Authors

  • Shaxobiddin Kuziev

DOI:

https://doi.org/10.71337/inlibrary.uz.jasss.96826

Abstract

 In this work, we discuss the construction of derivative optimal quadrature formulas in the Sobolev space. We derive an analytical expression for an error functional norm and obtain a system of linear equations Winner-Hopf type by the coefficients using the method of Lagrange multipliers. Further, we apply the Sobolev method to get the analytical representation of the optimal coefficients. Using these coefficients, we calculate the norm of the error functional of the optimal quadrature formula in cases  and  in Sobolev  space. We verify our theoretical conclusions with the help of numerical experiments.

 

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159

OPTIMAL QUADRATURE FORMULA OF THE HERMITE TYPE

Kuziev Shaxobiddin Sobirovich

Kokand University, Department of Digital Technologies

and Mathematics, (PhD)

shaxobiddin.qoziyev.89@gmail.com

Abstract:

In this work, we discuss the construction of derivative optimal quadrature formulas in

the Sobolev space. We derive an analytical expression for an error functional norm and obtain a

system of linear equations Winner-Hopf type by the coefficients using the method of Lagrange

multipliers. Further, we apply the Sobolev method to get the analytical representation of the

optimal coefficients. Using these coefficients, we calculate the norm of the error functional of

the optimal quadrature formula in cases

3

m

=

and

4

m

=

in Sobolev

( )

2

(0,1)

m

L

space. We verify

our theoretical conclusions with the help of numerical experiments.

Keywords:

Error functional, extremal function, Sobolev space, optimal coefficients, optimal

quadrature formula.

INTRODUCTION

Currently, there are various approaches to constructing quadrature formulas. One of them

is the classical approach to constructing quadrature formulas, which requires the accuracy of the

polynomial formula for the highest possible order. Another of them is the functional approach to

constructing quadrature formulas. In this case, the quadrature formula is designed to minimize

the error functional norm in a given Banach space [1,2,3,4]. Article [5] presents new and

effective quadrature formulas, which merge function and first derivative estimation at equally-

spaced data points, with a particular emphasis on improving computational efficiency in terms of

both cost and time. The objective of the research presented in work [6] is to simplify the

computation of the components involved in the integral transformation, denoted as

m

F

and

0.

m

The analytical expressions for these components encompass definite integrals. Instead of

the Newton-Cotes formulas, it is proposed to use non-trivial quadrature formulas with unevenly

distributed integration points. The quadrature method is essential in the approximate solution of

integral equations. In [7], the trapezoidal numerical integration formula is used to solve the

Fredholm-Hammerstein integral equations. In [8], the perturbed Milne quadrature rule was

derived for

n

-fold differentiable functions. The following articles [9-16] create optimal

quadrature formulas for different Hilbert spaces. Additionally, precise estimates of the designed

recipes are provided.

The work consists of the following sections: In the first Section, we introduce the

introduction; In the second Section, the statement of the problem of constructing a quadrature

formula is considered; In the third Section, the general form of the error functional norm is

derived using the extremal function. In Section 4, we derived a system of linear equations of the

Wiener-Hopf type for the coefficients of the quadrature formula being considered in the space

( )

2

(0,1)

m

L

. Additionally, we found the analytical form of the optimal coefficients of the quadrature

formulas (1) for

3

m

=

and

4

m

=

in the space

( )

2

(0,1)

m

L

.


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160

STATEMENT OF THE PROBLEM

We consider the following quadrature formula

[ ]

[ ]

1

2

0

1

0

0

0

( )

( )

( '(0)

'(1))

''( )

12

N

N

h

f x dx

f h

f

f

f

K

K

h

b

b

b

b

b

b

=

=

@

+

-

+

(1)

where

0

,

= 0, ,

2

[ ] =

,

= 1,

1,

h

N

K

h

N

b

b

b

-

, and

1

[ ]

K

b

are the unknown coefficients of the quadrature formula

(1),

( )

2

(0,1)

m

f L

,

( )

2

(0,1)

m

L

is the space of functions which are square integrable with

m

-th

generalized derivative.

The difference between the exact value and the approximated value obtained from the quadrature

formula is referred to as the error of the formula.

[ ]

[ ]

1

2

0

1

0

0

0

( )

( )

( '(0)

'(1))

''( )

12

N

N

h

f x dx

f h

f

f

f

K

K

h

b

b

b

b

b

b

=

=

-

-

-

-

(

)

( ) ( )

, .

