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Article

Grüss-Type Inequalities for Vector-Valued Functions

by
Mohammad W. Alomari
1,
Christophe Chesneau
2 and
Víctor Leiva
3,*
1
Department of Mathematics, Faculty of Science and Information Technology, Irbid National University, Irbid 21110, Jordan
2
Department of Mathematics, Université de Caen Basse-Normandie, F-14032 Caen, France
3
School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
*
Author to whom correspondence should be addressed.
Mathematics 2022, 10(9), 1535; https://doi.org/10.3390/math10091535
Submission received: 31 March 2022 / Revised: 24 April 2022 / Accepted: 30 April 2022 / Published: 3 May 2022

Abstract

:
Grüss-type inequalities have been widely studied and applied in different contexts. In this work, we provide and prove vectorial versions of Grüss-type inequalities involving vector-valued functions defined on R n for inner- and cross-products.

1. Introduction and Background

Grüss-type inequalities have been largely studied and applied to diverse frameworks. In [1], Grüss-type inequalities with multiple points for derivatives bounded by functions on time scales were analyzed. In [2], the Ostrowski–Grüss type inequality of the Chebyshev functional was analyzed and an application to the one-point integral formula was provided.
A number of authors have demonstrated some Grüss-type inequalities in one and several variables [3,4,5]. For more results on multivariate and multidimensional Grüss-type inequalities, we refer the reader to [6,7,8].
In 1935, Grüss [9] introduced his celebrated inequality, which reads
1 ( d c ) c d H t K t d t 1 ( d c ) c d H t d t 1 ( d c ) c d K t d t 1 4 R 2 R 1 S 2 S 1 .
The inequality presented in (1) is valid for integrable bounded functions H , K : [ c , d ] R , such that R 1 H ( t ) R 2 and S 1 K ( t ) S 2 , for all t [ c , d ] .
In [10], a Grüss-type inequality was proved for double integrals by means of
1 β 2 α 2 β 1 α 1 α 2 β 2 α 1 β 1 H t , s K t , s d t d s   1 β 2 α 2 β 1 α 1 α 2 β 2 α 1 β 1 H t , s d t d s 1 β 2 α 2 β 1 α 1 α 2 β 2 α 1 β 1 K ( t , s ) d t d s    4 U 1 V 1 β 1 α 1 λ 1 + δ 1 λ 1 + δ 1 + 1 λ 1 + δ 1 + 2 + U 1 V 2 β 1 α 1 λ 1 β 2 α 1 δ 2 λ 1 + 1 δ 2 + 1 λ 1 + 2 δ 2 + 2     + U 2 V 1 β 1 α 1 λ 2 β 2 α 2 δ 1 λ 2 + 1 δ 1 + 1 λ 2 + 2 δ 1 + 2 + U 2 V 2 β 2 α 2 λ 2 + δ 2 λ 2 + δ 2 + 1 λ 2 + δ 2 + 2 ,
where H , K : α 1 , β 1 × α 2 , β 2 R are two functions, with “×” representing here the standard Cartesian product, satisfying the conditions stated as
H p , q H s , t U 1 p s λ 1 + U 2 q t λ 2
and
K p , q K s , t V 1 p s δ 1 + V 2 q t δ 2 ,
with U 1 , U 2 > 0 , λ 1 , λ 2 ( 0 , 1 ] , V 1 , V 2 > 0 , and δ 1 , δ 2 ( 0 , 1 ] .
Next, we present some notations and preliminaries. For x , y R and each 1 n , we consider the subset E : = { ( s 1 , , s n ) : x s y , 1 n } R n .
Now, for a real interval [ x , y ] , with { 1 , , n } , we assume the partition
Q : = s 0 , s 1 , , s μ ,
where s 0 ( ) < s 1 ( ) < < s n ( ) , s 0 ( ) : = x , s μ ( ) : = y and Δ s r ( ) : = s r ( ) s r 1 ( ) , for all r { 1 , , μ } .
Let Q : = = 1 n Q be a partition of the n-dimensional interval [ x , y ] : = = 1 n x , y , where x : = ( x 1 , , x n ) and y : = ( y 1 , , y n ) . Then, we denote that
x y f s d s : = x n y n x 1 y 1 f s 1 , , s n d s 1 d s n ,
where s : = ( s 1 , , s n ) . Now, for j { 1 , , m } and { 1 , , n } , let
Δ h j s 1 , s 2 , , s n : = h j s 1 , s 2 , , s n h j s 1 1 , s 1 2 , , s 1 n .
Note that the function h j ( s 1 , , s n ) is said to be of bounded variation (in the Arzelà sense) [11,12], if there exists a positive number A j > 0 such that, for every partition on E , we have
= 1 n Δ h j s 1 , , s n A j , j { 1 , , m } .
Let h j be of bounded variation on E , and ( Q ) denote the sum: = 1 n | Δ h j ( s ( 1 ) , , s ( n ) ) | , corresponding to the partition Q of E . The number stated as
E h j : = sup Q : Q Q E
is called the total variation of h j on E , where Q E is the set for all partitions on E .
A vector-valued function of several variables h : E R m , with h : = ( h 1 , , h m ) , is said to be of bounded variation if there exists a positive number A > 0 , such that, for every partition on E , we obtain
= 1 n Δ h s 1 , , s n A .
Here, A : = max 1 j m A j and
Δ h s 1 , , s n : = h s 1 , , s n h s 1 1 , , s 1 n .
Let h be of bounded variation on E . In addition, let ( Q ) denote the sum:
i = 1 n Δ h ( s ( 1 ) , , s ( n ) ) ,
corresponding to the partition Q of E . Then, the number stated as
E h : = sup Q : Q Q E
is called the total variation of h on E .
A function h j : E R is said to be of the Hölder type; that is, for each n-tuple s : = ( s 1 , , s n ) and t : = ( t 1 , , t n ) in E , h j satisfies
h j s h j t = 1 n A s t r ,
for some A > 0 and r ( 0 , 1 ] .
In general, a vector-valued function of several variables h : E R m , with h : = ( h 1 , , h m ) , is said to be of the Hölder type if, for each n-tuple s : = ( s 1 , , s n ) and t : = ( t 1 , , t n ) in E , h satisfies that
h s h t = 1 n A s t r ,
if, and only if,
h j s h j t = 1 n A j s t r j , j { 1 , , m } ,
for some A j > 0 and r j ( 0 , 1 ] . In our case, we have
A : = max 1 j m A j , r : = max 1 j m r j , { 1 , , n } .
Consider vector-valued functions h , k : E R m . In this work, we study the Chebyshev functionals defined by
T d h , k : = 1 Δ x y h s · k s d s 1 Δ x y h s d s · 1 Δ x y k s d s
and
T c h , k : = 1 Δ x y h s × k s d s 1 Δ x y h s d s × 1 Δ x y k s d s ,
where T c h , k is well defined only when m = 3 . This case only is considered in the paper. Note that, here, “×” and “·” denote the cross- and dot-products, respectively. We use the notation Δ : = = 1 n y x .
The objective of this work is to prove vectorial versions of Grüss-type inequalities involving vector-valued functions defined on R n for inner- and cross-products. In Section 2, we provide the main result of this investigation presenting vector versions, whereas Section 3 introduces the sharp inequality. In Section 4, some concluding remarks are stated.

