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volume 7, issue 1, article 8, 2006.

Received 26 May, 2004;

accepted 10 November, 2005.

Communicated by:C.E.M. Pearce

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Journal of Inequalities in Pure and Applied Mathematics

SHARP ERROR BOUNDS FOR SOME

QUADRATURE FORMULAE AND APPLICATIONS

NENAD UJEVI ´C

Department of Mathematics University of Split

Teslina 12/III, 21000 Split CROATIA.

EMail:ujevic@pmfst.hr

c

2000Victoria University ISSN (electronic): 1443-5756 103-04

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Sharp Error Bounds for Some Quadrature Formulae and

Applications Nenad Ujevi´c

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J. Ineq. Pure and Appl. Math. 7(1) Art. 8, 2006

http://jipam.vu.edu.au

Abstract

In the article ”N. Ujevi´c, A generalization of the pre-Grüss inequality and applications to some quadrature formulae,J. Inequal. Pure Appl. Math.,3(2), Art.

13, 2002” error bounds for some quadrature formulae are established. Here we prove that all inequalities (error bounds) obtained in this article are sharp.

We also establish a new sharp averaged midpoint-trapezoid inequality and give applications in numerical integration.

2000 Mathematics Subject Classification:Primary 26D10, Secondary 41A55.

Key words: Sharp error bounds, Quadrature formulae, Numerical integration.

1. Introduction

In recent years a number of authors have considered error inequalities for some known and some new quadrature rules. For example, this topic is considered in [1] – [6] and [11] – [14].

In this paper we consider the midpoint, trapezoid and averaged midpoint- trapezoid quadrature rules. These rules are also considered in [12], where some new improved versions of the error inequalities for the mentioned rules are de- rived.

Here we first prove that all inequalities obtained in [12] are sharp. Second, we specially consider the averaged midpoint-trapezoid quadrature rule. In [6]

it is shown that the last mentioned rule has a better estimation of error than the well-known Simpson’s rule and in [13] it is shown that this rule is an optimal quadrature rule. We give a new sharp error bound for this rule. Finally, we give applications in numerical integration.

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2. Midpoint Inequality

Let I ⊂ R be a closed interval anda, b ∈ IntI, a < b.Let f : I → Rbe an absolutely continuous function whose derivative f⁰ ∈ L₂(a, b). We define the mapping

Φ(t) =

( t−^2a+b₃ , t∈ a,â+b₂ t−â+2b₃ , t∈ â+b₂ , b such thatΦ0(t) = Φ(t)/kΦk₂, where

kΦk²₂ = Z b

a

(Φ(t))²dt= (b−a)³ 36 . We have

Q(f;a, b) = Z b

a

Φ₀(t)f⁰(t)dt

= 2

√b−a

f(a) +f

a+b 2

+f(b)− 3 b−a

Z b a

f(t)dt

.

In [12] we can find the following midpoint inequality (2.1)

f

a+b 2

(b−a)− Z b

a

f(t)dt

≤ (b−a)^3/2 2√

3 C₁, where

(2.2) C₁ =

(

kf⁰k²₂− [f(b)−f(a)]²

b−a −[Q(f;a, b)]² )¹₂

.

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Proposition 2.1. The inequality (2.1) is sharp in the sense that the constant ¹

2√

cannot be replaced by a smaller one. 3

Proof. We first define the mapping

(2.3) f(t) =

( ₁

2t², t∈

0,¹₂

1

2t²−t+ ¹₂, t∈ ¹₂,1 and note thatf is a Lipschitzian function.

On the other hand, each Lipschitzian function is an absolutely continuous function [10, p. 227].

Let us now assume that the inequality (2.1) holds with a constantC > 0, i.e.

(2.4)

f

a+b 2

(b−a)− Z b

a

f(t)dt

≤C(b−a)^3/2C₁,

whereC₁is defined by (2.2). Choosinga= 0,b = 1andf defined by (2.3), we get

Z 1 0

f(t)dt = 1 24, f

1 2

= 1 8 such that the left-hand side of (2.4) becomes

(2.5) L.H.S.(2.4) = 1

12.

We also find thatC1 = ₂^√¹₃ such that the right-hand side of (2.4) becomes

(2.6) R.H.S.(2.4) = C

2√ 3. From (2.4) – (2.6) we getC ≥ ¹

2√

3, proving thatC = ¹

2√

3 is the best possible in (2.1).

