1 Introduction

Let w, l , c and c 0 be the linear spaces of all, bounded, convergent and null sequences x=( x k ) for all kN, respectively.

Let X and Y be two subsets of w. By (X,Y), we denote the class of all matrices of A such that A m (x)= k = 1 a m k x k converges for each mN, the set of all natural numbers, and the sequence Ax= ( A m ( x ) ) m = 1 Y for all xX.

Let A=( a m k ) be an infinite matrix of complex numbers. Then A is said to be regular if and only if it satisfies the following well-known Silverman-Toeplitz conditions:

  1. (1)

    sup m k = 1 | a m k |<,

  2. (2)

    lim m a m k =0 for each kN,

  3. (3)

    lim m k = 1 a m k =1.

The idea of statistical convergence was given by Zygmund [1] in 1935. The concept of statistical convergence was introduced by Fast [2] and Schoenberg [3] independently for the real sequences. Later on, it was further investigated from a sequence point of view and linked with the summability theory by Fridy [4] and many others. The natural density of a subset E of ℕ is denoted by

δ(E)= lim m 1 m |{kE:km}|,

where the vertical bar denotes the cardinality of the enclosed set.

Spaces of strongly summable sequences were studied by Kuttner [5], Maddox [6] and others. The class of sequences that are strongly Cesaro summable with respect to a modulus was introduced by Maddox [7] as an extension of the definition of strongly Cesaro summable sequences. Connor [8] has further extended this definition to a definition of strong A-summability with respect to a modulus, where A=( a m k ) is a non-negative regular matrix, and established some connections between strong A-summability with respect to a modulus and A-statistical convergence.

Assume now that A is a non-negative regular summability matrix. Then a sequence x=( x k ) is said to be A-statistically convergent to a number L if δ A (K)= lim m k = 1 a m k χ K (k)=0 or, equivalently, lim m k K a m k =0 for every ε>0, where K={kN:| x k L|ε} and χ K (k) is the characteristic function of K. We denote this limit by st A -limx=L [9] (see also [8, 10, 11]).

For A= C 1 , the Cesaro matrix, A-statistical convergence reduces to statistical convergence (see [2, 4]). Taking A=I, the identity matrix, A-statistical convergence coincides with ordinary convergence. We note that if A=( a m k ) is a regular summability matrix for which lim m max k | a m k |=0, then A-statistical convergence is stronger than usual convergence [10]. It should be also noted that the concept of A-statistical convergence may also be given in normed spaces [12].

The notion of difference sequence space was introduced by Kızmaz [13]. It was further generalized by Et and Çolak [14] as follows: Z( Δ μ )={x=( x k )w:( Δ μ x k )Z} for Z= l ,c and  c 0 , where μ is a non-negative integer, Δ μ x k = Δ μ 1 x k Δ μ 1 x k + 1 , Δ 0 x k = x k for all kN or equivalent to the following binomial representation:

Δ μ x k = v = 0 μ ( 1 ) v ( μ v ) x k + v .

These sequence spaces were generalized by Et and Başarır [15] taking Z= l (p),c(p)and  c 0 (p).

Dutta [16] introduced the following difference sequence spaces using a new difference operator: Z( Δ ( η ) )={x=( x k )w: Δ ( η ) xZ} for Z= l ,c and  c 0 , where Δ ( η ) x=( Δ ( η ) x k )=( x k x k η ) for all k,ηN.

In [17], Dutta introduced the sequence spaces c ¯ (,, Δ ( η ) μ ,p), c 0 ¯ (,, Δ ( η ) μ ,p), l (,, Δ ( η ) μ ,p), m(,, Δ ( η ) μ ,p) and m 0 (,, Δ ( η ) n ,p), where η, μN and Δ ( η ) μ x=( Δ ( η ) μ x k )=( Δ ( η ) μ 1 x k Δ ( η ) μ 1 x k η ) and Δ ( η ) 0 x k = x k for all k,ηN, which is equivalent to the following binomial representation:

Δ ( η ) μ x k = v = 0 μ ( 1 ) v ( μ v ) x k η v .

