Introduction
Let X \mathcal{X} X be a normed linear space (NLS) over scalar field. Definition of the norm has the triangle inequality
∥ x + y ∥ ≤ ∥ x ∥ + ∥ y ∥ , ∀ x , y ∈ X . (1) \lVert x+y\rVert\leq\lVert x\rVert+\lVert y\rVert,\quad\forall x,y\in\mathcal{X}. \tag*{(1)} ∥ x + y ∥ ≤ ∥ x ∥ + ∥ y ∥ , ∀ x , y ∈ X . (1)
In 1992, Hudzik and Landes derived a breakthrough generalization of Inequality (1) which is valid for any two nonzero elements in a NLS [2 ] .
Theorem 1.1. [2 ] (Hudzik-Landes Inequality ) Let X \mathcal{X} X be a NLS. Then for all x , y ∈ X ∖ { 0 } x,y\in\mathcal{X}\setminus\{0\} x , y ∈ X ∖ { 0 } ,
∥ x + y ∥ ≤ ∥ x ∥ + ∥ y ∥ − ( 2 − ∥ x ∥ x ∥ + y ∥ y ∥ ∥ ) min { ∥ x ∥ , ∥ y ∥ } . (2) \lVert x+y\rVert\leq\lVert x\rVert+\lVert y\rVert-\left(2-\left\lVert\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right\rVert\right)\min\{\lVert x\rVert,\lVert y\rVert\}. \tag*{(2)} ∥ x + y ∥ ≤ ∥ x ∥ + ∥ y ∥ − ( 2 − ∥ x ∥ x + ∥ y ∥ y ) min {∥ x ∥ , ∥ y ∥} . (2)
In 2006, Maligranda independently derived Inequality (2) [4 ] . It is natural to look for a generalization of Inequality (2) to more than two non-zero vectors. This is done independently by Dragomir in 2006 [1 ] and by Kato, Saito and Tamura in 2007 [3 ] .
Theorem 1.2. [1 , 3 ] (Hudzik-Landes-Dragomir-Kato-Saito-Tamura Inequality ) Let X \mathcal{X} X be a NLS and n ∈ N n\in\mathbb{N} n ∈ N . Then for all x 1 , … , x n ∈ X ∖ { 0 } x_1,\ldots,x_n\in\mathcal{X}\setminus\{0\} x 1 , … , x n ∈ X ∖ { 0 } , we have
∥ ∑ j = 1 n x j ∥ ≤ ∑ j = 1 n ∥ x j ∥ − ( n − ∥ ∑ j = 1 n x j ∥ x j ∥ ∥ ) min 1 ≤ k ≤ n ∥ x k ∥ . \left\lVert\sum_{j=1}^{n}x_j\right\rVert\leq\sum_{j=1}^{n}\lVert x_j\rVert-\left(n-\left\lVert\sum_{j=1}^{n}\frac{x_j}{\lVert x_j\rVert}\right\rVert\right)\min_{1\leq k\leq n}\lVert x_k\rVert. j = 1 ∑ n x j ≤ j = 1 ∑ n ∥ x j ∥ − ( n − j = 1 ∑ n ∥ x j ∥ x j ) 1 ≤ k ≤ n min ∥ x k ∥ .
It is natural and important to ask what are finite field versions of Theorems Theorems 1.1 and 1.2 ? We answer the question by deriving finite field version of Hudzik-Landes-Dragomir-Kato-Saito-Tamura Inequality (Theorem 2.9 ).
We begin by introducing the notion of sub-modulus field.
Definition 2.1. Let F F F be a (finite) field. A map ∣ ⋅ ∣ : F → [ 0 , ∞ ) |\cdot|: F \to[0,\infty) ∣ ⋅ ∣ : F → [ 0 , ∞ ) is said to be sub-modulus or sub-valued if the following conditions hold.
(i) If λ ∈ F \lambda\in F λ ∈ F is such that ∣ λ ∣ = 0 |\lambda| = 0 ∣ λ ∣ = 0 , then λ = 0 \lambda= 0 λ = 0 .
(ii) ∣ λ μ ∣ ≤ ∣ λ ∣ ∣ μ ∣ |\lambda\mu| \leq|\lambda||\mu| ∣ λ μ ∣ ≤ ∣ λ ∣∣ μ ∣ for all λ , μ ∈ F \lambda,\mu\in F λ , μ ∈ F .
(iii) ∣ λ + μ ∣ ≤ ∣ λ ∣ + ∣ μ ∣ |\lambda+\mu| \leq|\lambda|+|\mu| ∣ λ + μ ∣ ≤ ∣ λ ∣ + ∣ μ ∣ for all λ , μ ∈ F \lambda,\mu\in F λ , μ ∈ F .
In this case, we say that F F F is a sub-modulus or sub-valued field.
