Computation of the Eigenvalues of 3D “Charged” Integral Equations

(1)

http://www.scirp.org/journal/jamp ISSN Online: 2327-4379

ISSN Print: 2327-4352

DOI: 10.4236/jamp.2017.510171 Oct. 31, 2017 2051 Journal of Applied Mathematics and Physics

Computation of the Eigenvalues of 3D

“Charged” Integral Equations

Diego Caratelli

1,2

, Pierpaolo Natalini

3

, Paolo E. Ricci

4

1_{The Antenna Company, High Tech Campus, Eindhoven, the Netherlands} 2_{Tomsk Polytechnic University, Tomsk, Russia}

3_{Università degli Studi Roma Tre, Largo San Leonardo Murialdo, Roma, Italia}

4_{International Telematic University UniNettuno, Corso Vittorio Emanuele II, Roma, Italia}

Abstract

The Rayleigh-Ritz and the inverse iteration methods are used in order to compute the eigenvalues of 3D Fredholm-Stieltjes integral equations, i.e. 3D

Fredholm equations with respect to suitable Stieltjes-type measures. Some ap-plications are shown, relevant to the problem of computing the eigenvalues of a body charged by a finite number of masses concentrated on points, curves or surfaces lying in.

Keywords

3D Fredholm-Stieltjes Integral Equations, Eigenvalues, Rayleigh-Ritz Method, Inverse Iteration Method

1. Introduction

The theory of Fredholm integral equations is strictly connected with the birth of functional analysis. A background of this theory can be found in classical books (see e.g. [1] [2]). For recent developments relevant to numerical computation of solutions, see [3].

In [4] [5], the Author shows that the Fredholm theory still holds considering the so called charged Fredholm integral equations, i.e. Fredholm equations with respect to a Stieltjes measure obtained by adding to the ordinary Lebesgue measure a finite combination of positive masses concentrated in arbitrary points of the considered interval. A topic which at present is included, as a particular case, in the theory of strictly positive compact operators.

In the one-dimensional case, the mechanical interpretation of these equations

How to cite this paper: Caratelli, D., Na-talini, P. and Ricci, P.E. (2017) Computa-tion of the Eigenvalues of 3D “Charged” Integral Equations. Journal of Applied Mathematics and Physics, 5, 2051-2071. https://doi.org/10.4236/jamp.2017.510171 Received: October 7, 2017

Accepted: October 28, 2017 Published: October 31, 2017 Copyright © 2017 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).

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DOI: 10.4236/jamp.2017.510171 2052 Journal of Applied Mathematics and Physics

is connected with the problem of the free vibrations of a string charged by a finite number of cursors, and is related to an extension of the classical orthogonality property of eigensolutions, called the “Sobolev-type” orthogonality (see e.g. [6] [7]).

In preceding articles [8] [9] [10], the problem of numerical computation of the above mentioned eigenvalue problems was solved, by using the so called inverse iteration method, showing applications in one and two-dimensional cases.

In this article, after briefly recalling the main results about the theory of eigenvalues for charged Fredholm integral equations, we mention how to obtain (in the particular case of a symmetric and strictly positive operator), the lower and upper approximations of these eigenvalues by means of the Rayleigh-Ritz method [1], [11] and the Fichera orthogonal invariants method [11] [12]

respectively. Then we conclude by showing that, even in the three-dimensional case, the lower approximations of the eigenvalues obtained by means of the Rayleigh-Ritz method can be improved by applying the inverse iteration method

[13]. Numerical computations relevant to the considered case are developed in the concluding section.

2. Lebesgue-Stieltjes Measures in a 3D Interval

Consider the interval  and the Hilbert space 2 2

( )

dM dM

L

≡

L



equipped with

the scalar product

[

,

]

:

(

,

)

_{( )}

(

,

)

(

,

)

(

,

)

r n m

P dP dM

U V U V _ρ U V U V _γ U V

Σ

∆ ∆ ∆

= + + + ₍₁₎

where

[

,

]

(

,

)

2

dM

L dM

U V ≡ U V and the subscripts “ρ

( )

P dP”, “∆_r”, “∆γ_n”, “

m

Σ

∆ ”

refer to the following definitions:

(

U V,

)

_ρ_{( )}_{P dP}:=

∫

U P V P

( ) ( ) ( )