The difference sequence spaces have been studied by several authors, [1534]. Başar and Altay [35] introduced the generalized difference matrix B=( b m k ) for all k,mN, which is a generalization of Δ ( 1 ) -difference operator, by

b m k = { r , k = m , s , k = m 1 , 0 ( k > m )  or  ( 0 k < m 1 ) .

Başarır and Kayıkçı [36] defined the matrix B μ =( b m k μ ) which reduced the difference matrix Δ ( 1 ) μ in case r=1, s=1. The generalized B μ -difference operator is equivalent to the following binomial representation:

B μ x= B μ ( x k )= v = 0 μ ( μ v ) r μ v s v x k v .

Related articles can be found in [3541].

The concept of 2-normed space was initially introduced by Gähler [42] in the mid of 1960s, while that of n-normed spaces can be found in Misiak [43]. Since then, many others have used these concepts and obtained various results; see, for instance, Gunawan [44], Gunawan and Mashadi [45], Gunawan et al. [46] (see also [4754]).

2 Definitions and preliminaries

Let n be a non-negative integer and let X be a real vector space of dimension dn2. A real-valued function ,, on X n satisfies the following conditions:

  1. (1)

    x 1 ,, x n =0 if and only if x 1 ,, x n are linearly dependent,

  2. (2)

    x 1 ,, x n is invariant under permutation,

  3. (3)

    α x 1 ,, x n 1 , x n =|α| x 1 ,, x n 1 , x n for any αR,

  4. (4)

    x 1 ,, x n 1 ,y+z x 1 ,, x n 1 ,y+ x 1 ,, x n 1 ,z.

Then it is called an n-norm on X and the pair (X,,,) is called an n-normed space. A trivial example of an n-normed space is X= R n equipped with the following Euclidean n-norm: x 1 , , x n E =|det( x i j )|, where x i =( x i 1 ,, x i n ) R n for each i=1,,n. The standard n-norm on X, where X is a real inner product space of dimension dn, is defined as

x 1 , , x n S := | x 1 , x 1 x 1 , x n x n , x 1 x n , x n | 1 2 ,

where , denotes the inner product on X. If X= R n , then this n-norm is exactly the same as the Euclidean n-norm x 1 , , x n E as mentioned earlier. Notice that for n=1, the n-norm above is the usual norm x 1 S = x 1 , x 1 1 2 which gives the length of x 1 , while for n=2, it defines the standard 2-norm x 1 , x 2 S = ( x 1 S 2 . x 2 S 2 x 1 , x 1 2 ) 1 2 which represents the area of the parallelogram spanned by x 1 and x 2 . Further, if X= R 3 , then x 1 , x 2 , x 3 S = x 1 , x 2 , x 3 E represents the volume of the parallelograms spanned by x 1 , x 2 and x 3 . In general x 1 , , x n S represents the volume of the n-dimensional parallelepiped spanned by x 1 ,, x n in X.

A sequence ( x k ) in an n-normed space (X,,,) is said to converge to some LX in the n-norm if for each ε>0 there exists a positive integer n 0 = n 0 (ε) such that x k L, z 1 ,, z n 1 <ε for all k n 0 and for every z 1 ,, z n 1 X [45].

An Orlicz function is a function M:[0,)[0,) which is continuous, non-decreasing and convex with M(0)=0, M(x)>0 for x>0 and M(x) as x. It is well known that if M is a convex function, then M(αx)αM(x) with 0<α<1.

Let Λ=( Λ k ) be a sequence of nonzero scalars. Then, for a sequence space E, the multiplier sequence space E Λ , associated with the multiplier sequence Λ, is defined as

E Λ = { x = ( x k ) w : ( Λ k x k ) E } .