Example 2.2. Let p p p be a prime, let Z p : = { 0 , 1 , … , p − 1 } \mathbb{Z}_p := \{0,1,\ldots,p-1\} Z p := { 0 , 1 , … , p − 1 } be the standard field of integers modulo p p p . Given n ∈ Z p n \in\mathbb{Z}_p n ∈ Z p , we define
∣ n ∣ : = unique natural number a such that 0 ≤ a ≤ p − 1 and n ≡ a ( m o d p ) . |n| := \text{unique natural number }a\text{ such that }0 \leq a \leq p-1\text{ and }n \equiv a \pmod{p}. ∣ n ∣ := unique natural number a such that 0 ≤ a ≤ p − 1 and n ≡ a ( mod p ) .
We next introduce the notion of sub-normed linear space.
Definition 2.3. Let X \mathcal{X} X be a vector space over a sub-modulus field F F F . A map ∥ ⋅ ∥ : X → [ 0 , ∞ ) \|\cdot\|:\mathcal{X}\to[0,\infty) ∥ ⋅ ∥ : X → [ 0 , ∞ ) is said to be sub-norm if the following conditions hold.
(i) If x ∈ X x \in\mathcal{X} x ∈ X is such that ∥ x ∥ = 0 \|x\| = 0 ∥ x ∥ = 0 , then x = 0 x = 0 x = 0 .
(ii) ∥ λ x ∥ ≤ ∣ λ ∣ ∥ x ∥ \|\lambda x\| \leq|\lambda|\|x\| ∥ λ x ∥ ≤ ∣ λ ∣∥ x ∥ for all λ ∈ F \lambda\in F λ ∈ F , for all x ∈ X x \in\mathcal{X} x ∈ X .
(iii) ∥ x + y ∥ ≤ ∥ x ∥ + ∥ y ∥ \|x+y\| \leq\|x\|+\|y\| ∥ x + y ∥ ≤ ∥ x ∥ + ∥ y ∥ for all x , y ∈ X x,y \in\mathcal{X} x , y ∈ X .
In this case, we say X \mathcal{X} X is a sub-normed linear space.
Example 2.4. Let p ∈ [ 1 , ∞ ) p \in[1,\infty) p ∈ [ 1 , ∞ ) . Let F F F be a sub-modulus field. For d ∈ N d \in\mathbb{N} d ∈ N , let F d F^d F d be the standard vector space. We define
∥ ( a j ) j = 1 d ∥ p : = ( ∑ j = 1 d ∣ a j ∣ p ) 1 p , ∀ ( a j ) j = 1 d ∈ F d . \left\|(a_j)_{j=1}^{d}\right\|_p := \left(\sum_{j=1}^{d}|a_j|^p\right)^{\frac{1}{p}}, \qquad\forall(a_j)_{j=1}^{d}\in F^d. ( a j ) j = 1 d p := ( j = 1 ∑ d ∣ a j ∣ p ) p 1 , ∀ ( a j ) j = 1 d ∈ F d . Then ∥ ⋅ ∥ p \|\cdot\|_p ∥ ⋅ ∥ p is a sub-norm on F d F^d F d .
Example 2.5. p ∈ [ 1 , ∞ ) p \in[1,\infty) p ∈ [ 1 , ∞ ) . Let F F F be a sub-modulus field. Define c 00 p ( N , F ) : = { { a n } n = 1 ∞ : a n ∈ F , ∀ n ∈ N , a n ≠ 0 only for finitely many n } c_{00}^{p}(\mathbb{N},F):=\{\{a_n\}_{n=1}^{\infty}:a_n\in F,\ \forall n\in\mathbb{N},\ a_n\ne0\text{ only for finitely many }n\} c 00 p ( N , F ) := {{ a n } n = 1 ∞ : a n ∈ F , ∀ n ∈ N , a n = 0 only for finitely many n } .
We define
∥ { a n } n = 1 ∞ ∥ p : = ( ∑ n = 1 ∞ ∣ a n ∣ p ) 1 p , ∀ { a n } n = 1 ∞ ∈ c 00 p ( N , F ) . \left\|\{a_n\}_{n=1}^{\infty}\right\|_p := \left(\sum_{n=1}^{\infty}|a_n|^p\right)^{\frac{1}{p}}, \qquad\forall\{a_n\}_{n=1}^{\infty}\in c_{00}^{p}(\mathbb{N},F). ∥ { a n } n = 1 ∞ ∥ p := ( n = 1 ∑ ∞ ∣ a n ∣ p ) p 1 , ∀ { a n } n = 1 ∞ ∈ c 00 p ( N , F ) . Then ∥ ⋅ ∥ p \|\cdot\|_p ∥ ⋅ ∥ p is a sub-norm on c 00 p ( N , F ) c_{00}^{p}(\mathbb{N},F) c 00 p ( N , F ) .
Example 2.6. Let F F F be a sub-modulus field. Define c 00 ( N , F ) : = { { a n } n = 1 ∞ : a n ∈ F , ∀ n ∈ N , a n ≠ 0 only for finitely many n } c_{00}(\mathbb{N},F):=\{\{a_n\}_{n=1}^{\infty}:a_n\in F,\ \forall n\in\mathbb{N},\ a_n\ne0\text{ only for finitely many }n\} c 00 ( N , F ) := {{ a n } n = 1 ∞ : a n ∈ F , ∀ n ∈ N , a n = 0 only for finitely many n } .