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3. Trapezoid Inequality

Let I ⊂ R be a closed interval anda, b ∈ IntI, a < b.Let f : I → Rbe an absolutely continuous function whose derivative f⁰ ∈ L₂(a, b). We define the mapping

χ(t) =

( t− ^5a+b₆ , t∈ a,â+b₂ t− â+5b₆ , t∈ â+b₂ , b such thatχ₀(t) = χ(t)/kχk₂, where

kχk²₂ = Z b

a

(χ(t))²dt = (b−a)³ 36 . We have

P(f;a, b) = Z b

a

χ0(t)f⁰(t)dt

= 1

√b−a

f(a) + 4f

a+b 2

+f(b)− 6 b−a

Z b a

f(t)dt

.

In [12] we can find the following trapezoid inequality:

(3.1)

f(a) +f(b)

2 (b−a)− Z b

a

f(t)dt

≤ (b−a)^3/2 2√

3 C₂, where

(3.2) C₂ =

(

kf⁰k²₂− [f(b)−f(a)]²

b−a −[P(f;a, b)]² )¹₂

.

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2√

Proof. We define the mapping

(3.3) f(t) = 1

2t²− 1 2t.

It is obvious that f is an absolutely continuous function. Let us now assume that the inequality (3.1) holds with a constantC >0, i.e.

(3.4)

f(a) +f(b)

2 (b−a)− Z b

a

f(t)dt

≤C(b−a)^3/2C₂, whereC₂ is defined by (3.2).

Choosinga= 0,b= 1andf defined by (3.3), we get Z 1

0

f(t)dt= 1

12 and f(0) =f(1) = 0.

Thus, the left-hand side of (3.4) becomes

(3.5) L.H.S.(3.4) = 1

12. The right-hand side of (3.4) becomes

(3.6) R.H.S.(3.4) = C

2√ 3.

From (3.4) – (3.6) we get C ≥ ₂^√¹₃, proving that ₂^√¹₃ is the best possible in (3.1).

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4. Averaged Midpoint-Trapezoid Inequality

Let I ⊂ R be a closed interval and a, b ∈ IntI, a < b.Let f : I → R be an absolutely continuous function whose derivative f⁰ ∈ L₂(a, b). We now consider a simple quadrature rule of the form

(4.1) f(a) + 2f ^a+b₂

+f(b)

4 (b−a)−

Z b a

f(t)dt

= 1 2

f

a+b 2

+ f(a) +f(b) 2

(b−a)− Z b

a

f(t)dt=R(f).

It is not difficult to see that (4.1) is a convex combination of the midpoint quadrature rule and the trapezoid quadrature rule. In [6] it is shown that (4.1) has a better estimation of error than the well-known Simpson’s quadrature rule (when we estimate the error in terms of the first derivativef⁰of integrandf). In [12] the following inequality is proved

(4.2)

f(a) + 2f ^a+b₂

+f(b)

4 (b−a)−

Z b a

f(t)dt

≤ (b−a)^3/2 4√

3 C₃,

where (4.3) C₃ =

"

kf⁰k²₂− [f(b)−f(a)]² b−a

− 1 b−a

f(a)−2f

a+b 2

+f(b) 2#¹₂

.

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4√

Proof. We first define the mapping

(4.4) f(t) =

( ₁

2t²− ¹₄t, t∈ 0,¹₂

1

2t²− ³₄t+ ¹₄, t∈ ¹₂,1 and note thatf is a Lipschitzian function.

Let us now assume that the inequality (4.2) holds with a constantC > 0, i.e.

(4.5)

f(a) + 2f ^a+b₂

+f(b)

4 (b−a)−

Z b a

f(t)dt

≤C(b−a)^3/2C₃, whereC3is defined by (4.3). Choosinga= 0,b = 1andf defined by (4.4), we get

Z 1 0

f(t)dt =− 1

48, f(0) =f(1) =f 1

2

= 0

such that the left-hand side of (4.5) becomes

(4.6) L.H.S.(4.5) = 1

48. We also find thatC₃ = ¹

4√

3 such that the right-hand side of (4.5) becomes

(4.7) R.H.S.(4.5) = C

4√ 3. From (4.5) – (4.7) we getC ≥ ¹

4√

3, proving thatC = ¹

4√

3 is the best possible in (4.2).