The following well-known inequality will be used throughout the paper. Let p=( p k ) be any sequence of positive real numbers with 0<h= inf k p k p k sup k p k =H, D=max{1, 2 H 1 }. Then we have, for all a k , b k C and for all kN,

| a k + b k | p k D ( | a k | p k + | b k | p k ) ,
(2.1)

and for aC, |a | p k max{ | a | h ,|a | H }.

In this paper, we introduce some new sequence spaces on a real n-normed space by using an infinite matrix, an Orlicz function and a generalized B Λ μ -difference operator. Further, we examine some topological properties of these sequence spaces. We also introduce a new concept which will be called ( B Λ μ ) n -statistical A-convergence in an n-normed space.

3 Main results

In this section, we give some new sequence spaces on a real n-normed space and investigate some topological properties of these spaces. We also give some inclusion relations.

Let A=( a m k ) be an infinite matrix of non-negative real numbers, let p=( p k ) be a bounded sequence of positive real numbers for all kN, and let Λ=( Λ k ) be a sequence of nonzero scalars. Further, let M be an Orlicz function and (X,,,) be an n-normed space. We denote the space of all X-valued sequence spaces by w(nX) and x=( x k )w(nX) by x=( x k ) for brevity. We define the following sequence spaces for every nonzero z 1 , z 2 ,, z n 1 X and for some ρ>0:

W ( A , B Λ μ , M , p , , , ) = { x = ( x k ) : lim m k = 1 a m k [ M ( B Λ μ x k L ρ , z 1 , , z n 1 ) ] p k = 0 for some  L X } , W 0 ( A , B Λ μ , M , p , , , ) = { x = ( x k ) : lim m k = 1 a m k [ M ( B Λ μ x k ρ , z 1 , , z n 1 ) ] p k = 0 } , W ( A , B Λ μ , M , p , , , ) = { x = ( x k ) : sup m k = 1 a m k [ M ( B Λ μ x k ρ , z 1 , , z n 1 ) ] p k < } ,

where and throughout the paper B Λ μ x k = v = 0 μ ( μ v ) r μ v s v x k v Λ k v and μ,kN. If we consider some special cases of the spaces above, the following are obtained:

  1. (1)

    If we take μ=0, then the spaces above are reduced to W(A,Λ,M,p,,,), W 0 (A,Λ,M,p,,,), W (A,Λ,M,p,,,), respectively.

  2. (2)

    If we take r=1, s=1, then we get the spaces W(A, Δ Λ μ ,M,p,,,), W 0 (A, Δ Λ μ ,M,p,,,), W (A, Δ Λ μ ,M,p,,,).

  3. (3)

    If M(x)=x, then the spaces above are denoted by W(A, B Λ μ ,p,,,), W 0 (A, B Λ μ ,p,,,), W (A, B Λ μ ,p,,,), respectively.

  4. (4)

    If p k =1 for all kN and Λ=( Λ k )=(1,1,1,), then the spaces above are reduced to the sequence spaces W(A, B μ ,M,,,), W 0 (A, B μ ,M,,,), W (A, B μ ,M,,,), respectively.

  5. (5)

    If M(x)=x and p k =1 for all kN, then the spaces above are denoted by W(A, B Λ μ ,,,), W 0 (A, B Λ μ ,,,), W (A, B Λ μ ,,,), respectively.

  6. (6)

    If we take A= C 1 , i.e., the Cesaro matrix, then the spaces above are reduced to the spaces W( B Λ μ ,M,p,,,), W 0 ( B Λ μ ,M,p,,,), W ( B Λ μ ,M,p,,,).

  7. (7)

    If we take A=( a m k ) is de la Vallee Poussin mean, i.e.,

    a m k = { 1 λ m , k I m = [ m λ m + 1 , m ] , 0 , otherwise ,
    (3.1)

where λ m is a non-decreasing sequence of positive numbers tending to ∞ and λ m + 1 λ m +1, λ 1 =1, then the spaces above are denoted by W(λ, B Λ μ ,M,p,,,), W 0 (λ, B Λ μ ,M,p,,,), W (λ, B Λ μ ,M,p,,,).