ρ P dP

 (2)

(

)

( ) ( )

1

, :

r

h h h h

U V _∆ m U A V A

=

∑

(3)

(

)

(

( )

)

(

( )

)

(

( )

)

1

, : d

n k

n

k k

U V _∆_γ _γσ P s U P s V P s s

=

∑∫

(4)

and

(

)

(

( )

)

(

( )

)

(

( )

)

1

, : , , , d

m

U V τ P u v U P u v V P u v σ

Σ

∆ =

∑∫

₌ Σ_ 

 , (5)

(obviously 2

d :

σ

= EG−F d du v), so that the Stieltjes measure is the sum of the

ordinary Lebesgue measure plus a finite sum of charges mh concentrated on

points Ah⊂ ,

(

h=1, 2,,r

)

, plus a finite sum of continuous charges

belonging to the curves Σ ⊂_ , with densities σk

( )

⋅ ,

(

k=1, 2,,n

)

, plus a

finite sum of continuous charges belonging to the surfaces Σ ⊂_  , with densities τk

( )

⋅ ,

(

=1, 2,,m

)

.

It is worth noting that 2

( )

dM

(3)

space 2

( )

L



, a (complete) Hilbert space, which does not have singularities (i.e.

discontinuities) at points Ah,

(

h=1, 2,,r

)

, at curves γk,

(

k=1, 2,,n

)

,

and surfaces Σ__,

(

=1, 2,,m

)

, according to the usual condition for existence

of the relevant Stieltjes integrals (see e.g. [14]).

Let us consider, for example, the computation of integrals with respect to the above Dirac-type measures:

(

,

) ( )

h

( )

h h

(

, h

) ( )

h

K P Q ϕ Q mδ A =m K P A ϕ A

∫



(

) ( )

(

( )

)

(

( )

)

( )

(

)

(

( )

)

(

( )

)

, d , d k k k

K P Q Q Q s Q s s

K P Q s Q s Q s s

γ γ

ϕ

σ

δ

ϕ

τ

=

∫

 

(

) ( )

(

( )

)

(

( )

)

( )

(

)

(

( )

)

(

( )

)

, , , d

, , , , d

K P Q Q Q u v Q u v

K P Q u v Q u v Q u v

ϕ

τ

δ

σ

ϕ

τ

σ

Σ Σ =

∫

    

These formulas will be useful in the following.

The Eigenvalue Problem for 3D “Charged” Operators

Consider in 2

( )

dM

L



the eigenvalue problem ϕ µϕ=

 (6)

( )

: K

( ) ( )

,Q Q dMQ ϕ ⋅ =

∫

_ ⋅ ϕ

 (7)

where K P Q

(

,

)

is a symmetric kernel, and

( )

(

( )

)

(

( )

)

( )

(

)

(

( )

)

1 1

1

d d d

, , d

k

r n

Q h h k

h k

m

M Q Q m A Q s Q s s

Q u v Q u v

γ

ρ δ σ δ

τ δ σ

= = Σ = = + + +

∑

∑∫

_   (8)

(δ denoting the usual Dirac-Delta function). According to the above positions, we have

( )( )

(

) ( ) ( )

(

) ( )

(

)

(

( )

)

(

( )

)

(

( )

)

(

)

(

( )

)

(

( )

)

(

( )

)

(

) ( )

(

)

_{( )}

(

) ( )

)

_{( )}

(

) ( )

(

)

_{( )}

(

) ( )

)

_{( )} 1 1 1

, d ,

, d

, , , d

, , , , , , , , k r n m r

h h h

h n

k k

m

Q dQ Q

Q Q

P K P Q Q Q Q m K P A A

K P Q Q s Q s Q s s

K P Q Q s Q u v Q u v

K P Q Q K P Q Q

γ

ρ γ

ϕ ϕ ρ ϕ

ϕ σ δ

ϕ τ δ σ

ϕ ϕ ϕ ϕ Σ = = Σ = ∆ ∆ ∆ = + + + = + + +

∑

∫

∑∫ ∫

_       (9) so that

[

]

(

)( ) ( )

)

_{( )}

(

)( ) ( )

)

_{( )}

(

)( ) ( )

(

)