2. The Vector Versions

In this section, we present our first main result by the following theorem.
Theorem 1.
Let h , k : E R 3 be functions satisfying the conditions γ k ( s ) Γ and ϕ h ( s ) Φ , for all s E and for some real vectors γ , Γ , ϕ , Φ R 3 . Then, we have
T c h , k 1 4 sin θ Φ ϕ Γ γ 1 4 Φ ϕ Γ γ ,
where θ 0 , 2 π is the angle between the vectors
h s 1 Δ x y h t d t , k s 1 Δ x y k t d t .
Proof. 
We note that, for any two vectors p : = ( p 1 , p 2 , p 3 ) and q : = ( q 1 , q 2 , q 3 ) in R 3 , the cross-product is defined as
p × q : = e 1 e 2 e 3 p 1 p 2 p 3 q 1 q 2 q 3 ,
where | · | denotes the determinant of the 3 × 3 given matrix. Moreover, e 1 , e 2 , e 3 are the unit coordinate vectors in R 3 , which are defined by e 1 : = ( 1 , 0 , 0 ) , e 2 : = ( 0 , 1 , 0 ) , and e 3 : = ( 0 , 0 , 1 ) .
Let us recall the celebrated identity
p × q 2 = p 2 q 2 p · q 2 ,
for any vectors p , q R 3 . The positive area formed by a parallelogram with sides p and q can be understood as the length of the cross-product. A celebrated definition of the dot-product is well-known and stated as
p · q : = p q cos θ .
Thus, the identity presented in (9) can be written as
p × q 2 = p 2 q 2 1 cos 2 θ ,
which is exactly
p × q = p q sin θ .
Now, we observe that γ k ( s ) Γ if, and only if, γ j k j ( s ) Γ j , for each j { 1 , 2 , 3 } and for all s E , where γ = ( j = 1 3 γ j 2 ) 1 / 2 and Γ = ( j = 1 3 Γ j 2 ) 1 / 2 .
Note that the cross-product version of the Korkine identity for vector-valued functions of several variables states that
T c h , k = 1 Δ x y h s 1 Δ x y h t d t × k s 1 Δ x y k t d t d s .
Taking the Euclidean norm given in (11), employing the triangle inequality, and then considering the identity defined in (10), we get
T c h , k = 1 Δ x y h s 1 Δ x y h t d t × k s 1 Δ x y k t d t d s 1 Δ x y h s 1 Δ x y h t d t × k s 1 Δ x y k t d t d s = 1 Δ x y h s 1 Δ x y h t d t k s 1 Δ x y k t d t sin θ d s .
Now, we define
I : = 1 Δ x y k s 1 Δ x y k t d t 2 d s = 1 Δ x y j = 1 3 k j s 1 Δ x y k j t d t 2 d s .
Then, we get
Δ · I = x y j = 1 3 k j 2 s 2 k j s 1 Δ x y k j t d t + 1 Δ x y k j t d t 2 d s = x y j = 1 3 k j 2 s 2 k j s 1 Δ x y k j t d t + 1 Δ x y k j t d t 2 d s = j = 1 3 x y k j 2 s d s 2 x y k j s d s 1 Δ x y k j t d t + x y 1 Δ x y k j t d t 2 d s = j = 1 3 x y k j 2 s d s 2 Δ x y k j t d t 2 + = 1 n y x Δ 2 x y k j t d t 2 = j = 1 3 x y k j 2 s d s 1 Δ x y k j s d s 2 = x y k s 2 d s 1 Δ x y k s d s 2 .
In addition, we have
I j : = 1 Δ x y k j s 1 Δ x y k j t d t 2 d s , j { 1 , 2 , 3 } , = Γ j 1 Δ x y k j s d s 1 Δ x y k j s d s γ j 1 Δ x y Γ j k j s k j s γ j d s .
As γ j k j s Γ j , for all s E , hence
1 Δ x y Γ j k j s k j s γ j d s 0 ,
which implies that
I j Γ j 1 Δ x y k j s d s 1 Δ x y k j s d s γ j .
Using the most basic inequality stated as
Y Z Z X 1 4 Y X 2 ,
which is valid for all X , Y , Z R , we get
I j 1 4 Γ j γ j 2 .
However, since I : = j = 1 3 I j , from the Cauchy–Buniakowski–Schwarz integral inequality, it follows that
I 1 Δ x y k s 1 Δ x y k t d t d s 2 ,
which from (13) gives
x y k s 1 Δ x y k t d t d s 1 2 Δ Γ γ .
In a similar vein, we have
x y h s 1 Δ x y h t d t d s 1 2 Δ Φ ϕ .
Combining the inequalities established in (13), (14), and (15), we reach the required result. The last inequality holds, since sin θ 1 , and so the proof is complete. □
Theorem 2.