  1. (8)

    By a lacunary sequence θ=( k m ), m=0,1, , where k 0 =0, we mean an increasing sequence of non-negative integers with h m =( k m k m 1 ) as m. The intervals determined by θ are denoted by I m =( k m 1 , k m ]. Let

    a m k = { 1 h m , k m 1 < k k m , 0 , otherwise .
    (3.2)

Then we obtain the spaces W(θ, B Λ μ ,M,p,,,), W 0 (θ, B Λ μ ,M,p,,,) and W (θ, B Λ μ ,M,p,,,), respectively.

  1. (9)

    If we take A=I, where I is an identity matrix and p k =1 for all kN, then the spaces above are reduced to the sequence spaces c( B Λ μ ,M,,,), c 0 ( B Λ μ ,M,,,) and l ( B Λ μ ,M,,,), respectively.

  2. (10)

    If we take A=I, where I is an identity matrix, M(x)=x and p k =1 for all kN, then we denote the spaces above by the sequence spaces c( B Λ μ ,,,), c 0 ( B Λ μ ,,,) and l ( B Λ μ ,,,).

Theorem 3.1 W(A, B Λ μ ,M,p,,,), W 0 (A, B Λ μ ,M,p,,,) and W (A, B Λ μ ,M,p,,,) are linear spaces.

Proof We consider only W(A, B Λ μ ,M,p,,,). Others can be treated similarly. Let x,yW(A, B Λ μ ,M,p,,,) and α, β be scalars. Suppose that x L 1 and y L 2 . Then there exists |α| ρ 1 +|β| ρ 2 >0 such that

k = 1 a m k [ M ( B Λ μ ( α x k + β y k ) ( α L 1 + β L 2 ) | α | ρ 1 + | β | ρ 2 , z 1 , , z n 1 ) ] p k k = 1 a m k [ M ( | α | ρ 1 | α | ρ 1 + | β | ρ 2 B Λ μ x k L 1 ρ 1 , z 1 , , z n 1 + | β | ρ 2 | α | ρ 1 + | β | ρ 2 B Λ μ y k L 2 ρ 2 , z 1 , , z n 1 B Λ μ ) ] p k k = 1 a m k [ | α | ρ 1 | α | ρ 1 + | β | ρ 2 M ( B Λ μ x k L 1 ρ 1 , z 1 , , z n 1 ) + | β | ρ 2 | α | ρ 1 + | β | ρ 2 M ( B Λ μ y k L 2 ρ 2 , z 1 , , z n 1 ) ] p k D k = 1 a m k [ M ( B Λ μ x k L 1 ρ 1 , z 1 , , z n 1 ) ] p k + D k = 1 a m k [ M ( B Λ μ y k L 2 ρ 2 , z 1 , , z n 1 ) ] p k ,

which leads us, by taking limit as m, to the fact that we get (αx+βy)W(A, B Λ μ ,M,p,,,). □

Theorem 3.2 For any two sequences p=( p k ) and q=( q k ) of positive real numbers and for any two n-norms , , 1 , , , 2 on X, the following holds: Z(A, B Λ μ ,M,p, , , 1 )Z(A, B Λ μ ,M,q, , , 2 ), where Z=W, W 0 and W .

Proof Since the zero element belongs to each of the above classes of sequences, thus the intersection is non-empty. □

Theorem 3.3 Let A=( a m k ) be a non-negative matrix, and let p=( p k ) be a bounded sequence of positive real numbers. Then, for any fixed mN, the sequence space W (A, B Λ μ ,M,p,,,) is a paranormed space for every nonzero z 1 ,, z n 1 X and for some ρ>0 with respect to the paranorm defined by

g m (x)=inf { ρ p m H : ( k = 1 a m k [ M ( B Λ μ x k ρ , z 1 , , z n 1 ) ] p k ) 1 H < } .