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5. A Sharp Error Inequality

In [12] we can find the following inequality

(5.1) S(f, g)² ≤S(f, f)S(g, g), where

(5.2) S(f, g) = Z b

a

f(t)g(t)dt− 1 b−a

Z b a

f(t)dt Z b

a

g(t)dt

− 1 kΨk²

Z b a

f(t)Ψ(t)dt Z b

a

g(t)Ψ(t)dt

andΨsatisfies (5.3)

Z b a

Ψ(t)dt= 0,

while

kΨk² = Z b

a

Ψ²(t)dt.

In [14] we can find a variant of the following lemma.

Lemma 5.1. Letf ∈C¹[a, c],g ∈C¹[c, b]be such thatf(c) = g(c). Then

h(t) =

( f(t), t∈[a, c]

g(t), t∈[c, b]

is an absolutely continuous function.

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Theorem 5.2. Let f : [0,1] → Rbe an absolutely continuous function whose derivativef⁰ ∈L2(0,1). Then

(5.4)

Z 1 0

f(t)dt−1 4

f(0) + 2f 1

2

+f(1)

≤ 1 4√

3 s

kf⁰k²−2

f 1

2

−f(0) 2

−2

f(1)−f 1

2 2

.

The inequality (5.4) is sharp in the sense that the constant ¹

4√

3 cannot be re- placed by a smaller one.

Proof. We define the functions

(5.5) p(t) =

( t− ¹₄, t ∈ 0,¹₂ t− ³₄, t ∈₁

2,1 and

(5.6) Ψ(t) =

( t, t∈ 0,¹₂ t−1, t∈₁

2,1 . It is not difficult to verify that

(5.7)

Z 1 0

p(t)dt= Z 1

0

Ψ(t)dt = 0.

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We also have

(5.8) kpk² =

Z 1 0

p²(t)dt= 1 48,

(5.9) kΨk² =

Z 1 0

Ψ²(t)dt= 1 12,

(5.10)

Z 1 0

p(t)Ψ(t)dt= 1 48. From (5.1), (5.2) and (5.3) we get

(5.11)

Z 1 0

p(t)f⁰(t)dt− 1 kΨk²

Z 1 0

p(t)Ψ(t)dt Z 1

0

f⁰(t)Ψ(t)dt 2

≤

"

kpk²− 1 kΨk²

Z 1 0

p(t)Ψ(t)dt ²#

×

"

kf⁰k²− Z 1

0

f⁰(t)dt ²

− 1 kΨk²

Z 1 0

f⁰(t)Ψ(t)dt ²#

. Integrating by parts, we obtain

Z 1 0

p(t)f⁰(t)dt = Z ¹₂

0

t−1

4

f⁰(t)dt+ Z 1

1 2

t−3

4

f⁰(t)dt (5.12)

= 1 4

f(0) + 2f 1

2

+f(1)

− Z 1

0

f(t)dt

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and

Z 1 0

f⁰(t)Ψ(t)dt = Z ¹₂

0

tf⁰(t)dt+ Z 1

1 2

(t−1)f⁰(t)dt (5.13)

=f 1

2

− Z 1

0

f(t)dt.

We introduce the notations

(5.14) i=

Z 1 0

f(t)dt,

(5.15) q= 1

4

f(0) + 2f 1

2

+f(1)

.

From (5.11) – (5.15) and (5.8) – (5.10) it follows that (5.16)

(q−i)− 1 4

f

1 2

−i 2

≤ 1 64

"

kf⁰k²−[f(1)−f(0)]²−12

f 1

2

−i 2#

or

(5.17) i²−2qi+4

3q²+ 1

48[f(1)−f(0)]²

− kf⁰k²+ 16

f 1

2 2

−32f 1

2

q ≤0.

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If we now introduce the notations

(5.18) β =−2q,

(5.19) γ = 4

3q²+ 1

48[f(1)−f(0)]²− kf⁰k²+ 16

f 1

2 2

−32f 1

2

q then we have

(5.20) i² +βi+γ ≤0.

Thus,i∈[i1, i2], where i₁ = −β−p

β²−4γ

2 , i₂ = −β+p

β²−4γ

2 .