We define
∥ { a n } n = 1 ∞ ∥ 00 : = max n ∈ N ∣ a n ∣ , ∀ { a n } n = 1 ∞ ∈ c 00 ( N , F ) . \left\|\{a_n\}_{n=1}^{\infty}\right\|_{00}:=\max_{n\in\mathbb{N}}|a_n|,\qquad\forall\{a_n\}_{n=1}^{\infty}\in c_{00}(\mathbb{N},F). ∥ { a n } n = 1 ∞ ∥ 00 := n ∈ N max ∣ a n ∣ , ∀ { a n } n = 1 ∞ ∈ c 00 ( N , F ) . Then ∥ ⋅ ∥ 00 \|\cdot\|_{00} ∥ ⋅ ∥ 00 is a sub-norm on c 00 ( N , F ) c_{00}(\mathbb{N},F) c 00 ( N , F ) .
We now derive finite field version of Inequality (2) .
Theorem 2.7. (Finite Field Hudzik-Landes Inequality ) Let X \mathcal{X} X be a sub-normed linear space over F \mathbb{F} F . Then for all x , y ∈ X ∖ { 0 } x,y \in\mathcal{X} \setminus\{0\} x , y ∈ X ∖ { 0 } with ∥ x ∥ , ∥ y ∥ ∈ F \lVert x\rVert,\lVert y\rVert\in\mathbb{F} ∥ x ∥ , ∥ y ∥ ∈ F it holds
∥ x + y ∥ ≤ min { ∥ x ∥ ( ∥ x ∥ x ∥ + y ∥ y ∥ ∥ + ∣ 1 ∥ x ∥ − 1 ∥ y ∥ ∣ ∥ y ∥ ) , ∥ y ∥ ( ∥ x ∥ x ∥ + y ∥ y ∥ ∥ + ∣ 1 ∥ y ∥ − 1 ∥ x ∥ ∣ ∥ x ∥ ) } . \lVert x+y\rVert\le\min\left\{\lVert x\rVert\left(\left\lVert\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right\rVert+\left\lvert\frac{1}{\lVert x\rVert}-\frac{1}{\lVert y\rVert}\right\rvert\lVert y\rVert\right),\lVert y\rVert\left(\left\lVert\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right\rVert+\left\lvert\frac{1}{\lVert y\rVert}-\frac{1}{\lVert x\rVert}\right\rvert\lVert x\rVert\right)\right\}. ∥ x + y ∥ ≤ min { ∥ x ∥ ( ∥ x ∥ x + ∥ y ∥ y + ∥ x ∥ 1 − ∥ y ∥ 1 ∥ y ∥ ) , ∥ y ∥ ( ∥ x ∥ x + ∥ y ∥ y + ∥ y ∥ 1 − ∥ x ∥ 1 ∥ x ∥ ) } .
Proof. Let x , y ∈ X ∖ { 0 } x,y \in\mathcal{X} \setminus\{0\} x , y ∈ X ∖ { 0 } with ∥ x ∥ , ∥ y ∥ ∈ F \lVert x\rVert,\lVert y\rVert\in\mathbb{F} ∥ x ∥ , ∥ y ∥ ∈ F . Then
∥ x + y ∥ = ∥ ∥ x ∥ ( x ∥ x ∥ + y ∥ y ∥ ) + ( 1 − ∥ x ∥ ∥ y ∥ ) y ∥ ≤ ∥ x ∥ ∥ x ∥ x ∥ + y ∥ y ∥ ∥ + ∥ ( 1 − ∥ x ∥ ∥ y ∥ ) y ∥ ≤ ∥ x ∥ ∥ x ∥ x ∥ + y ∥ y ∥ ∥ + ∣ 1 − ∥ x ∥ ∥ y ∥ ∣ ∥ y ∥ ≤ ∥ x ∥ ∥ x ∥ x ∥ + y ∥ y ∥ ∥ + ∥ x ∥ ∣ 1 ∥ x ∥ − 1 ∥ y ∥ ∣ ∥ y ∥ . \begin{aligned}
\lVert x+y\rVert&= \left\lVert\lVert x\rVert\left(\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right)+\left(1-\frac{\lVert x\rVert}{\lVert y\rVert}\right)y\right\rVert\\
&\le\lVert x\rVert\left\lVert\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right\rVert+\left\lVert\left(1-\frac{\lVert x\rVert}{\lVert y\rVert}\right)y\right\rVert\\
&\le\lVert x\rVert\left\lVert\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right\rVert+\left\lvert1-\frac{\lVert x\rVert}{\lVert y\rVert}\right\rvert\lVert y\rVert\\
&\le\lVert x\rVert\left\lVert\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right\rVert+\lVert x\rVert\left\lvert\frac{1}{\lVert x\rVert}-\frac{1}{\lVert y\rVert}\right\rvert\lVert y\rVert.
