = 0 δ(x) = 1. Surely, there is no such a function. Nevertheless, the following theorem may be proved Theorem 1 For any continuous function f C(), f(x)δ(x)dx = f(0). Proof By the integral mean value theorem, ε f(x)δ(x)dx = f(c) ε δ(x)dx = f(c). For ε ε ε 0, c 0. On one hand, the integral stays unchanged, on the other hand it tends to f(0). δ-sequences The idea of δ-function may be formalized by the notion of δ-sequences. Definition 1 A sequence of continuous or piecewise continuous functions ( n ) on the line is called a δ-sequence provided that the following holds: a) δ n 0 b) δ n 1 c) ε > 0 \[ ε,ε] δ n 0. By default, the integration domain is. Theorem For any finite continuous function f, and any δ-sequence, f(x) n (x)dx := (f, n ) f(0) as n. 1

Proof (f, n ) = I n + J n, I n = f(x) \[ ε,ε] n(x)dx, J n = f(x) [ ε,ε] n(x)dx I n supp f max f n (x)dx 0, n. \[ ε,ε] Here supp f is the length of the support of f. By the mean value theorem which is applicable because n 0, we have: J n = f(c) ε ε n (x)dx. The value f(c) is close to f(0) for small ε, and the value of ε ε n(x)dx is close to 1 for a large n. Therefore, I n is small, and J n is close to f(0). 3 Uniform convergence Corollary 1 Let f C 0 (), n be a δ-sequence. Then f n (x) = f(x) n (x y)dx f(y). Theorem 3 In assumptions of Corollary 1, the convergence f n fis uniform. Proof We want tot prove that α > 0 N: f n (y) f(y) < α n > N. (1) Take ε such that osc [y ε,y+ε] f < α y. Such an ε exists because the function f is finite. Take N such that \[ ε,ε] n(x)dx < α n > N. Such an N exists because of Assumption c). Hence (1) holds. 4 Important δ-sequences Let ϕ C,0 () be a unimodular function, ϕ(0) = 1. Unimodality means that sign ϕ = sign x. The graph of ϕ resembles a bell; the function ϕ has a maximum value at zero. Theorem 4 Let n = ϕ n ϕn (x)dx. () Then n is a δ sequence.

Proof The unimodality of the function ϕ implies that it is non-negative: ϕ 0. Moreover, ϕ(x) < 1 x 0. Assumption b of Definition 1: n(x)dx 1 holds by definition of n. Let us check the only remaining Assumption c. For any ε max ϕ(x) = q(ε), 0 < q(ε) < 1. x ε Let I n = ϕ n (x)dx. On the set x ε, we have: n (x) qn (ε) I n := λ n (ε). (3) Then x ε n (x)dx λ n (ε) supp ϕ. Proposition 1 In assumptions of the theorem, λ n (ε) 0 for any ε > 0. Theorem 4 is an immediate consequence of Proposition 1. Proof [of Proposition 1.] Fix ε > 0. The numerator of the fraction above tends to zero exponentially. Let us prove that the denominator is greater than some negative power of n: there exists c such that ϕ n (x)dx c. (4) n for any n sufficiently large. The n the ratio (3) tends to zero because the exponential decreases faster than any power. The unimodality of the function ϕ implies that for small x : x x 0, there exists CC such that ϕ(x) 1 Cx. For the large n, we have 1 n < x 0. On the interval x 1 n we get: ϕ(x) 1 C n. Hence, for x 1 n, We have: ϕ n (x) ( 1 C n ) n = i n. i n e C при n 3

Hence, for sufficiently large n, where This proves the proposition. I n i n n > c = 3 C. c n 5 Examples. The following two δ-sequences are used in the proof of Weierstrass approximation theorems. Take ϕ(x) = (1 x )χ [ 1,1]. (5) The sequence () with this ϕ is a δ-sequence. This sequence is remarkable because on the interval x y < 1, the function n (x y) is a polynomial in y. Take ϕ(x) = (cos x 1)χ [ π,π]. (6) The sequence () with this ϕ is also a δ-sequence. Any function in this sequence is a trigonometric polynomial in y for x y < π. 6 Polynomial approximations. Theorem 5 (Weierstrass) Any continuous function on a segment may be uniformely approximated by polynomials. Proof Let us bring the given segment to [0, 1] by an affine transformation. Consider the δ-sequence n (x) = P n (x)χ [ 1,1] constructed above; P n (x) = c n (1 x ) n. This sequence is given by formulas (), (5). Then n (x y) = P n (x y)χ [ 1+y,1+y] For fixed x, P n (x y) = Σ n 0 a k (x)y k. Moreover, n (x y) = Σa k (x)y k, provided that x y 1. Let us take a finite continuous function f C 0, supp f [0, 1]. For x [0, 1], y [0, 1] we have: x y 1. Hence, for these x and y, n (y x) = Σ n 0 a k (x)y k. Moreover, for any y, 1 1 f n (y) = f(x) n (x y)dx = f(x)σ n 0 a k (x)y k dx = Σ n 0 b k Y k, b k = f(x)a k (x)dx. 0 By Theorem 3, f n (y) f on. But on the segment [0, 1] the functions f n are polynomials. 0 4

Theorem 6 The same result, but polynomials trigonometric polynomials. (1+cos x)n Proof The proof is the same, but we take n (x) = I n, where I n = π (1 + cos π x)n dx. In the previous proof the segment [ 1, 1] should be replaced by [ π, π], and [0, 1] by [0, π]. 5