In other words,

−β 2 −

pβ² −4γ

2 ≤i≤ −β 2 +

pβ²−4γ 2 or

(5.21)

i+ β 2

≤

pβ²−4γ

2 .

We have

(5.22) β²−4γ = 1 12

"

kf⁰k²−2

f 1

2

−f(0) 2

−2

f(1)−f 1

2 2#

.

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From (5.21) and (5.22) we easily find that (5.4) holds.

We have to prove that (5.4) is sharp. For that purpose, we define the function

(5.23) f(t) =

( ₁

2t²− ¹₄t+₃₂¹, t∈ 0,¹₂

1

2t²− ³₄t+₃₂⁹, t∈₁

2,1 .

From Lemma5.1we see that the above function is absolutely continuous. If we substitute the above function in the left-hand side of (5.4) then we get

(5.24) L.H.S.(5.4) = 1

48.

If we substitute the above function in the right-hand side of (5.4) then we get

(5.25) R.H.S.(5.4) = 1

48. From (5.24) and (5.25) we conclude that (5.4) is sharp.

Theorem 5.3. Let f : [a, b] → Rbe an absolutely continuous function whose derivativef⁰ ∈L₂(a, b). Then

(5.26)

Z b a

f(t)dt−b−a 4

f(a) + 2f

a+b 2

+f(b)

≤ (b−a)^3/2 4√

3 kf⁰k²− 2 b−a

f

a+b 2

−f(a) 2

− 2 b−a

f(b)−f

a+b 2

2!¹₂ .

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The above inequality is sharp in the sense that the constant1/(4√

3)cannot be replaced by a smaller one.

Remark 1. We have better estimates than (5.26). For example, we have the inequality

(5.27)

b−a 4

f(a) + 2f

a+b 2

+f(b)

− Z b

a

f(t)dt

≤ 1

8kf⁰k_∞(b−a)². However, note that the estimate (5.27) can be applied only iff⁰is bounded. On the other hand, the estimate (5.26) can be applied for absolutely continuous functions iff⁰ ∈L₂(a, b).

There are many examples where we cannot apply the estimate (5.27) but we can apply (5.26).

Example 5.1. Let us consider the integralR1 0

√3

sint²dt.We have

f(t) = √³

sint² and f⁰(t) = 2tcost² 3³

√ sin²t²

such that f⁰(t)→ ∞, t →0and we cannot apply the estimate (5.27). On the other hand, we have

Z 1 0

[f⁰(t)]²dt ≤ 4 9max

t∈[0,1]

t²cost² sint²

Z 1 0

dt

√3

sint² ≤ 16 9 , i.e. kf⁰k₂ ≤ ⁴₃ and we can apply the estimate (5.26).

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6. Applications in Numerical Integration

Let π = {x₀ =a < x₁ <· · ·< x_n =b}be a given subdivision of the interval [a, b]such thathi =xi+1−xi =h= (b−a)/n. We define

(6.1) σ_n(f) =

n−1

X

i=0

b−a

n kf⁰k²₂−(f(x_i+1)−f(x_i))²

−

f(x_i)−2f

x_i+x_i+1 2

+f(x_i+1) 2#¹₂

,

(6.2) η_n(f) =

n−1

X

i=0

"

b−a

n kf⁰k²₂−2

f

x_i+x_i+1 2

−f(x_i) 2

− 2

f(x_i+1)−f

x_i+x_i+1 2

2#¹₂

and

(6.3) ω_n(f) =

(b−a)kf⁰k²₂− 1

n(f(b)−f(a))² ¹₂

.

Theorem 6.1. Let π be a given subdivision of the interval [a, b] and let the

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assumptions of Theorem5.2hold. Then

(6.4)

Z b a

f(t)dt− h 4

n−1

X

i=0

f(x_i) + 2f

x_i+x_i+1 2

+f(x_i+1)

≤ b−a 4√

3nσ_n(f)≤ b−a 4√

3nω_n(f),

whereσ_n(f)andω_n(f)are defined by (6.1) and (6.3), respectively.