\end{aligned} ∥ x + y ∥ = ∥ x ∥ ( ∥ x ∥ x + ∥ y ∥ y ) + ( 1 − ∥ y ∥ ∥ x ∥ ) y ≤ ∥ x ∥ ∥ x ∥ x + ∥ y ∥ y + ( 1 − ∥ y ∥ ∥ x ∥ ) y ≤ ∥ x ∥ ∥ x ∥ x + ∥ y ∥ y + 1 − ∥ y ∥ ∥ x ∥ ∥ y ∥ ≤ ∥ x ∥ ∥ x ∥ x + ∥ y ∥ y + ∥ x ∥ ∥ x ∥ 1 − ∥ y ∥ 1 ∥ y ∥ .
and
∥ x + y ∥ = ∥ ( 1 − ∥ y ∥ ∥ x ∥ ) x + ∥ y ∥ ( x ∥ x ∥ + y ∥ y ∥ ) ∥ ≤ ∥ ( 1 − ∥ y ∥ ∥ x ∥ ) x ∥ + ∥ ∥ y ∥ ( x ∥ x ∥ + y ∥ y ∥ ) ∥ ≤ ∣ 1 − ∥ y ∥ ∥ x ∥ ∣ ∥ x ∥ + ∥ y ∥ ∥ x ∥ x ∥ + y ∥ y ∥ ∥ ≤ ∥ y ∥ ∣ 1 ∥ y ∥ − 1 ∥ x ∥ ∣ ∥ x ∥ + ∥ y ∥ ∥ x ∥ x ∥ + y ∥ y ∥ ∥ . \begin{aligned}
\lVert x+y\rVert&= \left\lVert\left(1-\frac{\lVert y\rVert}{\lVert x\rVert}\right)x+\lVert y\rVert\left(\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right)\right\rVert\\
&\le\left\lVert\left(1-\frac{\lVert y\rVert}{\lVert x\rVert}\right)x\right\rVert+\left\lVert\lVert y\rVert\left(\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right)\right\rVert\\
&\le\left\lvert1-\frac{\lVert y\rVert}{\lVert x\rVert}\right\rvert\lVert x\rVert+\lVert y\rVert\left\lVert\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right\rVert\\
&\le\lVert y\rVert\left\lvert\frac{1}{\lVert y\rVert}-\frac{1}{\lVert x\rVert}\right\rvert\lVert x\rVert+\lVert y\rVert\left\lVert\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right\rVert.
\end{aligned} ∥ x + y ∥ = ( 1 − ∥ x ∥ ∥ y ∥ ) x + ∥ y ∥ ( ∥ x ∥ x + ∥ y ∥ y ) ≤ ( 1 − ∥ x ∥ ∥ y ∥ ) x + ∥ y ∥ ( ∥ x ∥ x + ∥ y ∥ y ) ≤ 1 − ∥ x ∥ ∥ y ∥ ∥ x ∥ + ∥ y ∥ ∥ x ∥ x + ∥ y ∥ y ≤ ∥ y ∥ ∥ y ∥ 1 − ∥ x ∥ 1 ∥ x ∥ + ∥ y ∥ ∥ x ∥ x + ∥ y ∥ y .
Therefore
∥ x + y ∥ ≤ ∥ x ∥ ( ∥ x ∥ x ∥ + y ∥ y ∥ ∥ + ∣ 1 ∥ x ∥ − 1 ∥ y ∥ ∣ ∥ y ∥ ) (3) \lVert x+y\rVert\le\lVert x\rVert\left(\left\lVert\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right\rVert+\left\lvert\frac{1}{\lVert x\rVert}-\frac{1}{\lVert y\rVert}\right\rvert\lVert y\rVert\right) \tag*{(3)} ∥ x + y ∥ ≤ ∥ x ∥ ( ∥ x ∥ x + ∥ y ∥ y + ∥ x ∥ 1 − ∥ y ∥ 1 ∥ y ∥ ) (3)
and
∥ x + y ∥ ≤ ∥ y ∥ ( ∥ x ∥ x ∥ + y ∥ y ∥ ∥ + ∣ 1 ∥ y ∥ − 1 ∥ x ∥ ∣ ∥ x ∥ ) . (4) \lVert x+y\rVert\le\lVert y\rVert\left(\left\lVert\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right\rVert+\left\lvert\frac{1}{\lVert y\rVert}-\frac{1}{\lVert x\rVert}\right\rvert\lVert x\rVert\right). \tag*{(4)} ∥ x + y ∥ ≤ ∥ y ∥ ( ∥ x ∥ x + ∥ y ∥ y + ∥ y ∥ 1 − ∥ x ∥ 1 ∥ x ∥ ) . (4)
Inequalities (3) and (4) give
∥ x + y ∥ ≤ min { ∥ x ∥ ( ∥ x ∥ x ∥ + y ∥ y ∥ ∥ + ∣ 1 ∥ x ∥ − 1 ∥ y ∥ ∣ ∥ y ∥ ) , ∥ y ∥ ( ∥ x ∥ x ∥ + y ∥ y ∥ ∥ + ∣ 1 ∥ y ∥ − 1 ∥ x ∥ ∣ ∥ x ∥ ) } . \lVert x+y\rVert\le\min\left\{\lVert x\rVert\left(\left\lVert\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right\rVert+\left\lvert\frac{1}{\lVert x\rVert}-\frac{1}{\lVert y\rVert}\right\rvert\lVert y\rVert\right),\lVert y\rVert\left(\left\lVert\frac{x}{\lVert x\rVert}+\frac{y}{\lVert y\rVert}\right\rVert+\left\lvert\frac{1}{\lVert y\rVert}-\frac{1}{\lVert x\rVert}\right\rvert\lVert x\rVert\right)\right\}. ∥ x + y ∥ ≤ min { ∥ x ∥ ( ∥ x ∥ x + ∥ y ∥ y + ∥ x ∥ 1 − ∥ y ∥ 1 ∥ y ∥ ) , ∥ y ∥ ( ∥ x ∥ x + ∥ y ∥ y + ∥ y ∥ 1 − ∥ x ∥ 1 ∥ x ∥ ) } .