N

N

x f x dx

f

-

=

=

l

l

(2)

From difference (2) it follows that quadrature formula (1) corresponds to the following error

functional

[ ]

[ ]

[ ]

2

0

1

0,1

0

0

( )

( )

(

)

( '( )

'( 1))

''(

)

12

N

N

N

h

x

x

K

x h

x

x

K

x h

b

b

c

b d

b

d

d

b d

b

=

=

=

-

-

+

-

-

-

-

l

(3)

where,

( )

x

d

is the Dirac’s delta-function, and

[0,1]

( )

x

c

is the characteristic function of the interval

[0,1]

.

The norm in space

( )

2

(0,1)

m

L

is defined by the following form:

(

)

( )

2

1

2

2

( )

(0,1)

0

( )

.

m

m

L

f

f

x dx

=

In addition, the error functional (3) is required to satisfy the following conditions

( ( ), ) = 0,

= 0,1,2,...,

1.

N

x x

m

a

a

-

l

(4)

Error (2) based on the Cauchy-Schwartz inequality is estimated as follows

( )

( )*

(0,1)

(0,1)

2

2

| ( , ) |

.

N

m

N

m

L

L

f

f

l

l

P P

P P

(5)

Here, we have been receiving the following tasks.

1. Find the norm of the error functional (3) of the quadrature formula (1).

2. Finding the coefficients

1

[ ],

0,

K

N

b

b

=

, which give the smallest value to the norm

( )*

2

m

N L

l

of

the error functional (3), that is, calculating the value

1

( )*

( )*

2

[ ]

2

.

inf

m

N

m

L

L

K

N

b

=

o

l

l

(6)

It is important to mention that, the coefficients

1

[ ],

0,

K

N

b

b

=

satisfying the quantity (6) of the

error functional (3), i.e., specifying the minimum norm

( )*

2

m

N L

l

. If such coefficients exist, these


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161

are called optimal coefficients, denoted as

1

[ ].

K

b

o

To solve these problems, we must sequentially

do the following:

a ) find the general form of

N

l

;

b ) seek the optimal coefficient

1

[ ]

K

b

o

that minimizes the norm of the error functional

N

l

.

In the following section, our main objective is to determine the norm of the error functional.

FINDING AND MINIMIZATION OF THE NORM OF THE ERROR FUNCTIONAL

We will use Riesz’s theorem on the general form of a linear continuous functional in a Hilbert space [17,18]. For an arbitrary linear continuous functional

defined in the space

, the only element of this space,

the equalities hold for all

( , )

,

N

N

f

U

f

=<

>

l

l

and

( )*

( )

2

2

m

m

N

N L

L

U

=

l

l

,

where

(

1

( )

)

0

( )

( )

,

N

m

m

U

x f

x dx

U

f

=

<

>

l

.

This means that, to find the general form of the norm of the error functional (3), it is necessary to

determine the function

.

N

U

l

To do this, we will use the concept of an extremal function

introduced by S.L. Sobolev [1]. The extremal function

N

U

l

corresponding to the error functional

N

l

defined by S.L. Sobolev on

( )

2

(0,1)

m

L

space

1

( ) ( 1)

( )* ( )

( ).

N

m

N

m

m

U

x

x

x P

x

m

-

= -

+

l

l

(7)

where

2

1

( )

2 (2

1)!

m

m

x

x

m

m

-

=

-

and this is the solution to equation

2

2

( )

( )

m

m

m

d

x

x

dx

m

d

=

[1],

1

( )

m

P

x

-

degree polynomial

1

m

-

and

*

is

the action of convolution.

Recall that the convolution of two functions

j

and

g

is defined as follows [1]

( )* ( )

(

) ( )

( ) (

) .

x g x

x y g y dy

y g x y dy

j

j

j

+

+

-

-

=

-

=

-

The following equality is appropriate for the error functional

N

l

norm in space

( )

2

(0,1)

m

L

*

[1].

( )*

2

2

( ,

) ( ( ),( 1)

( )* ( ))

m

N

m

N

N

N

N

m

L

U

x

x

x

m

=

=

-

l

l

l

l

l

Having performed some calculations on the right side of the last equality, we take the general

form of the squared norm of

N

l

corresponding to the quadrature formula (1)

2

( )*

2

(0,1)

( , )

( ) ( )

m

N

N

N

L

U

x U x dx

+

-

=

=

l

l

l

l

l


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162

[ ]

[ ]

[ ]

2

5

2

3

1

1

1

1

0

0

0

0

( 1)

2

2(2

5)!

