# Statistical convergence in a paranormed space

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## Statistical convergence in a paranormed space

*

### and Abdullah M Alroqi

* Correspondence: aalotaibi@kau. edu.sa

Department of Mathematics, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia

Abstract

In this article, we define the notion of statistical convergence, statistical Cauchy and stronglyp-Cesàro summability in a paranormed space. We establish some relations between them.

AMS subject classification (2000): 41A10; 41A25; 41A36; 40A05; 40A30.

Keywords:density, statistical convergence, statistical Cauchy, para-normed space, stronglyp-Cesàro summability.

1 Introduction and preliminaries

The concept of statistical convergence for sequences of real numbers was introduced by Fast [1] and Steinhaus [2] independently in the same year 1951 and since then sev-eral gensev-eralizations and applications of this notion have been investigated by various authors, namely [3-11]. This notion was defined in normed spaces by Kolk [12] and in locally convex Hausdorff topological spaces by Maddox [13]. Çakalli [14] extended this notation to topological Hausdorff groups. Recently, in [15,16], the concept of statistical convergence is studied in probabilistic normed space and in intuitionistic fuzzy normed spaces, respectively. In this article, we shall study the concept of statistical convergence, statistical Cauchy, and stronglyp-Cesàro summability in a paranormed space.

Let Kbe a subset of the set of natural numbersN. Then theasymptotic densityofK denoted byδ(K), is defined asδ(K) = limn1n|{kn:kK}|, where the vertical bars denote the cardinality of the enclosed set.

A number sequencex= (xk) is said to bestatistically convergent to the numberLif for each> 0, the setK() = {k≤n: |xk-L| >} has asymptotic density zero, i.e.,

lim

n

1

n|{kn:|xkL|}|= 0.

In this case we writest-limx=L.

A number sequencex= (xk) is said to be statistically Cauchysequence if for every

> 0, there exists a numberN=N() such that

lim

n

1

n{jn:xjxNε}= 0.

The concept of paranorm is a generalization of absolute value (see [17]).

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A paranormis a functiong:X® ℝdefined on a linear space Xsuch that for allx, y, zÎ X

(P1)g(x) = 0 ifx=θ (P2)g(-x) =g(x)

(P3)g(x+y)≤g(x) +g(y)

(P4) If (an) is a sequence of scalars withan ® a0 (n® ∞) andxn, aÎ X with xn ® a (n ® ∞) in the sense that g(xn - a) ® 0 (n ® ∞), then anxn ® a0a

(n® ∞), in the sense thatg(anxn- a0a)® 0 (n ®∞).

A paranorm gfor whichg(x) = 0 impliesx=θis called atotal paranorm onX, and the pair (X, g) is called a total paranormed space.

Note that each seminorm (norm)ponXis a paranorm (total) but converse need not be true.

In this article, we define and study the notion of convergence, statistical convergence, statistical Cauchy, and strong summability by a modulus function in a paranormed space.

Let (X, g) be a paranormed space.

Definition 1.1. A sequencex= (xk) is said to beconvergent(org-convergent) to the numberξin (X, g) if for everyε> 0, there exists a positive integerk0such thatg(xk-ξ) <εwheneverk≥k0. In this we writeg-limx=ξ, andξis called theg-limitofx.

Definition 1.2. A sequencex= (xk) is said to bestatistically convergent to the num-berξ in(X, g) (org(st)-convergent) if for each> 0,

lim

n

1

n{kn:g(xkξ)> ε}= 0.

In this case, we write g(st)-lim x = ξ. We denote the set of all g(st)-convergent sequences by Sg.

Definition 1.3. A number sequence x = (xk) is said to be statistically Cauchy sequence in (X, g) (org(st)-Cauchy) if for every> 0 there exists a number N=N() such that

lim

n

1

n{jn:g(xjxN)≥ε}= 0.

2 Main results

Theorem 2.1. If a sequence x= (xk) is statistically convergent in (X, g) then g(st)-limit is unique.

Proof. Suppose that g(st)-lim x= ξ1 andg(st)-limx =ξ2. Givenε> 0, define the

following sets as:

K1(ε) ={nN:g(xnξ1)≥ε/2}, K2(ε) ={nN:g(xnξ2)≥ε/2}.

Sinceg(st)-lim x=ξ1, we haveδ(K1(ε)) = 0. Similarly, g(st)-lim x=ξ2 implies that

δ(K2(ε)) = 0. Now, letK(ε) =K1(ε)∪K2(ε). Thenδ(K(ε)) = 0 and hence the compliment

KC(ε) is a nonempty set andδ(KC(ε)) = 1. Now if kÎ N\K(ε), then we haveg(ξ1-ξ2)≤g

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Since ε> 0 was arbitrary, we getg(ξ1- ξ2) = 0 and henceξ1=ξ2.

Theorem 2.2. Ifg-lim x =ξthen g(st)-lim x=ξ but converse need not be true in general.