Proof That g m (θ)=0 and g m (x)= g m (x) are easy to prove. So, we omit them. Let us take x=( x k ) and y=( y k ) in W (A, B Λ μ ,M,p,,,). Let

A ( x ) = { ρ > 0 : k = 1 a m k [ M ( B Λ μ x k ρ , z 1 , , z n 1 ) ] p k < } , A ( y ) = { ρ > 0 : k = 1 a m k [ M ( B Λ μ y k ρ , z 1 , , z n 1 ) ] p k < }

for every nonzero z 1 ,, z n 1 X. Let ρ 1 A(x) and ρ 2 A(y), then we have

( k = 1 a m k [ M ( B Λ μ ( x k + y k ) ( ρ 1 + ρ 2 ) , z 1 , , z n 1 ) ] p k ) 1 H <

by using Minkowski’s inequality for p=( p k )>1. Thus,

g m ( x + y ) = inf { ( ρ 1 + ρ 2 ) p m H : ρ 1 A ( x ) , ρ 2 A ( y ) } inf { ρ 1 p m H : ρ 1 A ( x ) } + inf { ρ 2 p m H : ρ 2 A ( y ) } = g m ( x ) + g m ( y ) .

We also get g m (x+y) g m (x)+ g m (y) for 0< p k 1 by using (2.1). Hence, we complete the proof of this condition of the paranorm. Finally, we show that the scalar multiplication is continuous. Whenever α0 and x is fixed imply g m (αx)0. Also, whenever xθ and α is any number imply g m (αx)0. By using the definition of the paranorm, for every nonzero z 1 ,, z n 1 X, we have

g m (αx)=inf { ρ p m H : ( k = 1 a m k [ M ( B Λ μ ( α x k ) ρ , z 1 , , z n 1 ) ] p k ) 1 H < } .

Then

g m (αx)=inf { ( α ϱ ) p m H : ( k = 1 a m k [ M ( B Λ μ x k ϱ , z 1 , , z n 1 ) ] p k ) 1 H < } ,

where ϱ= ρ α . Since |α | p k max{|α | h ,|α | H }, therefore |α | p k H ( max { | α | h , | α | H } ) 1 H . Then the required proof follows from the following inequality

g m ( α x ) ( max { | α | h , | α | H } ) 1 H inf { ϱ p m H : ( k = 1 a m k [ M ( B Λ μ x k ϱ , z 1 , , z n 1 ) ] p k ) 1 H < } = ( max { | α | h , | α | H } ) 1 H g m ( x ) .

 □

Theorem 3.4 Let M, M 1 , M 2 be Orlicz functions. Then the following hold:

  1. (1)

    Let 0<h p k 1. Then Z(A, B Λ μ ,M,p,,,)Z(A, B Λ μ ,M,,,), where Z=W, W 0 .

  2. (2)

    Let 1< p k H<. Then Z(A, B Λ μ ,M,,,)Z(A, B Λ μ ,M,p,,,), where Z=W, W 0 .

  3. (3)

    W 0 (A, B Λ μ , M 1 ,p,,,) W 0 (A, B Λ μ , M 2 ,p,,,) W 0 (A, B Λ μ , M 1 + M 2 ,p,,,).

Proof (1) We give the proof for the sequence space W 0 (A, B Λ μ ,M,p,,,) only. The other can be proved by a similar argument. Let ( x k ) W 0 (A, B Λ μ ,M,p,,,) and 0<h p k 1, then

k = 1 a m k [ M ( B Λ μ x k ρ , z 1 , , z n 1 ) ] k = 1 a m k [ M ( B Λ μ x k ρ , z 1 , , z n 1 ) ] p k .

Hence, we have the result by taking the limit as m. This completes the proof.

