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REFINEMENTS OF INEQUALITIES BETWEEN THE SUM OF SQUARES AND THE EXPONENTIAL OF SUM OF A NONNEGATIVE SEQUENCE

YU MIAO, LI-MIN LIU, AND FENG QI

COLLEGE OFMATHEMATICS ANDINFORMATIONSCIENCE

HENANNORMALUNIVERSITY

HENANPROVINCE, 453007, P.R. CHINA

yumiao728@yahoo.com.cn llim2004@163.com

RESEARCHINSTITUTE OFMATHEMATICALINEQUALITYTHEORY

HENANPOLYTECHNICUNIVERSITY

JIAOZUOCITY, HENANPROVINCE

454010, P.R. CHINA

qifeng618@gmail.com

Received 12 November, 2007; accepted 07 March, 2008 Communicated by A. Sofo

ABSTRACT. Using probability theory methods, the following sharp inequality is established:

ek kk

n

X

i=1

xi

!k

exp

n

X

i=1

xi

! ,

wherekN,nNandxi 0for1in. Upon takingk= 2in the above inequality, the inequalities obtained in [F. Qi, Inequalities between the sum of squares and the exponential of sum of a nonnegative sequence, J. Inequal. Pure Appl. Math. 8(3) (2007), Art. 78] are refined.

Key words and phrases: Inequality, Exponential of sum, Nonnegative sequence, Normal random variable.

2000 Mathematics Subject Classification. 26D15; 60E15.

1. INTRODUCTION

In , the following two inequalities were found.

Theorem A. For(x1, x2, . . . , xn)∈[0,∞)nandn ≥2, the inequality

(1.1) e2

4

n

X

i=1

x2i ≤exp

n

X

i=1

xi

!

is valid. Equality in (1.1) holds ifxi = 2for some given1≤i≤nandxj = 0for all1≤j ≤n withj 6=i. Thus, the constant e42 in (1.1) is the best possible.

337-07

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Theorem B. Let{xi}i=1be a nonnegative sequence such thatP

i=1xi <∞. Then

(1.2) e2

4

X

i=1

x2i ≤exp

X

i=1

xi

! .

Equality in (1.2) holds ifxi = 2 for some giveni ∈ N andxj = 0 for all j ∈ Nwithj 6= i.

Thus, the constant e42 in (1.2) is the best possible.

In this note, by using some inequalities of normal random variables in probability theory, we will establish the following two inequalities whose special cases refine inequalities (1.1) and (1.2).

Theorem 1.1. For(x1, x2, . . . , xn)∈[0,∞)nandn≥1, the inequality

(1.3) ek

kk

n

X

i=1

xi

!k

≤exp

n

X

i=1

xi

!

holds for allk ∈N. Equality in (1.3) holds ifPn

i=1xi =k.

Theorem 1.2. Let {xi}i=1 be a nonnegative sequence such that P

i=1xi < ∞. Then the in- equality

(1.4) ek

kk

X

i=1

xi

!k

≤exp

X

i=1

xi

!

is valid for allk∈N. Equality in (1.4) holds ifP

i=1xi =k.

Our original ideas stem from probability theory, so we will prove the above theorems by using some normal inequalities. In fact, from the proofs of the above theorems in the next section, one will notice that there may be simpler proofs of them, by which we will obtain more the general results of Section 3.

2. PROOFS OFTHEOREM1.1 ANDTHEOREM1.2

In order to prove Theorem 1.1 and Theorem 1.2, the following two lemmas are necessary.

Lemma 2.1. LetSbe a normal random variable with meanµand varianceσ2. Then

(2.1) E exp{tS}

= exp

µt+t2σ2 2

, t ∈R

and

(2.2) E(S−µ)2k2k(2k−1)!!, k∈N.

Proof. The proof is straightforward.

Lemma 2.2. LetSbe a normal random variable with mean0and varianceσ2. Then

(2.3) ek

(2k)k(2k−1)!!E(S2k)≤E eS

, k ∈N.

Proof. Putting

f(x) =klogx− x

2, x∈(0,∞),

it is easy to check thatf(x)takes the maximum value atx= 2k. By Lemma 2.1, we know that E(S2k) =σ2k(2k−1)!!, and E eS

=eσ2/2.

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Therefore, for the function g(σ2) = ek

(2k)k(2k−1)!!σ2k(2k−1)!!−eσ2/2 = ek

(2k)k(2k−1)!!E(S2k)−E eS , it is easy to check thatg(σ2)≤0and at the pointσ2 = 2k, the equality holds.

Now we are in a position to prove Theorem 1.1 and Theorem 1.2.