Let h , k : E R m be functions satisfying the conditions γ k ( s ) Γ and ϕ h ( s ) Φ , for all s E and for some real vectors γ , Γ , ϕ , Φ R m . Then, we obtain
T d ( h , k ) 1 4 Φ ϕ Γ γ cos θ 1 4 Φ ϕ Γ γ ,
where θ 0 , 2 π is the angle between the vectors
h s 1 Δ x y h t d t , k s 1 Δ x y k t d t .
Proof. 
The dot-product version of the Korkine identity for vector-valued functions of several variables states that
T d h , k : = 1 Δ x y h s 1 Δ x y h t d t · k s 1 Δ x y k t d t d s .
Taking the absolute value of the expression given in (17) and employing the triangle inequality, we get
T d h , k = 1 Δ x y h s 1 Δ x y h t d t · k s 1 Δ x y k t d t d s 1 Δ x y h s 1 Δ x y h t d t · k s 1 Δ x y k t d t d s = 1 Δ x y h s 1 Δ x y h t d t k s 1 Δ x y k t d t cos θ d s .
Now, we proceed as in the proof of Theorem 1, but for all j { 1 , , m } . Then, we have
x y k s 1 Δ x y k t d t d s 1 2 Δ Γ γ
and
x y h s 1 Δ x y h t d t d s 1 2 Δ Φ ϕ .
Combining the inequalities established in (18), (19), and (20), we obtain the required result. The last inequality holds since cos θ 1 , and so the proof is complete. □
Theorem 3.
Let h , k : E R 3 be such that h satisfies
h s h t = 1 n A s t r ,
where A > 0 , r ( 0 , 1 ] and there exist real vectors γ , Γ R 3 , such that γ k ( s ) Γ , for all s E . Then, we have
T c h , k 1 2 n + 1 sin θ Γ γ · i = 1 n A y x r .
Proof. 
A general Korkine identity for vector-valued functions of several variables states that
T c h , k : = 1 Δ x y h s 1 2 n d h d × k s 1 Δ x y k t d t d s ,
where the summation is over all d : = ( d 1 , , d n ) R n , such that d { x , y } , for 1 n .
Taking the Euclidean norm in the expression given in (22) and utilizing the triangle inequality, we get
T c h , k = 1 Δ x y h s 1 2 n d h d × k s 1 Δ x y k t d t d s 1 Δ x y h s 1 2 n d h d × k s 1 Δ x y k t d t d s = 1 Δ x y h s 1 2 n d h d k s 1 Δ x y k t d t sin θ d s .
From the proof of Theorem 1, we obtain
x y k s 1 Δ x y k t d t d s 1 2 Δ Γ γ .
Now, by assumption, we have
h s 1 2 n d h d 1 2 n d h s h d 1 2 n = 1 n A s d r .
Therefore, it follows that
sup s E h s 1 2 n d h d 1 2 n d sup s E h s h d 1 2 n = 1 n A sup s E s d r = 1 2 n = 1 n A y x r .
Combining the expressions formulated in (23), (24), and (25), we get the desired result established in (21). □
Theorem 4.
Let h , k : E R m be such that h satisfies
h s h t = 1 n A s t r ,
where A > 0 , r ( 0 , 1 ] and there exist real vectors γ , Γ R m , such that γ k ( s ) Γ , for all s E . Then, we get
T d h , k 1 2 n + 1 cos θ Γ γ · = 1 n A y x r .
Proof. 
A general Korkine identity for vector-valued functions of several variables states that
T d h , k : = 1 Δ x y h s 1 2 n d h d · k s 1 Δ x y k t d t d s ,
where the summation is over all d : = d 1 , , d n R n such that d { x , y } , for 1 n .
Taking the absolute value in the expression given in (27) and utilizing the triangle inequality, we get
T d h , k = 1 Δ x y h s 1 2 n d h d · k s 1 Δ x y k t d t d s 1 Δ x y h s 1 2 n d h d · k s 1 Δ x y k t d t d s = 1 Δ x y h s 1 2 n d h d k s 1 Δ x y k t d t cos θ d s .
From the proof of Theorem 2, we have
x y k s 1 Δ x y k t d t d s 1 2 Δ Γ γ .
Now, by assumption, we have
h s 1 2 n d h d 1 2 n d h s h d 1 2 n = 1 n A s d r .
Therefore, it follows that
sup s E h s 1 2 n d h d 1 2 n d sup s E h s h d 1 2 n = 1 n A sup s E s d r = 1 2 n = 1 n A y x r .
Combining the expressions formulated in (28), (29), and (30), we obtain the desired result established in (26). □