Note the additional assumption ∥ x ∥ , ∥ y ∥ ∈ F \lVert x\rVert,\lVert y\rVert\in\mathbb{F} ∥ x ∥ , ∥ y ∥ ∈ F in the previous theorem. The reason is that, since the norm is a real number, we generally do not have a guarantee that it belongs to the given sub-modulus field.
Example 2.8. Consider Z 3 \mathbb{Z}_3 Z 3 . Let x = 1 x=1 x = 1 , y = 2 y=2 y = 2 . Then ∣ x + y ∣ = ∣ 1 + 2 ∣ = ∣ 3 ∣ = 0 \lvert x+y\rvert=\lvert1+2\rvert=\lvert3\rvert=0 ∣ x + y ∣ = ∣ 1 + 2 ∣ = ∣ 3 ∣ = 0 and ∣ x ∣ + ∣ y ∣ = 1 + 2 = 3 \lvert x\rvert+\lvert y\rvert=1+2=3 ∣ x ∣ + ∣ y ∣ = 1 + 2 = 3 . We now find
∣ x ∣ ( ∣ x ∣ x ∣ + y ∣ y ∣ ∣ + ∣ 1 ∣ x ∣ − 1 ∣ y ∣ ∣ ∣ y ∣ ) = ∣ 1 ∣ ( ∣ 1 ∣ 1 ∣ + 2 ∣ 2 ∣ ∣ + ∣ 1 ∣ 1 ∣ − 1 ∣ 2 ∣ ∣ ∣ 2 ∣ ) = ∣ 1 ∣ ( ∣ 1 1 + 2 2 ∣ + ∣ 1 1 − 1 2 ∣ 2 ) = ∣ 1 + 1 ∣ + ∣ 1 − 2 ∣ 2 = ∣ 2 ∣ + ∣ 1 ∣ + ∣ 2 ∣ 2 = 2 + ∣ 2 ∣ 2 = 2 + 2 ⋅ 2 = 2 + 1 = 0 \begin{aligned}
\lvert x\rvert\left(\left\lvert\frac{x}{\lvert x\rvert}+\frac{y}{\lvert y\rvert}\right\rvert+\left\lvert\frac{1}{\lvert x\rvert}-\frac{1}{\lvert y\rvert}\right\rvert\lvert y\rvert\right)
&=\lvert1\rvert\left(\left\lvert\frac{1}{\lvert1\rvert}+\frac{2}{\lvert2\rvert}\right\rvert+\left\lvert\frac{1}{\lvert1\rvert}-\frac{1}{\lvert2\rvert}\right\rvert\lvert2\rvert\right)\\
&=\lvert1\rvert\left(\left\lvert\frac{1}{1}+\frac{2}{2}\right\rvert+\left\lvert\frac{1}{1}-\frac{1}{2}\right\rvert2\right)\\
&=\lvert1+1\rvert+\lvert1-2\rvert2\\
&=\lvert2\rvert+\lvert1\rvert+\lvert2\rvert2\\
&=2+\lvert2\rvert2\\
&=2+2\cdot2\\
&=2+1=0
\end{aligned} ∣ x ∣ ( ∣ x ∣ x + ∣ y ∣ y + ∣ x ∣ 1 − ∣ y ∣ 1 ∣ y ∣ ) = ∣ 1 ∣ ( ∣ 1 ∣ 1 + ∣ 2 ∣ 2 + ∣ 1 ∣ 1 − ∣ 2 ∣ 1 ∣ 2 ∣ ) = ∣ 1 ∣ ( 1 1 + 2 2 + 1 1 − 2 1 2 ) = ∣ 1 + 1 ∣ + ∣ 1 − 2 ∣ 2 = ∣ 2 ∣ + ∣ 1 ∣ + ∣ 2 ∣ 2 = 2 + ∣ 2 ∣ 2 = 2 + 2 ⋅ 2 = 2 + 1 = 0
and
∣ y ∣ ( ∣ x ∣ x ∣ + y ∣ y ∣ ∣ + ∣ 1 ∣ x ∣ − 1 ∣ x ∣ ∣ ∣ x ∣ ) = ∣ 2 ∣ ( ∣ 1 ∣ 1 ∣ + 2 ∣ 2 ∣ ∣ + ∣ 1 ∣ 2 ∣ − 1 ∣ 1 ∣ ∣ ∣ 1 ∣ ) = ∣ 2 ∣ ( ∣ 1 1 + 2 2 ∣ + ∣ 1 2 − 1 1 ∣ ) = 2 ( ∣ 1 + 1 ∣ + ∣ 2 − 1 ∣ ) = 2 ( ∣ 2 ∣ + ∣ 1 ∣ ) = 2 ( 2 + 1 ) = 0. \begin{aligned}
\lvert y\rvert\left(\left\lvert\frac{x}{\lvert x\rvert}+\frac{y}{\lvert y\rvert}\right\rvert+\left\lvert\frac{1}{\lvert x\rvert}-\frac{1}{\lvert x\rvert}\right\rvert\lvert x\rvert\right)
&=\lvert2\rvert\left(\left\lvert\frac{1}{\lvert1\rvert}+\frac{2}{\lvert2\rvert}\right\rvert+\left\lvert\frac{1}{\lvert2\rvert}-\frac{1}{\lvert1\rvert}\right\rvert\lvert1\rvert\right)\\
&=\lvert2\rvert\left(\left\lvert\frac{1}{1}+\frac{2}{2}\right\rvert+\left\lvert\frac{1}{2}-\frac{1}{1}\right\rvert\right)\\
&=2(\lvert1+1\rvert+\lvert2-1\rvert)\\
&=2(\lvert2\rvert+\lvert1\rvert)\\
&=2(2+1)=0.
