LEGENDRE SERIES
9. LEGENDRE SERIES
Since the Legendre polynomials form a complete orthogonal set on (−1, 1), we can expand functions in Legendre series just as we expanded functions in Fourier series.
Example 1. Expand in a Legendre series the function f (x) given by
(see Figure 9.1). We put
f (x) =
c (x).
Figure 9.1
l=0
Our problem is to find the coefficients c l . We do this by a method parallel to the one we used in finding the formulas for the coefficients in a Fourier series. We multiply both sides of (9.2) by P m (x) and integrate from −1 to 1. Because the Legendre polynomials are orthogonal, all the integrals on the right are zero except the one containing c m , and we can evaluate it by (8.1). Thus we get
f (x)P m (x) dx =
P l (x)P m (x) dx = c m · .
l=0
2m + 1
Section 9 Legendre Series 581
Using this result in our example (9.1), we find
f (x)P 0 (x) dx = c 0 [P 0 (x)] 2 dx
f (x)P
1 (x) dx = c 1 [P 1 (x)] dx
f (x)P 2 (x) dx = c 2 [P 2 (x)] 2 dx
Continuing in this way we find for the function given in (9.1) (9.4)
f (x) = 1 P 0 (x) + 3 2 11 4 P 1 (x) − 7 16 P 3 (x) + 32 P 5 (x) + · · · . It is unnecessary for f (x) to be continuous as it must be for expansion in a
Maclaurin series. Just as for Fourier series, the Dirichlet conditions (see Chapter 7, Section 6) are a convenient set of sufficient conditions for a function f (x) to be expandable in a Legendre series. If f (x) satisfies the Dirichlet conditions on (−1, 1), then at points inside (−1, 1) (not necessarily at the endpoints), the Legendre series converges to f (x) anywhere f (x) is continuous and converges to the midpoint of the jump at discontinuities.
Example 2. Here is an interesting fact about Legendre series. Sometimes we want to fit
a given curve as closely as possible by a polynomial of a given degree, say a cubic. The criterion of “Least Squares” is often used to determine the best fit. This means that if, say, we want to fit a given f (x) on (−1, 1) by a cubic, we find the coefficients
a, b, c, d so that
(9.5) [f (x) − (ax 3 + bx 2 + cx + d)] 2 dx
is as small as possible. Then (9.6)
f (x) ∼ 3 = ax + bx 2 + cx + d
is called the best approximation (by a cubic) in the least squares sense. It can
be proved that an expansion (as far as the desired degree of the polynomial ap- proximation) in Legendre polynomials gives this best least squares approximation (Problem 16).
PROBLEMS, SECTION 9
Expand the following functions in Legendre series. (
0<x<1 3. f (x) = P 3 ′ (x)
0<x<1
x,
4. f (x) = arc sin x 5.
582 Series Solutions of Differential Equations Chapter 12 ( 0
6. f (x) = on (−1, 0)
1 ´ 2 `ln Hint: See Chapter 11, Section 3, Problem 13.
on (0, 1) (
7. f (x) = on (−1, 0)
√ Hint: See Chapter 11, Sections 6 and 7. 1 − x on (0, 1)
8. Hint: Solve the recursion relation (5.8e) for P l (x) and show that
P l (x) dx =
1 [P
l−1 (a) − P l+1 (a)].
a 2l + 1
R (x). 1 Hint: For l ≥ n, −1 P n ′ (x)P l (x) dx = 0 (Why?); for l < n, integrate by parts.
9. f (x) = P n ′
Expand each of the following polynomials in a Legendre series. You should get the same results that you got by a different method in the corresponding problems in Section 5.
10. 3x 2 +x−1 11. 7x 4 − 3x + 1 3 12. x−x Find the best (in the least squares sense) second-degree polynomial approximation to each
of the given functions over the interval −1 < x < 1. (See Problem 16.)
13. x 4 14. |x|
15. cos πx
16. Prove the least squares approximation property of Legendre polynomials [see (9.5) and (9.6)] as follows. Let f (x) be the given function to be approximated. Let the functions p l (x) be the normalized Legendre polynomials, that is,
so that
[p l (x)] 2 dx = 1.
Show that the Legendre series for f (x) as far as the p 2 (x) term is Z 1
f (x) = c 0 p 0 (x) + c 1 p 1 (x) + c 2 p 2 (x)
with c l =
f (x)p l (x) dx.
Write the quadratic polynomial satisfying the least squares condition as b 0 p 0 (x) + b 1 p 1 (x) + b 2 p 2 (x) (by Problem 5.14 any quadratic polynomial can be written in this
form). The problem is to find b 0 ,b 1 ,b 2 so that
I=
[f (x) − (b 0 p
2 0 (x) + b 1 p 1 (x) + b 2 p 2 (x))] dx
is a minimum. Square the bracket and write I as a sum of integrals of the individual terms. Show that some of the integrals are zero by orthogonality, some are 1 because the p l ’s are normalized, and others are equal to the coefficients c l . Add and subtract
c 2 0 +c 2 1 +c 2 2 and show that Z 1
I= [f 2 (x) + (b 0 −c 0 ) 2 + (b 1 1 ) 2 + (b 2 2 ) 2 2 2 −c 2 −c −c 0 −c 1 −c 2 ] dx.
Now determine the values of the b’s to make I as small as possible. (Hint: The smallest value the square of a real number can have is zero.) Generalize the proof to polynomials of degree n.