2(2

3)!

m

m

N

N

N

m

h

h

x h

K

K

K

dx

m

m

b

g

b

b

g

b

b

g

b

-

-

=

=

=

-

-

= -

-

-

-

[ ]

2

2

4

2

4

1

0

( )

(

1)

6

2(2

4)!

m

m

N

h

h

h

K

m

b

b

b

b

-

-

=

+

-

-

-

[ ]

[ ]

[ ]

2

3

2

2

2

2

2

0

1

0

0

0

0

( )

(

1)

2

2(2

3)!

6

2(2

2)!

m

m

m

N

N

N

h

h

h

h

h

K

K

K

m

m

b

g

b

b

g

b

b

b

g

b

-

-

-

=

=

=

-

+

-

+

-

-

-

[ ]

[ ]

[ ]

2

1

2

1

1

0

0

0

0

0

0

0

2

2(2

1)!

2(2

1)!

m

m

N

N

N

x h

h

h

K

dx

K

K

m

m

b

b

g

b

b

g

b

b

g

-

-

=

=

=

-

-

-

+

-

-

2

4

1

.

6(2

1)! (2

1)! 144(2

3)!

h

h

m

m

m

+

+

+

-

+

-

(8)

So, problem 1 is completely solved.

TO FIND THE COEFFICIENTS OF OPTIMAL QUADRATURE FORMULA

Let us determine the coefficients

1

[ ]

K

b

o

. To do this, we construct the following Lagrange

function

(

)

1

1

1

0

1

1

[0], [1],..., [ ], , ,...,

m

K

K

K N

l l

l

-

Y

(

)

( )*

2

1

2

0

2 ( 1)

,

.

m

m

m

N

N

L

x

a

a

a

l

-

=

=

-

-

l

l

(9)

From the constructed Lagrange function (9), we get the partial derivatives by the coefficients

1

[ ], (

0,..., )

K

N

b

b

=

and,

,(

0,1,...,

1)

m

a

a

l

=

-

. As result of this we have the following system of

linear equations respect to

1

[ ]

K

b

and

a

l

[ ]

( )

1

3

0

(

)

( )

( ),

0, ,

N

IV

m

m

m

K

h

h

P

h

F h

N

g

g m

b

g

b

b

b

-

=

-

+

=

=

(10)

[ ]

3

3

1

0

1

!

( )

,

0,

3,

!(

3 )!

N

j

j

j

B

K

h

h

m

j

j

a

a

a

a

g

a

g

g

a

a

+ -

+ -

=

=

= -

=

-

+ -

(11)

where

2

5

( )

2

| |

( ) =

( ) =

2(2

5)!

m

IV

m

m

x

x

x

m

m

m

-

-

-

,

3

( )

m

P

h

b

-

is an unknown polynomial of degree

3

m

-

whose

coefficients are expressed through

a

l

,

3

i

B

+

are Bernoulli numbers, and

3

2

2

2

5

2

5

3

3

3

2

2

0

1

( 1)

( )

( )

.

(2

5 )!

( 3)!

2 !( 3

)!

(2

2)!

i

i

m

m

i

m

i

i

j

i

j

i

m

m

i

j

B

B h

B

h

h

F h

h

m

i

i

j i

j

m

b

b

+

-

- -

-

+ -

+ -

+

-

=

=

-

=

-

+

+

- -

+

+ -

-

(12)

This system has a unique solution at every fixed

N

point and gives a minimum value to norm,

( )*

2

2

m

N L

l

is a quadratic function of

1

[ ], (

0,..., )

K

N

b

b

=

the coefficients of many variables. As a

result, it reaches a single minimum at a specific value of

1

1

[ ]

[ ]

K

K

b

b

=

o

. The ones found

1

[ ]

K

b

o

are

called optimal coefficients and the corresponding quadrature formula is called optimal

quadrature formula.


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Theorem 1.

For the coefficients of the optimal quadrature formula (1) in

(3)

2

(0,1)

L

Sobolev

space, the following equalities hold:

1

[ ] = 0,

= 0, .

K

N

b

b

(13)

Theorem 2.