\end{aligned} ∣ y ∣ ( ∣ x ∣ x + ∣ y ∣ y + ∣ x ∣ 1 − ∣ x ∣ 1 ∣ x ∣ ) = ∣ 2 ∣ ( ∣ 1 ∣ 1 + ∣ 2 ∣ 2 + ∣ 2 ∣ 1 − ∣ 1 ∣ 1 ∣ 1 ∣ ) = ∣ 2 ∣ ( 1 1 + 2 2 + 2 1 − 1 1 ) = 2 (∣ 1 + 1 ∣ + ∣ 2 − 1 ∣) = 2 (∣ 2 ∣ + ∣ 1 ∣) = 2 ( 2 + 1 ) = 0.
Therefore
0 = ∣ x + y ∣ = min { 0 , 0 } < ∣ x ∣ + ∣ y ∣ = 3. 0=\lvert x+y\rvert=\min\{0,0\}<\lvert x\rvert+\lvert y\rvert=3. 0 = ∣ x + y ∣ = min { 0 , 0 } < ∣ x ∣ + ∣ y ∣ = 3.
Now we derive finite field version of Theorem 1.2.
Theorem 2.9. (Finite Field Hudzik-Landes-Dragomir-Kato-Saito-Tamura Inequality ) Let X \mathcal{X} X be a sub-normed linear space over F \mathbb{F} F and n ∈ N n\in\mathbb{N} n ∈ N . Then for all x 1 , … , x n ∈ X ∖ { 0 } x_1,\ldots,x_n\in\mathcal{X}\setminus\{0\} x 1 , … , x n ∈ X ∖ { 0 } with ∥ x 1 ∥ , … , ∥ x n ∥ ∈ F \lVert x_1\rVert,\ldots,\lVert x_n\rVert\in\mathbb{F} ∥ x 1 ∥ , … , ∥ x n ∥ ∈ F it holds
∥ ∑ j = 1 n x j ∥ ≤ min 1 ≤ k ≤ n { ∥ x k ∥ ( ∥ ∑ j = 1 n x j ∥ x j ∥ ∥ + ∑ 1 ≤ j ≤ n j ≠ k ∣ 1 ∥ x k ∥ − 1 ∥ x j ∥ ∣ ∥ x j ∥ ) } ≤ min 1 ≤ k ≤ n { ∥ x k ∥ ( ∥ ∑ j = 1 n x j ∥ x j ∥ ∥ + ( n − 1 ) max 1 ≤ j ≤ n j ≠ k ∣ 1 ∥ x k ∥ − 1 ∥ x j ∥ ∣ ∥ x j ∥ ) } . \begin{aligned}
\left\lVert\sum_{j=1}^{n}x_j\right\rVert
&\leq\min_{1\leq k\leq n}\left\{\lVert x_k\rVert\left(\left\lVert\sum_{j=1}^{n}\frac{x_j}{\lVert x_j\rVert}\right\rVert+\sum_{\substack{1\leq j\leq n\\j\ne k}}\left\lvert\frac{1}{\lVert x_k\rVert}-\frac{1}{\lVert x_j\rVert}\right\rvert\lVert x_j\rVert\right)\right\}\\
&\leq\min_{1\leq k\leq n}\left\{\lVert x_k\rVert\left(\left\lVert\sum_{j=1}^{n}\frac{x_j}{\lVert x_j\rVert}\right\rVert+(n-1)\max_{\substack{1\leq j\leq n\\j\ne k}}\left\lvert\frac{1}{\lVert x_k\rVert}-\frac{1}{\lVert x_j\rVert}\right\rvert\lVert x_j\rVert\right)\right\}.