_{( )} , , , , r n

dM P dP P

P

U V U P V P U P V P

U P V P

γ ρ ∆ ∆ = + +    

(4)

[

]

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

(

) ( )

)

_{( )}

( )

)

( )

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

(

) ( )

)

_{( )}

( )

)

( )

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

(

) ( )

)

_{( )}

( )

)

( )

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

(

) ( )

)

_{( )}

( )

)

( ) , , , , , , , , , , , , , , , , , , , , , , , , , r n m r r r

n _r m _r

dM

Q dQ Q

P dP P dP

Q Q

P dP P dP

Q dQ Q

P P

Q Q

P P

U V

K P Q U Q V P K P Q U Q V P

ρ _ρ _ρ

γ _ρ _ρ

ρ γ Σ Σ ∆ ∆ ∆ ∆ ∆ ∆ ∆ _∆ ∆ _∆ = + + + + + + + 

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

(

) ( )

)

_{( )}

( )

)

( )

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

(

) ( )

)

_{( )}

( )

)

( )

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

(

) ( )

)

_{( )}

( )

)

( )

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

(

) ( )

)

_{( )}

( )

)

( ) , , , , , , , , , , , , , , , , , , , , , , , , r n n

n _n m _n

r

m m

n m

m m

Q dQ Q

P P

Q Q

P P

Q dQ Q

P P

Q _P Q

P

K P Q U Q V P K P Q U Q V P

ρ _γ _γ

γ _γ _γ

ρ γ Σ Σ Σ Σ Σ Σ ∆ ∆ ∆ ∆ _∆ ∆ ∆ ∆ ∆ ∆ ∆ _∆ ∆ _∆ + + + + + + + + Furthermore

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

(

) ( ) ( ) ( ) ( )

, , ,

, d d

Q dQ

P dP

K P Q U Q V P

K P Q U Q V P P Q P Q

ρ _ρ ρ ρ × =

∫∫

 

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( )

(

) ( ) ( )

1 , , , , d

rQ P dP

r

h h h

h

K P Q U Q V P

m U A K P A V P P P

ρ ρ ∆ = =

∑

∫



(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( )

(

) ( ) ( )

1 , , , , d r

Q dQ _P

r

h h h

h

K P Q U Q V P

m V A K P A U P P P

ρ ρ ∆ = =

∑

∫



where we used the symmetry of the kernel,

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( )

(

)

(

( )

)

(

( )

)

( ) ( )

1 , , ,

, d d

n

k

Q _{P dP}

n

k

K P Q U Q V P

K P Q s U Q s Q s V P P s P

γ _ρ

γ τ ρ

∆ = =

∑∫ ∫

_ 

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( )

(

)

(

( )

)

(

( )

)

( ) ( )

1 , , ,

, d d

n k Q dQ P n k k

K P Q U Q V P

K P Q s V Q s Q s U P P s P

ρ _γ

γ σ ρ

∆ = =

∑∫ ∫



(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

(

( ) ( )

)

(

( )

)

( )

(

)

(

( )

)

(

( )

)

1 1 1 2 , , , , , , ,

, , , d d

j m m m m Q P j

K P Q U Q V P K P t w Q u v U Q u v

V P t w τ Q u v τ P t w σ σ

Σ

∆ _∆ Σ Σ

= = = ×

∑∑∫ ∫

_   

(5)

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( )

(

)

(

( )

)

(

( )

)

( ) ( )

1 , , ,

, , , , d d

m Q P dP

m

K P Q U Q V P

K P Q u v U Q u v Q u v V P P P

ρ

τ ρ σ

Σ ∆ Σ = =

∑∫ ∫

   

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( )

(

)

(

( )

)

(

( )

)

( ) ( )

1 , , ,

, , , , d d

m

Q dQ

P

m

K P Q U Q V P

K P Q u v V Q u v Q u v U P P P

ρ

τ ρ σ

Σ ∆ Σ = =

∑∫ ∫

   

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

(

)

( )

1 1 , , , , r r Q P r r

h j h j h j

h j

K P Q U Q V P

m m K A A U A V A

∆ _∆ = = =

∑∑

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( )

(

( )

)

(

( )

)

(

( )

)

1 1 , , , , d n _r k Q P r n

h h h k

h k

K P Q U Q V P

m V A K A Q s U Q s Q s s

γ γ σ ∆ _∆ = = =

∑

∑∫

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( )