Proof. We have

(6.5) h 4

f(x_i) + 2f

x_i+x_i+1 2

+f(x_i+1)

− Z xi+1

xi

f(t)dt

= Z xi+1

xi

K_i(t)f⁰(t)dt,

where

Ki(t) =







t− ^3xⁱ^+x₄ⁱ⁺¹, t∈

x_i,^xⁱ^+x₂ⁱ⁺¹

t− ^xⁱ^+3x₄ⁱ⁺¹, t∈ ^xⁱ^+x₂ⁱ⁺¹, x_i+1 . From Proposition4.1we obtain

h 4

f(x_i) + 2f

xi+xi+1

2

+f(x_i+1)

− Z xi+1

xi

f(t)dt

≤ h^3/2 4√

3

hkf⁰k²₂− 1

h(f(x_i+1)−f(x_i))²

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− 1 h

f(x_i)−2f

x_i+x_i+1 2

+f(x_i+1) 2#¹₂

. If we sum (6.5) overifrom0to n−1and apply the above inequality then we get

Z b a

f(t)dt− h 4

n−1

X

i=0

f(x_i) + 2f

x_i+x_i+1 2

+f(x_i+1)

≤ h^3/2 4√

3

"_n−1 X

i=0

kf⁰k²₂− 1

h(f(xi+1)−f(xi))²

− 1 h

f(x_i)−2f

x_i+x_i+1 2

+f(x_i+1) 2#¹₂

. From the above relation and the facth= (b−a)/nwe see that the first inequality in (6.4) holds.

Using the Cauchy inequality we have

n−1

X

i=0

kf⁰k²₂− 1

h(f(x_i+1)−f(x_i))² ¹₂ (6.6)

≤n

"

kf⁰k²₂− 1 b−a

n−1

X

i=0

(f(x_i+1)−f(x_i))²

#¹₂

≤n

kf⁰k²₂− 1 b−a

1

n(f(b)−f(a))² ¹₂

.

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Since

kf⁰k²₂− 1

h(f(x_i+1)−f(x_i))²− 1 h

f(x_i)−2f

x_i +x_i+1 2

+f(x_i+1) 2

≤ kf⁰k²₂ − 1

h(f(x_i+1)−f(x_i))², we easily conclude that the second inequality in (6.4) holds, too.

Remark 2. The second inequality in (6.4) is coarser than the first inequality. It may be used to predict the number of steps needed in the compound rule for a given accuracy of the approximation. Of course, we shall use the first inequality in (6.4) to obtain the error bound. Note also that in this last case we use the same values f(x_i)to calculate the approximation of the integralRb

a f(t)dt and to obtain the error bound and recall that function evaluations are generally considered the computationally most expensive part of quadrature algorithms.

Theorem 6.2. Under the assumptions of Theorem6.1we have

Z b a

f(t)dt− h 4

n−1

X

i=0

f(x_i) + 2f

x_i+x_i+1 2

+f(x_i+1)

≤ b−a 4√

3nη_n(f)≤ b−a 4√

3nω_n(f), whereη_n(f)is defined by (6.2).

Proof. The proof of this theorem is similar to the proof of Theorem 6.1. Here we use Theorem5.3.

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References

[1] G.A. ANASTASSIOU, Ostrowski type inequalities, Proc. Amer. Math.

Soc., 123(12) (1995), 3775–3781.

[2] P. CERONE, Three points rules in numerical integration, Nonlinear Anal.- Theory Methods Appl., 47(4) (2001), 2341–2352.

[3] P. CERONEANDS.S. DRAGOMIR, Midpoint-type rules from an inequal- ities point of view, Handbook of Analytic-Computational Methods in Ap- plied Mathematics, Ed.: G. Anastassiou, CRC Press, New York, (2000), 135–200.

[4] P. CERONE AND S.S. DRAGOMIR, Trapezoidal-type rules from an in- equalities point of view, Handbook of Analytic-Computational Methods in Applied Mathematics, Ed.: G. Anastassiou, CRC Press, New York, (2000), 65–134.

[5] D. CRUZ-URIBE AND C.J. NEUGEBAUER, Sharp error bounds for the trapezoidal rule and Simpson’s rule, J. Inequal. Pure Appl. Math., 3(4) (2002), Art. 49. [ONLINE:http://jipam.vu.edu.au/article.

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[7] A. GHIZZETTI AND A. OSSICINI, Quadrature Formulae, Birkhäuser Verlag, Basel/Stuttgart, 1970.

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[8] A.R. KROMMER AND C.W. UEBERHUBER, Computational Integra- tion, SIAM, Philadelphia, 1998.

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