  1. (2)

    Let 1< p k H< and ( x k ) W 0 (A, B Λ μ ,M,,,). Then, for each 0<ε<1, there exists a positive integer M 0 such that

    k = 1 a m k [ M ( B Λ μ x k ρ , z 1 , , z n 1 ) ] <ε<1

for all m> M 0 . This implies that

k = 1 a m k [ M ( B Λ μ x k ρ , z 1 , , z n 1 ) ] p k k = 1 a m k [ M ( B Λ μ x k ρ , z 1 , , z n 1 ) ] .

Hence we have the result.

  1. (3)

    Let x=( x k ) W 0 (A, B Λ μ , M 1 ,p,,,) W 0 (A, B Λ μ , M 2 ,p,,,). Then, by the following inequality, the result follows

    k = 1 a m k [ ( M 1 + M 2 ) ( B Λ μ x k ρ , z 1 , , z n 1 ) ] p k D k = 1 a m k [ M 1 ( B Λ μ x k ρ , z 1 , , z n 1 ) ] p k + D k = 1 a m k [ M 2 ( B Λ μ x k ρ , z 1 , , z n 1 ) ] p k .

If we take the limit as m, then we get ( x k ) W 0 (A, B Λ μ , M 1 + M 2 ,p,,,). This completes the proof. □

Theorem 3.5 Z(A, B Λ μ 1 ,M,p,,,)Z(A, B Λ μ ,M,p,,,) and the inclusion is strict for μ1. In general, Z(A, B Λ j ,M,p, , , 1 )Z(A, B Λ μ ,M,p,,,) for j=0,1,2,,μ1 and the inclusions are strict, where Z=W, W 0 and W .

Proof We give the proof for W 0 (A, B Λ μ 1 ,M,p,,,) only. The others can be proved by a similar argument. Let x=( x k ) be any element in the space W 0 (A, B Λ μ 1 ,M,p,,,), then there exists ρ=|r| ρ 1 +|s| ρ 2 >0 such that

lim m k = 1 a m k [ M ( B Λ μ 1 x k ρ , z 1 , , z n 1 ) ] p k =0.

Since M is non-decreasing and convex, it follows that

k = 1 a m k [ M ( B Λ μ x k | r | ρ 1 + | s | ρ 2 , z 1 , , z n 1 ) ] p k = k = 1 a m k [ M ( r B Λ μ 1 x k + s B Λ μ 1 x k 1 | r | ρ 1 + | s | ρ 2 , z 1 , , z n 1 ) ] p k D k = 1 a m k [ M ( B Λ μ 1 x k ρ 1 , z 1 , , z n 1 ) ] p k + D k = 1 a m k [ M ( B Λ μ 1 x k 1 ρ 2 , z 1 , , z n 1 ) ] p k .

The result holds by taking the limit as m. □

In the following example we show that the inclusion given in the theorem above is strict.

Example 3.6 Let M(x)=x, p k =1 for all kN, Λ=( Λ k )=(1,1,), A= C 1 , i.e., the Cesaro matrix, r=1, s=1, where B Λ μ x k = v = 0 μ ( μ v ) r μ v s v x k v Λ k v for all r,sR{0}. Consider the sequence x=( x k )=( k μ 1 ). Then x=( x k ) belongs to W 0 ( B μ ,M,p,,,) but does not belong to W 0 ( B μ 2 ,M,p,,,).

Theorem 3.7 Let A=( a m k ) be a non-negative regular matrix and p=( p k ) be such that 0<h p k H<. Then

l ( B Λ μ , M , , , ) W ( A , B Λ μ , M , p , , , ) .

Proof Let l ( B Λ μ ,M,,,). Then there exists T 0 >0 such that [M( B Λ μ x k ρ , z 1 ,, z n 1 )] T 0 for all kN and for every nonzero z 1 ,, z n 1 X. Since A=( a m k ) is a non-negative regular matrix, we have the following inequality by (1) of Silverman-Toeplitz conditions:

sup m k = 1 a m k [ M ( B Λ μ x k ρ , z 1 , , z n 1 ) ] p k max { T 0 h , T 0 H } sup m k = 1 a m k <.