(

( )

)

(

( )

)

(

( )

)

1 1 , , , , d r n k Q P r n

h h h k

h k

K P Q U Q V P

m U A K A Q s V Q s Q s s

γ

γ σ

∆ _∆

= =

=

∑

∑∫

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( )

(

( )

)

(

( )

)

(

( )

)

1 1 , , ,

, , , , d

mQ _rP

r m

h h h

h

K P Q U Q V P

m V A K A Q u v U Q u v τ Q u v σ

Σ ∆ _∆ Σ = = =

∑

∑∫

  

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( )

(

( )

)

(

( )

)

(

( )

)

1 1 , , ,

, , , , d

r

m

Q

P

r m

h h h

h

K P Q U Q V P

m U A K A Q u v V Q u v τ Q u v σ

Σ ∆ _∆ Σ = = =

∑

∑∫

  

(

) ( )

(

)

_{( )}

( )

(

)

( )

(

)

(

( )

)

(

( ) ( )

)

(

( )

)

(

( )

)

1 1 , , ,

, , , , d d

m n Q P n m k k

K P Q U Q V P

V P s P s K P s Q u v U Q u v Q u v s

γ

σ τ σ

Σ ∆ _∆ Σ = = =

∑

∑∫

  

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( )

(

)

(

( )

)

(

( ) ( )

)

(

( )

)

(

( )

)

1 1 , , ,

, , , , d d

n m Q P n m k k

K P Q U Q V P

U P s P s K P s Q u v V Q u v Q u v s

γ

σ τ σ

Σ ∆ _∆ Σ = = =

∑

∑∫

  

(6)

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( ) ( )

(

)

(

( )

)

(

( )

)

(

( )

)

(

( )

)

1 1 , , ,

, d d

n _n k Q P n n k k

K P Q U Q V P

K P Q s U Q s V P Q s P s

γ _γ

γ γ σ σ σ σ σ σ

∆ _∆ = = =

∑∑∫ ∫

  

(

) ( )

(

)

_{( )}

( )

(

)

_{( )}

( ) ( )

(

)

(

( )

)

( )

(

)

(

( )

)

(

( )

)

1 1 1 2 , , , , , , ,

, , , d d

m m j Q P m m j

K P Q U Q V P

K P t w Q u v U Q u v

V P t w τ Q u v τ P t w σ σ

Σ Σ ∆ _∆ Σ Σ = = = ×

∑∑∫ ∫

_   

3. Computation of the Eigenvalues for Charged

Integral Equations

The computation of the eigenvalues of second kind Fredholm integral equations is usually performed by using the Rayleig-Ritz method [1] [11] for lower bounds, and the Fichera orthogonal invariants method [11] [12] for upper bounds. An alternative procedure, called the inverse iteration method, can be used to improve the lower approximations previously obtained by means of the Rayleigh-Ritz method. This approach was already considered in [13], and will be applied even in the present case.

We will not describe herewith the three above mentioned methods, because they are essentially independent of the dimension of the considered vibrating item (string, membrane or body). We refer for shortness to the above mentioned articles [8] [9] [11] [12].

4. Applications

Let ≡

[ ] [ ] [ ]

0,a × 0,b × 0,c , O≡

(

0, 0, 0 ,

)

U≡

(

a b c, ,

)

,

(

P, P, P

)

,

(

Q, Q, Q

)

,

(

P, P, Q

)

,

(

P, Q, Q

)

P≡ x y z Q≡ x y z R≡ x y z S≡ x y z

(

_Q, _P, _P

)

,

(

_Q, _Q, _P

)

,

(

_P, _Q, _P

)

,

(

_Q, _P, _Q

)

T ≡ x y z L≡ x y z M ≡ x y z N≡ x y z

0≤xP≤a, 0≤yP≤b, 0≤zP≤c

0≤x_Q≤a, 0≤y_Q≤b, 0≤z_Q≤c

(

)