Proof. Letg-limx=ξ. Then for everyε> 0, there is a positive integerNsuch that

g(xnξ)< ε

for all n ≥N. Since the setA():= {kÎ N: g(xk- ξ)≥ ε}⊂{1, 2, 3, ...},δ(A()) = 0. Henceg(st)-limx=ξ.

The following examle shows that the converse need not be true.

Example 3.1. LetX =ℓ(1/k): = {x= (xk):∑k|xk|1/k<∞} with the paranormg(x) = (∑k |xk|1/k). Define a sequencex= (xk) by

xk:=

k, ifk=n2,nN; 0, otherwise;

and write

K(ε) :={kn:g(xk)≥ε}, 0< ε <1.

We see that

g(xk) :=

k1/k, ifk=n2,nN; 0, otherwise;

and hence

lim

k g(xk) :=

1, ifk=n2,nN; 0, otherwise;

Therefore g-limxdoes not exist. On the other handδ(K(ε)) = 0, that is,g(st)-limx= 0.

Theorem 2.3. Letg(st)-limx=ξ1and g(st) - limy=ξ2. Then

(i)g(st)-lim(x±y) =ξ1±ξ2, (ii)g(st)-limax=aξ1,a Îℝ.

Proof. It is easy to prove.

Theorem 2.4. A sequencex= (xk) in (X, g) is statistically convergent toξif and only if there exists a set K = {k1 <k2 < ... <kn < ... } ⊆ N with δ(K) = 1 such that

g(xknξ)→0(n→ ∞).

Proof. Suppose thatg(st)-limx=ξ. Now, write forr= 1, 2, ....

Kr:={nN:g(xknξ)≤1−

1 r},

and

Mr:={nN:g(xknξ)>

1 r}.

Then δ(Kr) = 0,

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and

δ(Mr) = 1,r= 1, 2,. . . (2:4:2)

Now we have to show that for nÎMr,(xkn)isg-convergent to ξ. On contrary sup-pose that (xkn) is not g-convergent to ξ. Therefore there is ε > 0 such that

g(xknξ)≤ε for infinitely many terms. Let :={nN:g(xknξ)> ε} and ε >1

r,rN. Then

δ(Mε) = 0, (2:4:3)

and by (2.4.1),Mr⊂Mε. Henceδ(Mr) = 0, which contradicts (2.4.2) and we get that

(xkn)is g-convergent toξ.

Conversely, suppose that there exists a setK= {k1 <k2<k3 < ... <kn< ...} with δ(K) = 1 such that g−limn→∞xkn=ξ. Then there is a positive integerNsuch thatg(xn-ξ) <ε for n>N. PutKε(t): = {nÎ N:g(xn-ξ)≥ε} andK’: = {kN+1,kN+2, ...}. Thenδ(K’) = 1 and

Kε⊆N-K’which implies thatδ(Kε) = 0. Henceg(st)-limx=ξ.

Theorem 2.5. Let (X, g) be a complete paranormed space. Then a sequencex= (xk) of points in (X, g) is statistically convergent if and only if it is statistically Cauchy.

Proof. Suppose thatg(st)-limx=ξ. Then, we get

δ(A(ε)) = 0, (2:5:1)

whereA(ε): = {nÎN:g(xn- ξ)≥ε/2}. This implies that

δ(AC(ε)) =δnN:g(xnξ)< ε

= 1.

Let m Î AC(ε). Then g(xm - ξ) <ε/2. Now, let B(ε): = {n Î N: g(xm - xn)≥ ε}. We need to show that B(ε)⊂A(ε). LetnÎ B(ε). Theng(xn-xm)≥εand hence g(xn-ξ)≥ ε/2, i.e. nÎ A(ε). Otherwise, ifg(xn-ξ) <εthen

εg(xnxm)≤g(xnξ) +g(xmξ)< ε2+ε2 =ε,

which is not possible. Hence B(ε) ⊂ A(ε), which implies that x = (xk) is g(st)-convergent.

Conversely, suppose that x = (xk) is g(st)-Cauchy but notg(st)-convergent. Then there existsMÎ Nsuch thatδ(G(ε) = 0,

whereG(ε): = {nÎN: g(xn- xM)≥ε}, andδ(D(ε)) = 0, whereD(ε): ={nÎN:g(xn-ξ) <ε/2}, i.e.,δ(DC(ε)) = 1. Since

g(xn- xm)≤2g(xn-ξ) <ε,

if g(xn- ξ) <ε/2. Thereforeδ(GC(ε)) = 0, i.e., δ(G(ε) = 1, which leads to a contradic-tion, sincex= (xk) wasg(st)-Cauchy. Hencex= (xk) must beg(st)-convergent.

3 Strong summability

In this section, we define the notion of strong summability by a modulus function and establish its relation with statistical convergence in a paranormed space.

Definition 3.1. A sequencex= (xk) is said to bestrongly p-Cesàro summable(0 <p<∞) to the limitξin (X, g) if

lim

n

1 n

n

j=1

g(xjξ)

p

= 0,

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Theorem 3.1. (a) If 0 <p<∞andxk® ξ[C1,g]p, thenx= (xk) is statistically conver-gent toξin (X, g).