Section 10 The Associated Legendre Functions 583
Parts
» CONVERGENCE TESTS FOR SERIES OF POSITIVE TERMS; ABSOLUTE CONVERGENCE
» CONDITIONALLY CONVERGENT SERIES
» POWER SERIES; INTERVAL OF CONVERGENCE
» EXPANDING FUNCTIONS IN POWER SERIES
» TECHNIQUES FOR OBTAINING POWER SERIES EXPANSIONS
» ACCURACY OF SERIES APPROXIMATIONS
» COMPLEX POWER SERIES; DISK OF CONVERGENCE
» ELEMENTARY FUNCTIONS OF COMPLEX NUMBERS
» POWERS AND ROOTS OF COMPLEX NUMBERS
» THE EXPONENTIAL AND TRIGONOMETRIC FUNCTIONS
» LINEAR COMBINATIONS, LINEAR FUNCTIONS, LINEAR OPERATORS
» LINEAR DEPENDENCE AND INDEPENDENCE
» SPECIAL MATRICES AND FORMULAS
» EIGENVALUES AND EIGENVECTORS; DIAGONALIZING MATRICES
» APPLICATIONS OF DIAGONALIZATION
» A BRIEF INTRODUCTION TO GROUPS
» POWER SERIES IN TWO VARIABLES
» APPROXIMATIONS USING DIFFERENTIALS
» CHAIN RULE OR DIFFERENTIATING A FUNCTION OF A FUNCTION
» APPLICATION OF PARTIAL DIFFERENTIATION TO MAXIMUM AND MINIMUM PROBLEMS
» MAXIMUM AND MINIMUM PROBLEMS WITH CONSTRAINTS; LAGRANGE MULTIPLIERS
» ENDPOINT OR BOUNDARY POINT PROBLEMS
» DIFFERENTIATION OF INTEGRALS; LEIBNIZ’ RULE
» APPLICATIONS OF INTEGRATION; SINGLE AND MULTIPLE INTEGRALS
» CHANGE OF VARIABLES IN INTEGRALS; JACOBIANS
» APPLICATIONS OF VECTOR MULTIPLICATION
» DIRECTIONAL DERIVATIVE; GRADIENT
» SOME OTHER EXPRESSIONS INVOLVING ∇
» GREEN’S THEOREM IN THE PLANE
» THE DIVERGENCE AND THE DIVERGENCE THEOREM
» THE CURL AND STOKES’ THEOREM
» SIMPLE HARMONIC MOTION AND WAVE MOTION; PERIODIC FUNCTIONS
» APPLICATIONS OF FOURIER SERIES
» COMPLEX FORM OF FOURIER SERIES
» LINEAR FIRST-ORDER EQUATIONS
» OTHER METHODS FOR FIRST-ORDER EQUATIONS
» SECOND-ORDER LINEAR EQUATIONS WITH CONSTANT COEFFICIENTS AND ZERO RIGHT-HAND SIDE
» SECOND-ORDER LINEAR EQUATIONS WITH CONSTANT COEFFICIENTS AND RIGHT-HAND SIDE NOT ZERO
» OTHER SECOND-ORDER EQUATIONS
» SOLUTION OF DIFFERENTIAL EQUATIONS BY LAPLACE TRANSFORMS
» A BRIEF INTRODUCTION TO GREEN FUNCTIONS
» THE BRACHISTOCHRONE PROBLEM; CYCLOIDS
» SEVERAL DEPENDENT VARIABLES; LAGRANGE’S EQUATIONS
» TENSOR NOTATION AND OPERATIONS
» KRONECKER DELTA AND LEVI-CIVITA SYMBOL
» PSEUDOVECTORS AND PSEUDOTENSORS
» VECTOR OPERATORS IN ORTHOGONAL CURVILINEAR COORDINATES
» ELLIPTIC INTEGRALS AND FUNCTIONS
» GENERATING FUNCTION FOR LEGENDRE POLYNOMIALS
» COMPLETE SETS OF ORTHOGONAL FUNCTIONS
» NORMALIZATION OF THE LEGENDRE POLYNOMIALS
» THE ASSOCIATED LEGENDRE FUNCTIONS
» GENERALIZED POWER SERIES OR THE METHOD OF FROBENIUS
» THE SECOND SOLUTION OF BESSEL’S EQUATION
» OTHER KINDS OF BESSEL FUNCTIONS
» ORTHOGONALITY OF BESSEL FUNCTIONS
» SERIES SOLUTIONS; FUCHS’S THEOREM
» HERMITE FUNCTIONS; LAGUERRE FUNCTIONS; LADDER OPERATORS
» LAPLACE’S EQUATION; STEADY-STATE TEMPERATURE IN A RECTANGULAR PLATE
» THE DIFFUSION OR HEAT FLOW EQUATION; THE SCHR ¨ODINGER EQUATION
» THE WAVE EQUATION; THE VIBRATING STRING
» STEADY-STATE TEMPERATURE IN A CYLINDER
» VIBRATION OF A CIRCULAR MEMBRANE
» STEADY-STATE TEMPERATURE IN A SPHERE
» INTEGRAL TRANSFORM SOLUTIONS OF PARTIAL DIFFERENTIAL EQUATIONS
» EVALUATION OF DEFINITE INTEGRALS BY USE OF THE RESIDUE THEOREM
» THE POINT AT INFINITY; RESIDUES AT INFINITY
» SOME APPLICATIONS OF CONFORMAL MAPPING
» THE NORMAL OR GAUSSIAN DISTRIBUTION
» STATISTICS AND EXPERIMENTAL MEASUREMENTS
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