, if , , ,

,

, if , , ,

, if

P Q P Q P Q

P Q

OP UQ x x y y z z

OQ UP x x y y z z

OR UL x x y y z z

OS UT x x y y z z

K P Q

OT US x x y y z z

OL UR x x y y z z

OM UN x x y y z z

ON UM x x

⋅ ≤ ≤ ≤ ⋅ ≥ ≥ ≥ ⋅ ≤ ≤ ≥ ⋅ ≤ ≥ ≥ = ⋅ ≥ ≤ ≤ ⋅ ≥ ≥ ≤ ⋅ ≤ ≥ ≤

(7)

(

)

(

) (

)

(

) (

)

(

) (

)

(

)

(

)

(

) (

)

2 2 2

, if , , ,

, if , ,

,

P P P Q Q Q P Q P Q P Q

Q Q Q P P P P Q P Q P Q

P P Q Q Q P P Q P Q P Q

P Q Q Q P P P Q P Q P Q

x y z a x b y c z x x y y z z

K P Q

+ + − + − + − ≤ ≤ ≤ + + − + − + − ≥ ≥ ≥ + + − + − + − ≤ ≤ ≥ + + − + − + − ≤ ≥ ≥ =

(

)

(

) (

)

(

) (

)

(

)

(

)

(

)

(

)

(

)

(

)

(

)

2 2 2

2

2 2

2 2 2

2

2 2

2 2 2

,

, if , , ,

, if , , .

Q P P P Q Q P Q P Q P Q

Q Q P P P Q P Q P Q P Q

P Q P Q P Q P Q P Q P Q

Q P Q P Q P P Q P Q P Q

x y z a x b y c z x x y y z z

   + + − + − + − ≥ ≤ ≤ + + − + − + − ≥ ≥ ≤ + + − + − + − ≤ ≥ ≤ + + − + − + − ≥ ≤ ≥                 

Obviously K P Q

(

,

)

=K Q P

(

,

)

, i.e. the kernel is symmetric, and even positive definite, since K P Q

(

,

)

>0, ∀

(

P Q,

)

∈.

Consider in 2

( )

dM

L



the eigenvalue problem ϕ µϕ=

 (2)

( ) ( )

: K ,Q Q dMQ

ϕ =

∫

⋅ ϕ



 (3)

where

( )

(

( )

)

(

( )

)

( )

(

)

(

( )

)

1 1 1

d d d

, , d

k

r n

Q h h k

h k

m

M Q Q m A Q s Q s s

Q u v Q u v

γ

ρ δ σ δ

τ δ σ

= = Σ = = + + +

∑

∑∫

_  

(δ denoting the usual Dirac-Delta function).

The considered operator is compact and strictly positive, since it is connected with free vibrations of a body charged by a finite number of masses concentrated on points, curves, or surfaces contained in .

Numerical Example 1

Let a= = =b c 1, and dM =ρ

(

x y z, ,

)

d d dx y z with

(

x y z, ,

)

1 2x 3y 4z

ρ = + + + (see Figure 1). Under this assumption, the

apprpximate eigenvalues evaluated by using the Rayleigh-Ritz and inverse iteration methods are listed in Table 1, whereas the relevant eigenfunctions are shown in Figure 2.

Numerical Example 2

Let a= = =b c 1, and dM =ρ

(

x y z, ,

)

d d dx y z with

(

)

( )2

(

)

3

, , 8π e x y z sin π

x y z xyz

ρ ₌ − + +

(see Figure 3). Under this assumption, the approximate eigenvalues evaluated by using the Rayleigh-Ritz and inverse iteration methods are listed in Table 2, whereas the relevant eigenfunctions are shown in Figure 4.

(8)

[image:8.595.215.535.73.277.2] [image:8.595.210.539.327.688.2]

Figure 1. Spatial distribution of the volume density function ρ

(

x y z, ,

)

relevant to the numerical Example 1.

(a) (b)

(c) (d)

(e) (f)

(9)

[image:9.595.219.530.69.274.2]

Figure 3. Spatial distribution of the volume density function ρ

(

x y z, ,

)

relevant to the numerical Example 2.