For the coefficients of the optimal quadrature formula (1) in

(4)

2

(0,1)

L

Sobolev

space the following equalities hold:

1

[0] =

,

1

N

q q

K

ah

q

-

-

(

)

1

[ ] =

,

= 1,

1,

N

K

ah q

q

N

b

b

b

b

-

+

-

(14)

1

[ ] =

,

1

N

q q

K N

ah

q

-

-

where

2

120(1

)

N

h

a

q

=

+

,

3 2

q

=

-

.

Here we are engaged in the proof of Theorem 2, and Theorem 1 is proved analogously .

Proof of Theorem 2.

We can obtain the following system by using equations (10)

through (12) from

4

m

=

[ ]

( )

1

4

1

4

0

(

)

( )

( ),

0, ,

N

IV

K

h

h

P h

F h

N

g

g m

b

g

b

b

b

=

-

+

=

=

(15)

[ ]

1

0

0,

N

K

g

g

=

=

(16)

[ ]

1

0

( ) 0,

N

K

h

g

g

b

=

=

(17)

where

3

( )

4

( )

12

IV

h

h

b

m

b

=

,

4

6

2

4

1

( )

( ) ( )

.

1440

2

30240

h

h

F h

h

h

b

b

b

=

-

+

+

(18)

Now we should solve the system of linear equations (10)-(11). To get an analytical solution for
these equations, we need a discrete analogue of the differential operator

4

4

d

dx

(see,[19]), i.e.,

| |

2

4

6 3 ,

| | 2,

6

( ) =

19 12 3,

| |= 1,

6 3 8,

= 0.

q

D h

h

b

b

b

b

b

-

-

(19)

here

3 2

q

=

-

,

1,

0,

( )

0,

0

h

b

d b

b

=

=

and the operator

2

( )

D h

b

has the following properties

2

2

( )* ( ) = ( ),

D h

G h

h

b

b

d b

2

=

0,

0

3,

( )( ) =

(2

4)!,

= 4.

k

k

D h

h

m

k

b

b

b

-

-


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164

When

0

b

<

and

N

b

>

let

1

[ ] 0

K

b

=

. Then we write equation (15) in the form of convolution

[ ]

( )

1

4

1

4

( )

( )

( ),

0, .

IV

K

h

P h

F h

N

g m

b

b

b

b

*

+

=

=

(20)

We will use the notation presented on the left-hand side of equation (20).

( )

1

4

1

( ) = [ ]

( )

( ).

IV

u h

K

h

P h

b

b m

b

b

*

+

(21)

According to properties

2

( )

D h

b

we have

[ ]

(

)

( )

1

2

2

4

1

1

[ ] ( ) =

( )

( )

[ ]

( )

IV

K

hD

u h

hD h

h

K

P h

b

b

b

b

m

b

b

b

=

*

*

*

+

(

)

( )

1

2

4

1

1

=

[ ]*

( )*

( )

[ ]* ( )

[ ].

IV

hK

D h

h

hK

h

K

b

b m

b

b d b

b

=

=

(22)

This means that to find the optimal coefficients

1

[ ]

K

b

it is necessary to determine the values of

the function

( )

u h

b

for all integer values

b

.

for

0,1,...,

N

b

=

4

( ) = ( )

u h

F h

b

b

.

for

0

b

<

[ ]

[ ]

3

2

1

1

0

0

( )

( )

( ) =

( )

12

4

N

N

h

h

u h

K

K

h

g

g

b

b

b

g

g

g

=

=

-

+

[ ]

[ ]

2

3

1

1

1

1

0

0

( )

1

( )

( )

( )

( ).

4

12

N

N

h

K

h

K

h

P h

Q h

g

g

b

g

g

g

g

b

b

-

=

=

-

+

+

=

(23)

According to

N

b

>

[ ]

[ ]

3

2

1

1

0

0

( )

( )

( ) =

( )

12

4

N

N

h

h

u h

K

K

h

g

g

b

b

b

g

g

g

=

=

-

[ ]

[ ]

2

3

1

1

1

1

0

0

( )

1

( )

( )

( )

( ).

4

12

N

N

h

K

h

K

h

P h

Q h

g

g

b

g

g

g

g

b

b

+

=

=

+

-

+

=

(24)

From (23) and (24) we have the following

1

4

1

( ),

0,

( ) =

( ), 0

,

( ),

,

Q h

u h

F h

N

Q h

N

b

b

b

b

b

b

b

-

+

(25)

here

1

0

1

( )

( )

Q h

d h

d

b

b

-

-

-

=

+

,(26)

1

0

1

( )

( )

Q h

d h

d

b

b

+

+

+

=

+

(27)

0

1

0

1

, , ,

d d d d

-

-

+

+

-

unknown constants.