3. Sharp Inequalities

In this section, we consider some sharp bounds for both Chebyshev functionals stated in (6) and (7). The following theorem holds.
Theorem 5.
Let h , k : E R 3 be such that h is of bounded variation on E and k as in Theorem 1. Then, we have
T c h , k 1 2 n + 1 sin θ · Γ γ · E h 1 2 n + 1 Γ γ · E h ,
with the constant 1 / 2 n + 1 in the right-hand side of both inequalities being the best possible.
Proof. 
As in Theorem 1, note that
T c h , k sin θ sup s E h s 1 2 n d h d 1 Δ x y k s 1 Δ x y k t d t d s .
However, as there exist real vectors γ , Γ R 3 , such that γ k s Γ for all s E , then
x y k s 1 Δ x y k t d t d s 1 2 Δ Γ γ .
Since h is of bounded variation on E , we have that
sup s E h s 1 2 n d h d 1 2 n d sup s E h s h d 1 2 n E h .
Merging the above inequalities, we get the required result given in (31). To prove that the sharpness of the expression stated in (31) holds for a constant C > 0 , that is,
T c h , k C Γ γ · E h ,
consider the vector-valued functions h , k : E R 3 defined as h : = ( h , h , h ) and k : = ( k , k , k ) with
h ( t ) : = k ( t ) = = 1 n sign t x + y 2 .
Note that h : = ( h , h , h ) is of bounded variation on E . Furthermore, we have
E h = 2 n , Γ γ = 2 , x y h t d t = x y k t d t = 0 , x y h t k t d t = y x .
By using the expression given in (32), we get 1 / 2 n + 1 C , which proves that 1 / 2 n + 1 is the best possible. Therefore, the proof is complete. □
Theorem 6.
Let h , k : E R m be such that h is of bounded variation on E and k as in Theorem 1. Then, we have
T d h , k 1 2 n + 1 cos θ · Γ γ · E h 1 2 n + 1 Γ γ · E h ,
with the constant 1 / 2 n + 1 in the right-hand side of both inequalities being the best possible.
Proof. 
As in Theorem 2, note that
T d h , k cos θ · sup s E h s 1 2 n d h d 1 Δ x y k s 1 Δ x y k t d t d s .
However, as there exist real vectors γ , Γ R m , such that γ k s Γ for all s E , then
x y k s 1 Δ x y k t d t d s 1 2 Δ Γ γ .
Since h is of bounded variation on E , we have that
sup s E h s 1 2 n d h d 1 2 n d sup s E h s h d 1 2 n E h .
Merging the above inequalities, we get the required result given in (33). To prove that the sharpness of the expression stated in (33) holds for a constant C > 0 , that is,
T c h , k C Γ γ · E h ,
consider the vector-valued functions h , k : E R m defined as h : = ( h , , h ) and k : = ( k , , k ) with
h ( t ) : = k ( t ) = = 1 n sign t x + y 2 .
Note that h : = ( h , , h ) is of bounded variation on E . Furthermore, we have
E h = 2 n , Γ γ = 2 , x y h t d t = x y k t d t = 0 , x y h t k t d t = y x .
By using the expression given in (34), we get 1 / 2 n + 1 C , which proves that 1 / 2 n + 1 is the best possible. Therefore, the proof is complete. □
Theorem 7.
Let h , k : E C 3 be such that h is of bounded variation on E and k is a Lebesgue integrable function on E . Then, we get
T c h , k 1 2 n sin θ E h 1 Δ x y k s 1 Δ x y k t d t d s 1 2 n E h 1 Δ x y k s 1 Δ x y k t d t d s ,
with the constant 1 / 2 n in the right-hand side of both inequalities being the best possible.
Proof. 
As in Theorem 5, we note that
T c h , k sin θ sup s E h s 1 2 n d h d 1 Δ x y k s 1 Δ x y k t d t d s .
Owing to the fact that h is of bounded variation on E , we have
sup s E h s 1 2 n d h d 1 2 n d sup s E h s h d 1 2 n E h .
Merging the obtained inequalities, we reach the required result given in (35). To prove that the sharpness of the expression stated in (35) holds for the constant C 1 > 0 , that is,
T h , k C 1 E h 1 Δ x y k s 1 Δ x y k t d t d s ,
consider the functions h , k : E R 3 , with h : = ( h , h , h ) = k defined as
h ( t ) : = k ( t ) = = 1 n sign t x + y 2 .