(a) (b)

(c) (d)

(e) (f)

[image:9.595.208.538.322.686.2]

(10)

[image:10.595.211.538.116.241.2]

Table 1. Approximate eigenvalues µh and µˆh (h=1, 2,, 6) relevant to the numerical Example 1, as computed by means of the Rayleigh-Ritz and inverse iteration method, respectively.

h µh µˆh

1 2.30919 2.30934

2 0.584150 0.584855

3 0.580232 0.580932

4 0.144731 0.145178

5 0.136953 0.137599

6 0.135077 0.135689

h µh µˆh

1 1.97474 1.97528

2 0.396395 0.396914

3 0.396395 0.396914

4 0.0957090 0.0960668

5 0.0912646 0.0917548

6 0.0912646 0.0917548

(

)

1 1 1

d , , d d d d d d

2 2 2

M =ρ x y z x y z+δ_x− _{ } δ y−  _{ }δ z− _ x y z

     

with

ρ

(

x y z

, ,

)

=

e

− − −x y z (see Figure 5). Under this assumption, the

approximate eigenvalues evaluated by using the Rayleigh-Ritz and inverse iteration methods are listed in Table 3, whereas the relevant eigenfunctions are shown in Figure 6.

Numerical Example 4 Let a= = =b c 1, and

(

)

(

) (

)

d , , d d d _h _h _h _h d d d

h

M =ρ x y z x y z+

∑

mδ x−x δ y−y δ z−z x y z with

(

x y z, ,

)

2 log 1

(

z

(

1 y

(

1 x

)

ρ = + + + _{, and} m_h=1

(

x_h+y_h+z_h

)

,

1 1

2

h h h

x y z h

H

 

= = = _ − _

  for h=1, 2, 3=H (see Figure 7). Under this assump-

tion, the approximate eigenvalues evaluated by using the Rayleigh-Ritz and inverse iteration methods are listed in Table 4, whereas the relevant eigenfunctions are shown in Figure 8.

(

)

(

) (

)

d , , d d d _h _h _h _h d d d

h

[image:10.595.209.540.301.430.2]

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[image:11.595.225.528.62.180.2] [image:11.595.209.538.228.588.2]

(a) (b)

Figure 5. Spatial distribution of the volume density ρ

(

x y z, ,

)

(a) and concentrated density functions (b) relevant to the numerical Example 3.

(a) (b)

(c) (d)

[image:11.595.205.537.640.734.2]

(e) (f)

Figure 6. Spatial distribution of the approximate eigenfunctions ϕˆh

(

x y z, ,

)

(h=1, 2,, 6) relevant to the numerical Example 3.

(

, ,

)

1 cos π

(

1 2 3 4

)

2

x y z x y z

ρ = + _ − − − _

 , and mh =3xh+2yh+zh,

1 2 5 6 1 3 5 7 1 2 3 4

1 4

x =x =x =x =y =y =y =y = =z z =z =z = ,

3 4 7 8 2 4 6 8 5 6 7 8

3 4

(12)

[image:12.595.213.537.63.197.2]

DOI: 10.4236/jamp.2017.510171 2062 Journal of Applied Mathematics and Physics (a) (b)

(

x y z, ,

)

(a) (b)

(c) (d)

(e) (f)

(

x y z, ,

)

(see Figure 9). Under this assumption, the approximate eigenvalues evaluated by using the Rayleigh-Ritz and inverse iteration methods are listed in Table 5, whereas the relevant eigenfunctions are shown in Figure 10.

[image:12.595.210.538.245.614.2]

(13)

[image:13.595.218.532.66.190.2]

(a) (b)

[image:13.595.211.538.286.393.2]

(

x y z, ,

)

h µh µˆh

1 0.827191 0.827079

2 0.0286952 0.0287715

3 0.0258541 0.0258874

4 0.0258541 0.0258874

5 0.0071926 0.00721217

6 0.00628217 0.00631337

h µh µˆh

1 1.37202 1.38514

2 0.461313 0.439849

3 0.150812 0.148043

4 0.122611 0.121926

5 0.115747 0.115269

6 0.0456381 0.0440725

h µh µˆh

1 11.6841 12.0167

2 3.25662 3.10980

3 3.18704 3.04593

4 1.27341 1.30780

5 0.211891 0.195245

[image:13.595.211.538.453.559.2] [image:13.595.210.539.618.734.2]

(14)

(c) (d)

[image:14.595.209.540.64.438.2]

(e) (f)

(

x y z, ,

)

Let a= = =b c 1, and

(

)

(

) (

)

(

) (

)

d , , d d d , , d d d

d d d s

h h h

h

M x y z x y z x y z z x y z

x x y y z z x y z

ρ

σ

δ

ζ

δ

= + −

+

∑

− − −

with

ρ

(

x y z, ,

)

=_cosh 2

(

y− +x

)

sin 2

(

x−y

)

_

(

1 e+ x y+

)

,

σ

(

x y z

, ,

)

=

e

− − +2x3y4z_, 1

4 s

ζ = _{, and} 1 1 1

2

h h

x y h

H

 

= − = _ − _

 ,

4 5 h

z = for h=1, 2=H (see Figure 11).