First, let's find the values

[ ]

1

K

b

for

1, 2,...

1

N

b

=

-

[ ]

1

2

2

( ) ( )

(

) ( )

K

hD h

u h

h

D h

h u h

g

b

b

b

b

g

g

=-

=

*

=

-

1

2

2

4

2

0

1

(

) ( )

(

) ( )

(

) ( )

N

N

h

D h

h u h

D h

h F h

D h

h u h

g

g

g

b

g

g

b

g

g

b

g

g

-

=-

=

= +

=

-

+

-

+

-

2

4

2

1

4

1

( )

( )

(

)( (

)

(

))

hD h

F h

h D h

h Q

h

F h

g

b

b

b

g

g

g

-

=

=

*

+

+

-

-

-

2

1

4

1

(

(

))( ( (

))

( (

)))

h D h

h N

Q h N

F h N

g

b

g

g

g

+

=

+

-

+

+

-

+


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5

1

1

1

4

1

4

1

1

(3 2 3)

( (

)

(

))

( ( (

))

( (

)))

N

h

q

q Q

h

F h

q

q Q h N

F h N

b

g

b

g

g

g

g

g

g

g

-

-

- -

+

=

=

=

-

-

-

-

+

+

-

+

,

N

h aq

bq

b

b

-

=

+

(28)

where

4

1

4

1

3

( (

)

(

)),

a

h

q Q

h

F h

g

g

g

g

-

=

=

-

-

-

4

1

4

1

3

( ( (

))

( (

))).

b

h

q Q h N

F h N

g

g

g

g

+

=

=

+

-

+

This means that after these notations the coefficients

[ ]

1

,

1,

1

K

N

b

b

=

-

will take the following

form:

[ ]

1

,

1,

1.

N

K

h aq

bq

N

b

b

b

b

-

=

+

=

-

(29)

Now we find

a

and

b

. To do this, we calculate on the left side of (15) the following sum

[ ]

[ ]

3

3

1

1

1

2

0

( )

( )

0

( )

( ),

12

6

N

h

h

h

S h

K

K

S h

S h

g

b

g

b

b

g

b

b

=

-

=

=

+

-

(30)

[ ]

3

1

1

1

1

(

)

( )

,

6

h

h

S h

K

b

g

b

g

b

g

-

=

-

=

[ ]

3

2

1

0

(

)

( )

.

12

N

h

h

S h

K

g

b

g

b

g

=

-

=

Then calculate

1

( )

S h

b

, for this we will use (29)

[ ]

3

3

1

1

1

1

1

1

(

)

(

)

( )

6

6

N

h

h

h

h

S h

K

h aq

bq

b

b

g

g

g

g

b

g

b

g

b

g

-

-

-

=

=

-

-

=

=

+

4

1

1

3

3

1

1

.

6

N

h aq q

bq

q

b

b

b

g

b

g

g

g

g

g

-

-

-

-

=

=

=

+

To calculate this amount we will use the following formula [20]

1

0

0

0

1

0

|

1

1

1

1

i

i

n

n

k

k

k

i k

i k

n

i

i

q

q

q

q

q

q

q

q

g

g

g

g

g

-

=

=

=

=

=

D

-

D

-

-

-

-

(31)

here

i k

g

D

this

i

- finite order difference

k

g

,

0

0

|

i k

i k

g

g

=

D

= D

and

1

0

( 1)

i

i k

i

k

i

C

-

=

D

=

-

l

l

l

l

.

4

1

1

3

3

3

3

1

1

1

1

1

0

0

1

( )

0

6

1

1

1

1

i

i

i

i

i

i

h

q

q

q

S h

aq

q

q

q

q

b

b

b

b

-

-

-

-

-

-

-

=

=

=

D

-

D

-

-

-

-

4

3

3

3

3

0

0

1

0

6

1

1

1

1

i

i

N

i

i

i

i

h

q

q

q

bq

q

q

q

q

b

b

b

-

=

=

+

D

-

D

-

-

-

-

(32)

here

2

2

( )

4 1

q

E q

q

q

-

=

+

+

- since this is the root of the Euler- Ferabenius polynomial of the

second order

1

3

3

3

3

1

0

0

0

0,

0

0.