(b) If x= (xk) is bounded and statistically convergent to ξin (X, g) thenxk ®ξ[C1,

g]p.

Proof. (a) Letxk®ξ[C1,g]p, then

0← 1 n

n

k=1

g(xkξ)

p

≥ 1 n

n

k= 1

g(xkξ)

p

ε

g(xkξ)

p

εp n ||,

asn®∞. That is,limn→∞1n||= 0and soδ(Kε) = 0, whereKε: = {k≤n: (g(xk-ξ))p

≥ε} . Hencex= (xk) is statistically convergent toξin (X, g).

(b) Suppose thatx= (xk) is bounded and statistically convergent toξin (X, g). Then for ε> 0, we haveδ(Kε) = 0. Since xÎ l, there exists M> 0 such that g(xk -ξ)≤M (k= 1, 2, ...). We have

1 n n k=1

g(xkξ)p= 1 n

n

k= 1 k∈/

g(xkξ)p+1 n

n

k= 1 k

g(xkξ)p=S1(n) +S2(n),

where

S1(n) = 1 n

n

k= 1 k∈/

g(xkξ)

p

andS2(n) = 1 n

n

k= 1 k

g(xkξ)

p

.

Now ifk∉Kεthen S1(n) <εq. ForkÎKε, we have

S2(n)≤supg(xkξ) ||/nM||/n→0, asn® ∞, sinceδ(Kε) = 0. Hence xk®ξ[C1,g]p. This completes the proof of the theorem.

Recall that amodulus fis a function from [0,∞) to [0,∞) such that (i)f(x) = 0 if and only if x= 0, (ii)f(x+y)≤f(x) +f(y) for allx, y ≥0, (iii)fis increasing, and (iv)fis continuous from the right at 0.

Now we define the following:

Definition 3.2. Let f be a modulus. we say that a sequence x = (xk) is strongly p-Cesàro summable with respect to fto the limitξin (X, g) if

lim n 1 n n j=1

f(g(xjξ))p

= 0,

(0 <p<∞). In this case we write xk®ξ(w(f, g, p)). As in [13], it is easy to prove the following:

Theorem 3.2. (a) Letfbe any modulus andxk®ξ(w(f, g, p)). Thenx= (xk) is statis-tically convergent to ξin (X, g).

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Acknowledgements

The authors would like to thank the Deanship of Scientific Research at King Abdulaziz University for its financial support under a grant with number 156/130/1431.

Authorscontributions

The authors have equitably contributed in obtaining the new results presented in this article. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Received: 14 August 2011 Accepted: 21 February 2012 Published: 21 February 2012

References

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2. Steinhaus, H: Sur la convergence ordinaire et la convergence asymptotique. Colloq Math.2, 73–34 (1951)

3. Osama, H, Edely, H, Mursaleen, M: On statisticalA-summability. Math Comput Model.49, 672–680 (2009). doi:10.1016/j. mcm.2008.05.053

4. Fridy, JA: On statistical convergence. Analysis.5, 301–313 (1985)

5. Fridy, JA: Lacunary statistical summability. J Math Anal Appl.173, 497–504 (1993). doi:10.1006/jmaa.1993.1082 6. Moricz, F: Statistical convergence of multiple sequences. Arch Math.81, 82–89 (2003). doi:10.1007/s00013-003-0506-9 7. Mursaleen, M:λ-statistical convergence. Math Slovaca.50, 111115 (2000)

8. Mursaleen, M, Osama, H, Edely, H: Statistical convergence of double sequences. J Math Anal Appl.288, 223–231 (2003). doi:10.1016/j.jmaa.2003.08.004

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10. Mursaleen, M, Mohiuddine, SA: On lacunary statistical convergence with respect to the intuitionistic fuzzy normed space. J Comput Appl Math.233, 142–149 (2009). doi:10.1016/j.cam.2009.07.005

11. Šalát, T: On statistically convergent sequences of real numbers. Math Slovaca.30, 139–150 (1980) 12. Kolk, E: The statistical convergence in Banach spaces. Tartu Ul Toime.928, 41–52 (1991)

13. Maddox, IJ: Statistical convergence in a locally convex space. Math Cambridge Phil Soc.104, 141145 (1988). doi:10.1017/S0305004100065312

14. Çakalli, H: On Statistical Convergence in topological groups. Pure Appl Math Sci.43, 2731 (1996) 15. Karakus, S: Statistical convergence on probabilistic normed spaces. Math Commun.12, 11–23 (2007)

16. Karakus, S, Demirci, K, Duman, O: Statistical convergence on intuitionistic fuzzy normed spaces. Chaos Solitons & Fractals.35, 763–69 (2008). doi:10.1016/j.chaos.2006.05.046

17. Mursaleen, M: Elements of Metric Spaces. Anamaya Publ., New Delhi (2005)

doi:10.1186/1029-242X-2012-39

Cite this article as:Alotaibi and Alroqi:Statistical convergence in a paranormed space.Journal of Inequalities and Applications20122012:39.

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