Under this assumption, the approximate eigenvalues evaluated by using the Rayleigh-Ritz and inverse iteration methods are listed in Table 6, whereas the relevant eigenfunctions are shown in Figure 12.

Numerical Example 7

Let a= = =b c 1 , and

(

)

(

) (

)

(

) (

)

d , , d d d , , d d d

d d d s

h h h h

h

M x y z x y z x y z x x y z

m x x y y z z x y z

ρ

σ

δ

ξ

δ

= + −

+

∑

− − −

with

(

)

(

) (

₂

) (

₂

)

₂ 1 2

, , sech 4 1 2 1 2 1 2

x y z x y z

ρ = _ _ − + − + − _ _

 ,

(

x y z, ,

)

5x sin 2

( ) ( )

y cos 3z

σ = + , 1

5 s

ξ = , and mh=yh+zh, 3 4 h

(15)

[image:15.595.212.535.59.186.2]

(

x y z, ,

)

(a) (b)

(c) (d)

(e) (f)

(

x y z, ,

)

1 1

2

h h

y z h

H

 

= = _ − _

  for h=1, 2, 3=H (see Figure 13). Under this assumption,

[image:15.595.210.539.235.602.2]

(16)

[image:16.595.217.532.61.180.2] [image:16.595.210.537.228.591.2]

(a) (b)

(

x y z, ,

)

(a) (b)

(c) (d)

(e) (f)

(

x y z, ,

)

(

)

( )

(

( )

)

(

d , , d d d , , d d d

3 3 3

d d d

4 4 4

s

M x y z x y z x y z x y x y z

x y z x y z

ρ σ δ ζ

δ δ δ

= + −

    

+ _ − _{ } − _ − _

(17)

[image:17.595.211.538.116.241.2]

h µh µˆh

1 1.46677 1.48646

2 0.636561 0.607952

3 0.142093 0.142025

4 0.0434152 0.0451649

5 0.0287695 0.0298745

6 0.0179072 0.0179560

h µh µˆh

1 1.71928 1.75065

2 0.404514 0.391333

3 0.327108 0.328006

4 0.155119 0.152451

5 0.133601 0.132340

6 0.0390550 0.0389560

with

ρ

(

x y z, ,

)

=_3 2 cos+

( )

xz +sin

( ) (

yz _ 1 2+ x+3y+4z

)

, and

( ) (

x y, 1 2x 3y

)

7 cos 6

( )

y sin 5

( )

x

σ

= + + _ + + __,

( )

1

( )

, 1 cos sin 3

2

s x y x y

ζ = _ + − __(see_{Figure 15}_{). Under this assumption, the}

approximate eigenvalues evaluated by using the Rayleigh-Ritz and inverse iteration methods are listed in Table 8, whereas the relevant eigenfunctions are shown in Figure 16.

(

)

( )

(

( )

)

( )

(

( )

)

(

( ))

(

) (

)

d , , d d d , , d d d

d d d

d d d s

l l

h h h h

h

M x y z x y z x y z x y x y z

z x z y z x y z

m x x y y z z x y z

ρ σ δ ζ

τ δ ξ δ η

δ δ δ

= + −

+ − −

+

∑

− − −

with ρ

(

x y z, ,

)

= +4 3cos 3

( )

x +2 sin 2

( )

y +tan

( )

z , σ

(

x y,

)

=2 log 1

(

+ +y xy

)

,

( )

1

(

)

, 1 2

4

s x y x y

ζ = − + _,τ

( )

z =2z, ξ_l

( )

z =η_l

( )

z =4z−3, and

h h h h

m =x +y +z , 1 2 1 3 1 4

x =x = y =y = , 3 4 2 4 3 4

x =x =y =y = , 2

3 h

[image:17.595.212.539.300.433.2]