\end{aligned} j = 1 ∑ n x j ≤ 1 ≤ k ≤ n min ⎩ ⎨ ⎧ ∥ x k ∥ j = 1 ∑ n ∥ x j ∥ x j + 1 ≤ j ≤ n j = k ∑ ∥ x k ∥ 1 − ∥ x j ∥ 1 ∥ x j ∥ ⎭ ⎬ ⎫ ≤ 1 ≤ k ≤ n min ⎩ ⎨ ⎧ ∥ x k ∥ j = 1 ∑ n ∥ x j ∥ x j + ( n − 1 ) 1 ≤ j ≤ n j = k max ∥ x k ∥ 1 − ∥ x j ∥ 1 ∥ x j ∥ ⎭ ⎬ ⎫ .
Proof. Let x 1 , … , x n ∈ X ∖ { 0 } x_1,\ldots,x_n \in\mathcal{X} \setminus\{0\} x 1 , … , x n ∈ X ∖ { 0 } with ∥ x 1 ∥ , … , ∥ x n ∥ ∈ F \|x_1\|,\ldots,\|x_n\| \in\mathbb{F} ∥ x 1 ∥ , … , ∥ x n ∥ ∈ F . Let 1 ≤ k ≤ n 1 \le k \le n 1 ≤ k ≤ n be fixed. Then
∥ ∑ j = 1 n x j ∥ = ∥ ∑ j = 1 n ∥ x k ∥ ∥ x j ∥ x j + ∑ j = 1 n ( 1 − ∥ x k ∥ ∥ x j ∥ ) x j ∥ ≤ ∥ ∑ j = 1 n ∥ x k ∥ ∥ x j ∥ x j ∥ + ∥ ∑ j = 1 n ( 1 − ∥ x k ∥ ∥ x j ∥ ) x j ∥ ≤ ∥ ∑ j = 1 n ∥ x k ∥ ∥ x j ∥ x j ∥ + ∥ x k ∥ ∥ ∑ j = 1 n ( 1 ∥ x k ∥ − 1 ∥ x j ∥ ) x j ∥ ≤ ∥ x k ∥ ∥ ∑ j = 1 n x j ∥ x j ∥ ∥ + ∥ x k ∥ ∥ ∑ j = 1 n ( 1 ∥ x k ∥ − 1 ∥ x j ∥ ) x j ∥ = ∥ x k ∥ ( ∥ ∑ j = 1 n x j ∥ x j ∥ ∥ + ∥ ∑ j = 1 n ( 1 ∥ x k ∥ − 1 ∥ x j ∥ ) x j ∥ ) = ∥ x k ∥ ( ∥ ∑ j = 1 n x j ∥ x j ∥ ∥ + ∥ ∑ 1 ≤ j ≤ n , j ≠ k ( 1 ∥ x k ∥ − 1 ∥ x j ∥ ) x j ∥ ) ≤ ∥ x k ∥ ( ∥ ∑ j = 1 n x j ∥ x j ∥ ∥ + ∑ 1 ≤ j ≤ n , j ≠ k ∥ ( 1 ∥ x k ∥ − 1 ∥ x j ∥ ) x j ∥ ) ≤ ∥ x k ∥ ( ∥ ∑ j = 1 n x j ∥ x j ∥ ∥ + ∑ 1 ≤ j ≤ n , j ≠ k ∣ 1 ∥ x k ∥ − 1 ∥ x j ∥ ∣ ∥ x j ∥ ) . \begin{aligned}
\left\|\sum_{j=1}^{n} x_j\right\| &=
\left\|\sum_{j=1}^{n} \frac{\|x_k\|}{\|x_j\|}x_j+\sum_{j=1}^{n}\left(1-\frac{\|x_k\|}{\|x_j\|}\right)x_j\right\| \\
&\le\left\|\sum_{j=1}^{n} \frac{\|x_k\|}{\|x_j\|}x_j\right\|+\left\|\sum_{j=1}^{n}\left(1-\frac{\|x_k\|}{\|x_j\|}\right)x_j\right\| \\
&\le\left\|\sum_{j=1}^{n} \frac{\|x_k\|}{\|x_j\|}x_j\right\|+\|x_k\|\left\|\sum_{j=1}^{n}\left(\frac{1}{\|x_k\|}-\frac{1}{\|x_j\|}\right)x_j\right\| \\
&\le\|x_k\|\left\|\sum_{j=1}^{n}\frac{x_j}{\|x_j\|}\right\|+\|x_k\|\left\|\sum_{j=1}^{n}\left(\frac{1}{\|x_k\|}-\frac{1}{\|x_j\|}\right)x_j\right\| \\
&= \|x_k\|\left(\left\|\sum_{j=1}^{n}\frac{x_j}{\|x_j\|}\right\|+\left\|\sum_{j=1}^{n}\left(\frac{1}{\|x_k\|}-\frac{1}{\|x_j\|}\right)x_j\right\|\right) \\
&= \|x_k\|\left(\left\|\sum_{j=1}^{n}\frac{x_j}{\|x_j\|}\right\|+\left\|\sum_{\substack{1\le j\le n,j\ne k}}\left(\frac{1}{\|x_k\|}-\frac{1}{\|x_j\|}\right)x_j\right\|\right) \\
&\le\|x_k\|\left(\left\|\sum_{j=1}^{n}\frac{x_j}{\|x_j\|}\right\|+\sum_{\substack{1\le j\le n,j\ne k}}\left\|\left(\frac{1}{\|x_k\|}-\frac{1}{\|x_j\|}\right)x_j\right\|\right) \\
&\le\|x_k\|\left(\left\|\sum_{j=1}^{n}\frac{x_j}{\|x_j\|}\right\|+\sum_{\substack{1\le j\le n,j\ne k}}\left|\frac{1}{\|x_k\|}-\frac{1}{\|x_j\|}\right|\|x_j\|\right).