1

1

i

i

i

i

i

i

q

q

q

q

-

-

=

=

D

=

D

=

-

-


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To mean

4

3

3

3

3

1

0

0

1

( )

.

6

1

1

1

1

i

i

N

i

i

i

i

h

aq

bq

q

S h

q

q

q

q

b

b

b

=

=

=

-

D

-

D

-

-

-

-

(33)

Using the following formula [19] we simplify (33)

0

0

.

p

p

p

x

x

p

n

a n

a

n

n

-

=

D

=

D

(34)

3

3

3

3

1

1

0

0

0

( )

1

( )

0

0 .

!(3

)!

1

1

1

1

i

i

j

N

j

i

j

i

j

j

i

i

h

aq

bq

q

S h

h

j

j

q

q

q

q

b

b

-

+

=

=

=

= -

D

-

D

-

-

-

-

-

(35)

Calculate

2

( )

S h

b

[ ]

[ ]

3

3

3

2

1

1

0

0

0

(

)

( ) ( 1)

( )

( )

12

2 !(3 )!

j

j

N

N

j

j

h

h

h

S h

K

K

h

j

j

g

g

b

g

b

b

g

g

g

-

=

=

=

-

-

=

=

-

[ ]

[ ]

[ ]

3

3

2

3

1

1

1

2

0

0

0

( ) ( 1)

1

( )

( )

( ) .

2 !(3 )!

4

12

j

j N

N

N

j

j

h

h

K

h

K

h

K

h

j

j

g

g

g

b

b

g

g

g

g

g

g

-

=

=

=

=

-

=

=

-

-

(36)

We substitute equalities (35) and (36) into (15), equate the corresponding powers

( )

h

b

[ ]

1

0

,

1

N

aq bq

K

h

q

-

=

-

(37)

2

1

4

2

,

24

( 1)

N

B h

aq bq

q

+

+

=

-

(38)

3

4

2

1

1

0

(

(1

)) 1

( ) ( )

[ ]( )

12(1 )

4

1440

N

N

h aq bq

q

h

P h

h

K

h

q

g

b

b

g

g

=

-

+

=

+

-

-

4

3

1

0

1

[ ]( )

.

12

2880

N

h

K

h

g

g

g

=

-

+

(39)

In equality (16) using (29) and (37) we find the form

[ ]

1

K N

.

[ ]

1

.

1

N

bq aq

K N

h

q

-

=

-

(40)

As stated above, we can expand equality (17) using (34), (37) and (40)

[ ]

1

1

1

2

1

1

1

0

0

( 1)

( )

0

(1 )

i

N

N

N

i

i

i

aq bq

K

h

h

q

g

g

g

+

+

+

=

=

+

-

=

D

-

1

1

1

1

1

0

0

( 1)

0

0.

!(1

)!

1

1

(1

)

p

N i

N

N

i p

i

p

i

h

aq

bq

aq

bq

h

h

p

p

q

q

q

+

+

+

+

=

=

+

-

-

D

+

+

=

-

-

-

-

(41)

From equality (41) we equate the corresponding powers

h

1

1

2

2

,

(1 )

(1 )

N

N

aq bq

aq

bq

q

q

+

+

+

+

=

-

-

(42)

and from (38) and (42) we find

a

and

b

2

2

,

120(1

)

120(1

)

N

N

h

h

a

b

q

q

=

=

+

+

, i.e

a b

=

.

Theorem 2 is completely proved.


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167

NUMERICAL RESULTS

We present the numerical values of

( )*

2

m

N L

l

of the quadrature formula in

(4)

2

(0,1)

L

. In

addition , we will calculate integrals of specific functions. We define

( )

N

R f

as the absolute

error of the optimal quadrature formula in space

(4)

2

(0,1).

L

Then from the Cauchy-Schwartz

inequality we possess the following:

( )

( )

( )

4

4

2

2

N

N

L

L

R f

f

*

l

the norm of

(4)*

2

N L

l

of the optimal quadrature formula of the Hermite type constructed in space

(4)

2

(0,1)

L

as follows.

7

( ) ln( 1)

f x

x

x

=

+ +

,

1

0

( )

I

f x dx

=

.

Table 1. We present the values of the norm of the error functional (3) of the optimal quadrature

formula (1) in space

(4)

2

(0,1)

L

at

10,

N

=

100

N

=

and

1000.

N

=

TABLE 1.