(18)

[image:18.595.212.539.75.195.2]

(

x y z, ,

)

(a) (b)

(c) (d)

(e) (f)

(

x y z, ,

)

1, 2, 3, 4

h= =H (see Figure 17). Under this assumption, the approximate

[image:18.595.211.538.241.629.2]

(19)

[image:19.595.211.537.68.199.2]

(

x y z, ,

)

(a) (b)

(c) (d)

(e) (f)

(

x y z, ,

)

Acknowledgements

[image:19.595.209.538.244.640.2]

(20)

[image:20.595.211.539.116.241.2]

h µh µˆh

1 1.24996 1.28312

2 0.232580 0.236340

3 0.212298 0.213581

4 0.175619 0.177549

5 0.0535925 0.0541850

6 0.0451690 0.0465835

h µh µˆh

1 6.85505 6.97836

2 1.56685 1.51732

3 1.18747 1.16698

4 0.588232 0.594839

5 0.301015 0.320349

6 0.197606 0.200069

development program running at The Antenna Company, and is funded by the Competitiveness Enhancement Program grant at Tomsk Polytechnic University.

References

[1] Mikhlin, S.G. (1964) Integral Equations and Their Applications. 2nd Edition, Per-gamon Press, Oxford.

[2] Zaanen, A.C. (1953) Linear Analysis. Measure and Integral, Banach and Hilbert Space, Linear Integral Equations. Interscience Publ. Inc., New York, North Holland Publ. Co., Amsterdam, P. Noordhoff N.V., Groningen.

[3] Anselone, P.M. (1971) Collectively Compact Operator Approximation Theory and Application to Integral Equations. Prentice-Hall Series in Automatic Computation. Prentice-Hall, Inc., Englewood Cliffs.

[4] Miranda, C. (1992) Opere Scelte. Cremonese, Roma.

[5] Miranda, C. (1938) Alcune generalizzazioni delle serie di funzioni ortogonali e loro applicazioni. [On Some Generalizations of Orthogonal Function Series Expansions and Relevant Applications.] Rend.Sem. Mat. Torino, 7, 5-17.

[6] Jung, I.H., Kwon, K.H., Lee, D.W. and Littlejohn, L.L. (1995) Sobolev Orthogonal Polynomials and Spectral Differential Equations. Transactions of the American Mathematical Society, 347, 3629-3643.

[image:20.595.211.539.300.424.2]

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DOI: 10.4236/jamp.2017.510171 2071 Journal of Applied Mathematics and Physics [7] Marcellán, F., Pérez, T.E., Piñar, M.A. and Ronveaux, A. (1996) General Sobolev

Orthogonal Polynomials. Journal of Mathematical Analysis and Applications, 200, 614-634. https://doi.org/10.1006/jmaa.1996.0227

[8] Natalini, P. and Ricci, P.E. (2006) Computation of the Eigenvalues of Fred-holm-Stieltjes Integral Equations. Applicable Analysis, 85, 607-622.

https://doi.org/10.1080/00036810500397039

[9] Natalini, P., Patrizi, R. and Ricci, P.E. (2006) Eigenfunctions of a Class of Fred-holm-Stieltjes Integral Equations via the Inverse Iteration Method. Journal of Ap-plied Functional Analysis, 1, 165-181.

[10] Caratelli, D., Natalini, P. and Ricci, P.E. (2017) Computation of the Eigenvalues of 2D “Charged” Integral Equations.

[11] Fichera, G. (1973) Abstract and Numerical Aspects of Eigenvalue Theory. Lecture Notes, The University of Alberta, Dept. of Math., Edmonton.

[12] Fichera, G. (1978) Numerical and Quantitative Analysis. Surveys and Reference Works in Mathematics, Pitman, Boston, London.

[13] Natalini, P., Noschese, S. and Ricci, P.E. (1999) An Iterative Method for Computing the Eigenvalues of Second Kind Fredholm Operators and Applications. Leganés, 9, 128-136.

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: 10.4236/jamp.2017.510171

http://creativecommons.org/licenses/by/4.0/

https://doi.org/10.1090/S0002-9947-1995-1308015-9

4. https://doi.org/10.1006/jmaa.1996.0227

https://doi.org/10.1080/00036810500397039