Observe that h is of bounded variation on E and
E h = 2 n , x y k t d t = 0 , x y k t d t = x y h t k t d t = 1 2 2 n = 1 n y x 2 .
By using the expression given in (36), we get 1 / 2 n C 1 , which proves that 1 / 2 n is the best possible for the formula presented in (35), and so the proof is complete. □
Theorem 8.
Let h , k : E C m be such that h is of bounded variation on E and k is a Lebesgue integrable function on E . Then, we get
T d h , k 1 2 n cos θ E h 1 Δ x y k s 1 Δ x y k t d t d s 1 2 n E h 1 Δ x y k s 1 Δ x y k t d t d s ,
with the constant 1 / 2 n in the right-hand side of both inequalities being the best possible.
Proof. 
As in Theorem 6, we note that
T h , k cos θ · sup s E h s 1 2 n d h d 1 Δ x y k s 1 Δ x y k t d t d s .
Owing to the fact that h is of bounded variation on E , we have
sup s E h s 1 2 n d h d 1 2 n d sup s E h s h d 1 2 n E h .
Merging the obtained inequalities, we reach the required result given in (37). To prove that the sharpness of the expression stated in (37) holds for the constant C 1 > 0 , that is,
T h , k C 1 E h 1 Δ x y k s 1 Δ x y k t d t d s ,
consider the functions h , k : E R m , with h : = ( h , , h ) = k defined as
h ( t ) : = k ( t ) = = 1 n sign t x + y 2 .
Observe that h is of bounded variation on E and
E h = 2 n , x y k t d t = 0 , x y k t d t = x y h t k t d t = 1 2 2 n = 1 n y x 2 .
By using the expression given in (38), we get 1 / 2 n C 1 , which proves that 1 / 2 n is the best possible for the formula presented in (37), and so the proof is complete. □
The variance of the function h : E C m , which is square integrable on E by K d h , is defined as
K d h : = T d h , h ¯ 1 / 2 = 1 Δ x y h t 2 d t 1 Δ x y h t d t 2 1 / 2 = T c h , h ¯ 1 / 2 , ( in   this   case   we   assume   m = 3 ,   that   is , h C 3 ) = K c h ,
where h ¯ denotes the complex conjugate function of h .
Corollary 1.
Let h : E R m and g : E R 3 be two functions which are of bounded variation on E . Then, we have that
K d h 1 2 n cos θ E h 1 2 n E h
and
K c g 1 2 n sin θ E g 1 2 n E g ,
with the constant 1 / 2 n in the right-hand side of both inequalities being the best possible.
Proof. 
We prove (41). By applying Theorem 6 for k = h ¯ , we get
K d 2 h 1 2 n cos θ E h 1 Δ x y h s 1 Δ x y h t d t d s 1 2 n E h 1 Δ x y h s 1 Δ x y h t d t d s .
By the Cauchy–Buniakowski–Schwarz inequality, we have
1 Δ x y h s 1 Δ x y h t d t d s K d h .
By combining the expressions given in (43) and (44), we obtain the required result. Now, if we choose h : E R m , for h : = ( h , , h ) , with
h ( s ) : = = 1 n sign s x + y 2 ,
then we establish the sharpness of the constant 1 / 2 n whose details are avoided. The proof of (42) goes similarly by applying Theorem 5, and the details are omitted. □
When both vector-valued functions have a bounded variation, we can now state the following theorem.
Theorem 9.
Let h , k : E R m and f , g : E R 3 be of bounded variation on E . Then, we get that
T d h , k 1 2 2 n cos θ E h · E k 1 2 2 n E h · E k
and
T c f , g 1 2 2 n sin θ E f · E g 1 2 2 n E f · E g ,
with the best possible constant in the right-hand side of both inequalities being 1 / 2 2 n .
Proof. 
From Theorem 6 and Corollary 1, it follows that
T d h , k 1 2 n cos θ E h · 1 Δ x y k s 1 Δ x y k t d t d s 1 2 n cos θ E h · K d k 1 2 2 n cos θ E h · E k .
The case of equality is obtained in the expression given in (45) when h , k : E R m , for h : = ( h , , h ) = k , with
h ( s ) : = k ( s ) = = 1 n sign s x + y 2 .
The proof of (46) goes similarly to the previous case by applying Theorem 5 and Corollary 1, replacing K d k by K c g in the proof of (45), and the details are omitted. □