Squared norm of error functional of optimal quadrature formula

10

N

=

100

N

=

1000

N

=

( )

4

2

N L

*

l

9.38093 * 10 ^

( -15)

8.37829*10 ^

( -23)

8.27817*10 ^

( -31)

Values of the integral of the function

( )

f x

at

10; 20; 30; 40; 50

N

=

calculate using the optimal

quadrature formula of the Hermite type in space

(4)

2

(0,1)

L

[14 ] and denote it as

O

1

EM

At the same time, the values of the integral of the function

( )

f x

at

10; 20; 30; 40; 50

N

=

. Let us

calculate using the coefficients of the optimal quadrature formula of the Euler-Maclaurin type

constructed in space

(4)

2

(0,1)

L

and denote it as

O

2

EM.

TABLE 2.

Error of optimal quadrature formula

N

I ( Exact value )

|I-O

1

EM|

|I- O

2

EM|

10

0.5112943610

4. 4*10^(-6)

3.2*10^(-6)

20

0.5112943610

1.4 *10^(-7)

1.0*10^(-7)

30

0.5112943610

1.9 *10^(-8)

1.3*10^(-8)

40

0.5112943610

4.5*10^(-9)

3.0*10^(-9)

50

0.5112943610

1.4*10^(-9)

9.0*10^(-10 )

CONCLUSION

The values of the function up to the second-order derivative at the nodal points are given.

Using these values, we constructed a quadrature formula with a derivative to calculate the exact

integral of the function. We found that the error functional corresponding to the difference

between this quadrature sum and the definite integral appears. The error functional is a


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168

multivariate function of the coefficients of the quadrature formula. We have constructed the

Lagrange function to find the conditional extremums of a multivariable function. To find the

minimum of this function in terms of coefficients, we got a system of linear algebraic equations

depending on the coefficients of the quadrature formula. We used the discrete analogue

4

4

/

d dx

to solve this system using the Sobolev method. Solving the system, we found the analytical view

of the coefficients. Using these coefficients, we calculated the norm of the error function. We

analyzed our theoretical results in numerical experiments.

REFERENCES

1.

Sobolev S.L. Introduction to the theory of cubature formulas. - M.: Nauka, 1974. - 808 p.

2.

Sobolev S.L., Vaskevich V.L. Cubature formulas. - Novosibirsk: Publishing House IM

SB RAS, 1996. - 484 p.

3.

Nikolsky S.M. Quadrature formulas. - M.: Nauka, 1988. - 256 p.

4.

Ramazanov M.D. Theory of lattice cubature formulas with a limited boundary layer. Ufa.

2009. 178 p.

5.

Muhammad Mujtaba Shaikh, Muhammad Saleem Chandio and Abdul Wasim Shaikh.

Some new time and cost efficient quadrature formulas to compute integrals using derivatives

with error analysis.

Symmetry

2022

, 14

(12)

,

2611;

https://doi.org/10.3390/sym14122611.

6.

Irina P., Tam P., Petr F . Quadrature rules for the

m

F

-transform polynomial components.

Axioms

2022 , 11(10 ), 501 ;

https://doi.org/10.3390/axioms11100501.

7.

Sanda M,. Iterative Numerical Methods for a Fredholm–Hammerstein Integral Equation

with Modified Argument.

Symmetry

2023 ,

15

(1), 66;

https://doi.org/10.3390/sym15010066.

8.

Ayman H., Rania S., Raed H., Mohammad W., Ahmad Q. A Perturbed Milne's

quadrature rule for n -times differentiable functions with

p

L

-error estimates.

Axioms

2023,

12

(9),

803;

https://doi.org/10.3390/axioms12090803.

9.

S Qo’ziyev, Methods, tools and forms of distance learning, Конференции.

10.

Shadimetov Kh., Nuraliev F., Kuziev Sh., Coefficients and errors of the optimal

quadrature formula of the Hermite type, AIP Conference Proceedings 3147 (1), 2024, pp. 1-12.

11.

S.S. Qo’ziyev, B.S. Tillaboyev, Talabalarda ijodkorlikni rivojlantirishda axborot

kommunikatsion texnologiyalarning o‘rni, Oriental renaissance: Innovative, educational, natural

and social sciences, 2021, pp. 344-352.

12.

F.A.Nuraliev, Sh.S.Kuziev, Optimal Quadrature Formulas with Derivative in the Space:

Optimal Quadrature Formulas with Derivative in the Space, Modern problems and prospects of

applied mathematics, 2024, 6(7), pp. 1-10.