Hence l ( B Λ μ ,M,,,) W (A, B Λ μ ,M,p,,,). □

4 ( B Λ μ ) n -statistically A-convergent sequences

In this section we introduce and study a new concept of ( B Λ μ ) n -statistical A-convergence in an n-normed space as follows.

Definition 4.1 Let (X,,,) be an n-normed space and let A=( a m k ) be a non-negative regular matrix. A real sequence x=( x k ) is said to be ( B Λ μ ) n -statistically A-convergent to a number L if δ A ( B Λ μ ) n (K)= lim m k = 1 a m k χ K (k)=0 or, equivalently, lim m k K a m k =0 for each ε>0 and for every nonzero z 1 ,, z n 1 X, where K={kN: B Λ μ x k L, z 1 ,, z n 1 ε} and χ K is the characteristic function of K.

In this case, we write ( B Λ μ ) n stat-A-limx=L. S(A ( B Λ μ ) n ) denotes the set of all ( B Λ μ ) n -statistically A-convergent sequences.

If we consider some special cases of the matrix, then we have the following:

  1. (1)

    If A= C 1 , the Cesaro matrix, then the definition reduces to ( B Λ μ ) n -statistical convergence.

  2. (2)

    If A=( a m k ) is de la Vallee Poussin mean, which is given by (3.1), then the definition reduces to ( B Λ μ ) n -statistical λ-convergence.

  3. (3)

    If we take A=( a m k ) as in (3.2), then the definition reduces to ( B Λ μ ) n -statistical lacunary convergence.

Theorem 4.2 Let p=( p k ) be a sequence of non-negative bounded real numbers such that inf k p k >0. Then W(A, B Λ μ ,p,,,)S(A ( B Λ μ ) n ).

Proof Assume that x=( x k )W(A, B Λ μ ,p,,,). So, we have for every nonzero z 1 ,, z n 1 X

lim m k = 1 a m k B Λ μ x k L , z 1 , , z n 1 p k =0.

Let ε>0 and K={kN: B Λ μ x k L, z 1 ,, z n 1 ε}. We obtain the following:

k = 1 a m k B Λ μ x k L , z 1 , , z n 1 p k = k K a m k B Λ μ x k L , z 1 , , z n 1 p k + k K a m k B Λ μ x k L , z 1 , , z n 1 p k min { ε h , ε H } k K a m k .

If we take the limit as m, then we get xS(A ( B Λ μ ) n ). This completes the proof. □

Theorem 4.3 Let p=( p k ) be a sequence of non-negative bounded real numbers such that inf k p k >0. Then

l ( B Λ μ , , , ) S ( A ( B Λ μ ) n ) W ( A , B Λ μ , p , , , ) .

Proof Suppose that x=( x k ) l ( B Λ μ ,,,)S(A ( B Λ μ ) n ). Then there exists an integer T such that B Λ μ x k L, z 1 ,, z n 1 T for all k>0 and for every nonzero z 1 ,, z n 1 X, and lim m k K a m k =0, where K={kN: B Λ μ x k L, z 1 ,, z n 1 ε}. Then we can write

k = 1 a m k B Λ μ x k L , z 1 , , z n 1 p k = k K a m k B Λ μ x k L , z 1 , , z n 1 p k + k K a m k B Λ μ x k L , z 1 , , z n 1 p k < max { ε h , ε H } k K a m k + max { T h , T H } k K a m k .

Since A=( a m k ) is a non-negative regular matrix, then we have

1 = lim m k = 1 a m k = lim m k K a m k + lim m k K a m k .

Hence, lim m k K a m k =1. Thus

lim m k = 1 a m k B Λ μ x k L , z 1 , , z n 1 p k < ε lim m k K a m k + T lim m k K a m k < ε ,

where max{ ε h , ε H }= ε and max{ T h , T H }= T .

Hence, x k W(A, B Λ μ ,p,,,). □