\end{aligned} j = 1 ∑ n x j = j = 1 ∑ n ∥ x j ∥ ∥ x k ∥ x j + j = 1 ∑ n ( 1 − ∥ x j ∥ ∥ x k ∥ ) x j ≤ j = 1 ∑ n ∥ x j ∥ ∥ x k ∥ x j + j = 1 ∑ n ( 1 − ∥ x j ∥ ∥ x k ∥ ) x j ≤ j = 1 ∑ n ∥ x j ∥ ∥ x k ∥ x j + ∥ x k ∥ j = 1 ∑ n ( ∥ x k ∥ 1 − ∥ x j ∥ 1 ) x j ≤ ∥ x k ∥ j = 1 ∑ n ∥ x j ∥ x j + ∥ x k ∥ j = 1 ∑ n ( ∥ x k ∥ 1 − ∥ x j ∥ 1 ) x j = ∥ x k ∥ ( j = 1 ∑ n ∥ x j ∥ x j + j = 1 ∑ n ( ∥ x k ∥ 1 − ∥ x j ∥ 1 ) x j ) = ∥ x k ∥ j = 1 ∑ n ∥ x j ∥ x j + 1 ≤ j ≤ n , j = k ∑ ( ∥ x k ∥ 1 − ∥ x j ∥ 1 ) x j ≤ ∥ x k ∥ j = 1 ∑ n ∥ x j ∥ x j + 1 ≤ j ≤ n , j = k ∑ ( ∥ x k ∥ 1 − ∥ x j ∥ 1 ) x j ≤ ∥ x k ∥ j = 1 ∑ n ∥ x j ∥ x j + 1 ≤ j ≤ n , j = k ∑ ∥ x k ∥ 1 − ∥ x j ∥ 1 ∥ x j ∥ .
By varying k k k and taking minimum in the right side of previous inequality gives
∥ ∑ j = 1 n x j ∥ ≤ min 1 ≤ k ≤ n { ∥ x k ∥ ( ∥ ∑ j = 1 n x j ∥ x j ∥ ∥ + ∑ 1 ≤ j ≤ n , j ≠ k ∣ 1 ∥ x k ∥ − 1 ∥ x j ∥ ∣ ∥ x j ∥ ) } ≤ min 1 ≤ k ≤ n { ∥ x k ∥ ( ∥ ∑ j = 1 n x j ∥ x j ∥ ∥ + ( n − 1 ) max 1 ≤ j ≤ n , j ≠ k ∣ 1 ∥ x k ∥ − 1 ∥ x j ∥ ∣ ∥ x j ∥ ) } . \begin{aligned}
\left\|\sum_{j=1}^{n}x_j\right\|
\le\min_{1\le k\le n}\left\{\|x_k\|\left(\left\|\sum_{j=1}^{n}\frac{x_j}{\|x_j\|}\right\|+\sum_{\substack{1\le j\le n,j\ne k}}\left|\frac{1}{\|x_k\|}-\frac{1}{\|x_j\|}\right|\|x_j\|\right)\right\} \\
&\le\min_{1\le k\le n}\left\{\|x_k\|\left(\left\|\sum_{j=1}^{n}\frac{x_j}{\|x_j\|}\right\|+(n-1)\max_{\substack{1\le j\le n,j\ne k}}\left|\frac{1}{\|x_k\|}-\frac{1}{\|x_j\|}\right|\|x_j\|\right)\right\}.
\end{aligned} j = 1 ∑ n x j ≤ 1 ≤ k ≤ n min ⎩ ⎨ ⎧ ∥ x k ∥ j = 1 ∑ n ∥ x j ∥ x j + 1 ≤ j ≤ n , j = k ∑ ∥ x k ∥ 1 − ∥ x j ∥ 1 ∥ x j ∥ ⎭ ⎬ ⎫ ≤ 1 ≤ k ≤ n min { ∥ x k ∥ ( j = 1 ∑ n ∥ x j ∥ x j + ( n − 1 ) 1 ≤ j ≤ n , j = k max ∥ x k ∥ 1 − ∥ x j ∥ 1 ∥ x j ∥ ) } .
Conclusions
In 1992, Hudzik and Landes improved triangle inequality for two nonzero elements in normed linear spaces [2 ] .
In 2006, Dragomir extended Hudzik-Landes inequality for more than two nonzero vectors [1 ] .
In 2007, Kato, Saito and Tamura extended Hudzik-Landes inequality without knowing the work of Dragomir [3 ] .
In this article, we derived finite field version of Hudzik-Landes-Dragomir-Kato-Saito-Tamura inequality.
References