13.

F.A.Nuraliev, Sh.S.Kuziev, The coefficients of an optimal quadrature formula in the

space of differentiable functions, Uzbek Mathematical Journal, 2023 4(1), pp. 127-138.

14.

Shadimetov Kh., Nuraliev F., Kuziev Sh., Optimal Quadrature Formula of Hermite Type

in the Space of Differentiable Functions, International Journal of Analysis and Applications,

2024, №25, pp. 1-19.

15.

Shadimetov Kh, Hayotov A., Nuraliev F. Construction of Optimal Interpolation

Formulas in the Sobolev Space. Journal of Mathematical Sciences (United States), 2022, 264(6),

pp 782-793.


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6.995, 2024 7.75

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169

16.

Sh.S.Kuziyev, Будушие учителя профессианалного образования планируют свою

исследовательскуюработу, Science, research, development, 2018, №12, pp. 76-82.

17.

Nuraliev F. Cuture formulas of Hermite type in the space of periodic functions of two

variables.

AIP Conference Proceedings , 2021, 2365, 020031.

18.

Kolmogorov A., Fomin S. Elements of the theory of functions and functional analysis. -

Moscow: Science, 1981. - 544 p.

19.

Shadimetov Kh. Discrete analogue of the operator

2

2

/

m

m

d

dx

and its construction //

Problems of Computational and Applied Mathematics - Tashkent, 1985. №79. pp. 22-35.

References

Sobolev S.L. Introduction to the theory of cubature formulas. - M.: Nauka, 1974. - 808 p.

Sobolev S.L., Vaskevich V.L. Cubature formulas. - Novosibirsk: Publishing House IM SB RAS, 1996. - 484 p.

Nikolsky S.M. Quadrature formulas. - M.: Nauka, 1988. - 256 p.

Ramazanov M.D. Theory of lattice cubature formulas with a limited boundary layer. Ufa. 2009. 178 p.

Muhammad Mujtaba Shaikh, Muhammad Saleem Chandio and Abdul Wasim Shaikh. Some new time and cost efficient quadrature formulas to compute integrals using derivatives with error analysis. Symmetry 2022, 14(12), 2611; https://doi.org/10.3390/sym14122611.

Irina P., Tam P., Petr F . Quadrature rules for the -transform polynomial components. Axioms 2022 , 11(10 ), 501 ; https://doi.org/10.3390/axioms11100501.

Sanda M,. Iterative Numerical Methods for a Fredholm–Hammerstein Integral Equation with Modified Argument. Symmetry 2023 , 15(1), 66; https://doi.org/10.3390/sym15010066.

Ayman H., Rania S., Raed H., Mohammad W., Ahmad Q. A Perturbed Milne's quadrature rule for n -times differentiable functions with -error estimates.Axioms 2023, 12 (9), 803; https://doi.org/10.3390/axioms12090803.

S Qo’ziyev, Methods, tools and forms of distance learning, Конференции.

Shadimetov Kh., Nuraliev F., Kuziev Sh., Coefficients and errors of the optimal quadrature formula of the Hermite type, AIP Conference Proceedings 3147 (1), 2024, pp. 1-12.

S.S. Qo’ziyev, B.S. Tillaboyev, Talabalarda ijodkorlikni rivojlantirishda axborot kommunikatsion texnologiyalarning o‘rni, Oriental renaissance: Innovative, educational, natural and social sciences, 2021, pp. 344-352.

F.A.Nuraliev, Sh.S.Kuziev, Optimal Quadrature Formulas with Derivative in the Space: Optimal Quadrature Formulas with Derivative in the Space, Modern problems and prospects of applied mathematics, 2024, 6(7), pp. 1-10.

F.A.Nuraliev, Sh.S.Kuziev, The coefficients of an optimal quadrature formula in the space of differentiable functions, Uzbek Mathematical Journal, 2023 4(1), pp. 127-138.

Shadimetov Kh., Nuraliev F., Kuziev Sh., Optimal Quadrature Formula of Hermite Type in the Space of Differentiable Functions, International Journal of Analysis and Applications, 2024, №25, pp. 1-19.

Shadimetov Kh, Hayotov A., Nuraliev F. Construction of Optimal Interpolation Formulas in the Sobolev Space. Journal of Mathematical Sciences (United States), 2022, 264(6), pp 782-793.