4. Concluding Remarks

In this work, we have proved vectorial versions of Grüss-type inequalities involving vector-valued functions defined on R n for inner- and cross-products. These results can be helpful for different purposes and applications.

Author Contributions

Formal analysis: M.W.A., C.C., V.L.; investigation: M.W.A., C.C.; writing—original draft: M.W.A., C.C.; writing—review and editing: V.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported partially by project grant “Fondecyt 1200525” (V.L.) from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science and Technology, Knowledge, and Innovation.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would also like to thank the Editors and three reviewers for their constructive comments which led to improvement in the presentation of the manuscript.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Alomari, M.W.; Chesneau, C.; Leiva, V. Grüss-Type Inequalities for Vector-Valued Functions. Mathematics 2022, 10, 1535. https://doi.org/10.3390/math10091535

AMA Style

Alomari MW, Chesneau C, Leiva V. Grüss-Type Inequalities for Vector-Valued Functions. Mathematics. 2022; 10(9):1535. https://doi.org/10.3390/math10091535

Chicago/Turabian Style

Alomari, Mohammad W., Christophe Chesneau, and Víctor Leiva. 2022. "Grüss-Type Inequalities for Vector-Valued Functions" Mathematics 10, no. 9: 1535. https://doi.org/10.3390/math10091535

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