MA4229 Lectures 13, 14 Week 12 Nov 1, 4 2010 Chapter 13 Approximation to periodic functions.

Slides:



Advertisements
Similar presentations
12.7 (Chapter 9) Special Sequences & Series
Advertisements

Vector Spaces A set V is called a vector space over a set K denoted V(K) if is an Abelian group, is a field, and For every element vV and K there exists.
10.4 Complex Vector Spaces.
5.1 Real Vector Spaces.
Let V be a variety. If fm 2 I(V), then f 2 I(V).
Ch 5.8: Bessel’s Equation Bessel Equation of order :
Chapter 5 Orthogonality
Ch 5.2: Series Solutions Near an Ordinary Point, Part I
Coordinate Systems (11/4/05) It turns out that every vector space V which has a finite basis can be “realized” as one of the spaces R n as soon as we pick.
Noncommutative Partial Fractions and Continued Fractions Darlayne Addabbo Professor Robert Wilson Department of Mathematics Rutgers University July 16,
ON MULTIVARIATE POLYNOMIAL INTERPOLATION
Chapter Polynomials of Higher Degree. SAT Problem of the day.
Warm up Use the Rational Root Theorem to determine the Roots of : x³ – 5x² + 8x – 6 = 0.
1 Preliminaries Precalculus Review I Precalculus Review II
Chapter 5: The Orthogonality and Least Squares
CHAPTER FIVE Orthogonality Why orthogonal? Least square problem Accuracy of Numerical computation.
1 Review of Fourier series Let S P be the space of all continuous periodic functions of a period P. We’ll also define an inner (scalar) product by where.
Linear Algebra Chapter 4 Vector Spaces.
Gram-Schmidt Orthogonalization
MA4248 Weeks 1-3. Topics Coordinate Systems, Kinematics, Newton’s Laws, Inertial Mass, Force, Momentum, Energy, Harmonic Oscillations (Springs and Pendulums)
Chapter 8 With Question/Answer Animations 1. Chapter Summary Applications of Recurrence Relations Solving Linear Recurrence Relations Homogeneous Recurrence.
Fall 2015 COMP 2300 Discrete Structures for Computation Donghyun (David) Kim Department of Mathematics and Physics North Carolina Central University 1.
Copyright © Cengage Learning. All rights reserved. CHAPTER 11 ANALYSIS OF ALGORITHM EFFICIENCY ANALYSIS OF ALGORITHM EFFICIENCY.
Chapter 5 Eigenvalues and Eigenvectors 大葉大學 資訊工程系 黃鈴玲 Linear Algebra.
Elementary Linear Algebra Anton & Rorres, 9th Edition
MA5241 Lecture 1 TO BE COMPLETED
Chapter 10 Real Inner Products and Least-Square
Section 5.1 Length and Dot Product in ℝ n. Let v = ‹v 1­­, v 2, v 3,..., v n › and w = ‹w 1­­, w 2, w 3,..., w n › be vectors in ℝ n. The dot product.
In this section we develop general methods for finding power series representations. Suppose that f (x) is represented by a power series centered at.
Elementary Linear Algebra Anton & Rorres, 9 th Edition Lecture Set – 07 Chapter 7: Eigenvalues, Eigenvectors.
3 DERIVATIVES.  Remember, they are valid only when x is measured in radians.  For more details see Chapter 3, Section 4 or the PowerPoint file Chapter3_Sec4.ppt.
MA4229 Lectures 9, 10 Weeks 5-7 Sept 7 - Oct 1, 2010 Chapter 7 The theory of minimax approximation Chapter 8 The exchange algorithm.
Probability Spaces A probability space is a triple (closed under Sample Space (any nonempty set), Set of Events a sigma-algebra over complementation and.
MA5238 Fourier Analysis Wayne Lawton Department of Mathematics S ,
CHAPTER 7 SECTION 1 ROOTS AND RADICAL EXPRESSIONS Algebra 2 Notes ~ April 10, 2009.
Maclaurin and Taylor Polynomials Objective: Improve on the local linear approximation for higher order polynomials.
6 6.3 © 2016 Pearson Education, Inc. Orthogonality and Least Squares ORTHOGONAL PROJECTIONS.
INFINITE SEQUENCES AND SERIES The convergence tests that we have looked at so far apply only to series with positive terms.
Every polynomial P(x) of degree n>0 has at least one zero in the complex number system. N Zeros Theorem Every polynomial P(x) of degree n>0 can be expressed.
MA4229 Lectures 15, 16 Week 13 Nov 1, Chapter 18 Interpolation by piecewise polynomials.
Matrices CHAPTER 8.9 ~ Ch _2 Contents  8.9 Power of Matrices 8.9 Power of Matrices  8.10 Orthogonal Matrices 8.10 Orthogonal Matrices 
An inner product on a vector space V is a function that, to each pair of vectors u and v in V, associates a real number and satisfies the following.
Eigenvalues, Zeros and Poles
Convergence of Taylor Series Objective: To find where a Taylor Series converges to the original function; approximate trig, exponential and logarithmic.
Answers for Review Questions for Lectures 1-4. Review Lectures 1-4 Problems Question 2. Derive a closed form for the estimate of the solution of the equation.
The Cauchy–Riemann (CR) Equations. Introduction The Cauchy–Riemann (CR) equations is one of the most fundamental in complex function analysis. This provides.
Tutorial 6. Eigenvalues & Eigenvectors Reminder: Eigenvectors A vector x invariant up to a scaling by λ to a multiplication by matrix A is called.
Calculus, Section 1.4.
MA2213 Lecture 8 Eigenvectors.
College Algebra Chapter 4 Exponential and Logarithmic Functions
Week 5 The Fourier series and transformation
Elementary Linear Algebra Anton & Rorres, 9th Edition
Chapter 3 The Real Numbers.
To any sequence we can assign a sequence with terms defined as
Chapter 3 The Real Numbers.
Quantum Two.
Section 4.1: Vector Spaces and Subspaces
Section 4.1: Vector Spaces and Subspaces
Quantum Two.
Polyhedron Here, we derive a representation of polyhedron and see the properties of the generators. We also see how to identify the generators. The results.
Ch 5.2: Series Solutions Near an Ordinary Point, Part I
Polyhedron Here, we derive a representation of polyhedron and see the properties of the generators. We also see how to identify the generators. The results.
College Algebra Chapter 4 Exponential and Logarithmic Functions
Linear Algebra Lecture 32.
Elementary Linear Algebra Anton & Rorres, 9th Edition
Chapter 4 Sequences.
Differential Equations
MA5242 Wavelets Lecture 1 Numbers and Vector Spaces
Eigenvalues and Eigenvectors
Finding Limits Using Tables and Graphs
Presentation transcript:

MA4229 Lectures 13, 14 Week 12 Nov 1, Chapter 13 Approximation to periodic functions

Periodic Functions We will let denote the real vector space represent continuous periodic functions on of continuous real valued functions Functions in that satisfy that have for period This means that they satisfy the condition Examples has as its set of periods the subgroup therefore so

Trigonometric Polynomials denote the real vector space of functions Question Why is Clearly where the coefficients Consider the function for every However for the function satisfies therefore can be approximated with arbitrary accuracy by trigonometric polynomials.

Trigonometric Polynomials This space satisfies some properties similar to those Property 2. Property 1. They are both real vector spaces. Property 3. They are both closed under translation Question Show that Question Describe satisfied by the space of algebraic polynomials. using Laurent polynomials. Question How many distinct roots can have? Define the space of (all) trig. polynomials

Approximation Theorem Theorem 13.1 For every there exist and such that Proof (pages ) uses the fact that continuous functions can be approximated to arbitrary accuracy by algebraic polynomials are dense. First express with even and odd Next define by Thm 6.1  there exists an algebraic poly.such that

Approximation Theorem Now the inequality so by defining the trigonometric polynomialby we obtain the approximation Since bothand are even functions we have

Approximation Theorem Now we need to construct that satisfies because then Approximating the odd functionis trickier than satisfies approximating the even function so we apply some magic. First observe that Choose the largestso that and choose the smallestso that

Approximation Theorem Now define the EVEN function to by and extending it on Now apply to to make it an even function. the method that we applied to obtain an even trigonometric polynomial such that and define the odd by Question Why is this odd?

Approximation Theorem We must examine three cases: Question Derive this bound for the case Therefore This concludes the proof of Theorem 13.1

The Fourier Series Operator Question Show that the set of functions is an orthonormal basis for the real Hilbert space with scalar product Define Fourier series operators

Tutorial 7 Due Thursday 4 November 1. Use the Bernstein approximation with methods 2. Compute developed in these notes to approximate defined by by defined above. 3. Derive (without using the book) the formula for the function 4. Do exercise 13.1 on page Do exercise 13.2 on page 161.