Proof of Theorem 1.1. Leti}1≤i≤n be a sequence of independent normal random variables with mean zero and varianceσi2 = 2xi for alli = 1, . . . , n. Furthermore, letSn = Pn

i=1ξi, and it is well known thatSn is a normal random variable with mean zero and variance σ2 = 2Pn

i=1xi. Therefore, we have

EeSn =eσ2/2 = exp

n

X

i=1

xi

!

and

E(Sn2) =σ2 = 2

n

X

i=1

xi. From Lemma 2.2, we have

ek

(2k)k(2k−1)!!E(Sn2k)≤E eSn ,

that is,

(2.4) ek

kk

n

X

i=1

xi

!k

≤exp

n

X

i=1

xi

! , where the equality holds in (2.4) ifPn

i=1xi =k.

Proof of Theorem 1.2. This can be concluded by lettingn→ ∞in Theorem 1.1.

3. FURTHERDISCUSSION

In this section, we will give the general results of Theorem 1.1 and Theorem 1.2 by a simpler proof.

Theorem 3.1. For(x1, x2, . . . , xn)∈[0,∞)nandn≥1, the inequality

(3.1) ek

kk

n

X

i=1

xi

!k

≤exp

n

X

i=1

xi

!

holds for all k ∈ (0,∞). Equality in (3.1) holds if Pn

i=1xi = k. For (x1, x2, . . . , xn) ∈ (−∞,0]nandn≥1, the inequality

(3.2) ek

|k|k

n

X

i=1

xi

!k

≥exp

n

X

i=1

xi

!

holds for allk ∈(−∞,0). Equality in (3.2) holds ifPn

i=1xi =k.

Proof. For allC > 0, s > 0and k > 0, letf(s) = logC+klogs−s. It is easy to see that the function f(s) takes its maximum at the points = k. If we lets = k, then we can obtain C = kekk. The remainder of the proof is easy and thus omitted.

By similar arguments to those above, we can further obtain the following result.

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Theorem 3.2. Let {xi}i=1 be a nonnegative sequence such that P

i=1xi < ∞. Then the in- equality

(3.3) ek

kk

X

i=1

xi

!k

≤exp

X

i=1

xi

!

is valid for allk ∈(0,∞). Equality in (3.3) holds ifP

i=1xi =k. In addition, let{xi}i=1 be a non-positive sequence such thatP

i=1xi >−∞. Then the inequality

(3.4) ek

|k|k

X

i=1

xi

!k

≥exp

X

i=1

xi

!

is valid for allk∈(−∞,0). Equality in (3.4) holds ifP

i=1xi =k.

4. REMARKS

After proving Theorem 1.1 and Theorem 1.2, we would like to state several remarks and an open problem posed in .

Remark 1. If we takek = 2in inequality (1.3), then (4.1) e2

4

n

X

i=1

x2i ≤ e2 4

n

X

i=1

x2i + 2 X

1≤i<j≤n

xixj

!

= e2 4

n

X

i=1

xi

!2

≤exp

n

X

i=1

xi

!

which means that inequality (1.3) refines inequality (1.1).

Remark 2. If we letk = 2in inequality (1.4), then (4.2) e2

4

X

i=1

x2i ≤ e2 4

X

i=1

x2i + 2 X

j>i≥1

xixj

!

= e2 4

X

i=1

xi

!2

≤exp

X

i=1

xi

! ,

which means that inequality (1.4) refines inequality (1.2).

Remark 3. If we letPn

i=1xi = y ≥ 0in (1.3) orP

i=1xi = y ≥ 0in (1.4), then inequalities (1.3) and (1.4) can be rewritten as

(4.3) ek

kkyk≤ey which is equivalent to

(4.4)

y k

k

≤ey−k and y

k ≤eyk−1. Taking yk =sin the above inequality yields

(4.5) s≤es−1.

It is clear that inequality (4.5) is valid for alls ∈ Rand the equality in it holds if and only if s = 1. This implies that inequalities (1.3) and (1.4) hold fork ∈ (0,∞)andxi ∈ Rsuch that Pn

i=1xi ≥ 0 forn ∈ N orP

i=1xi ≥ 0 respectively and that the equalities in (1.3) and (1.4) hold if and only ifPn

i=1xi =korP

i=1xi =k respectively.

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Remark 4. In , Open Problem 1, was posed: For (x1, x2, . . . , xn) ∈ [0,∞)n and n ≥ 2, determine the best possible constantsαn, λn ∈Rand0< βn, µn<∞such that

(4.6) βn

n

X

i=1

xαin ≤exp

n

X

i=1

xi

!

≤µn

n

X

i=1

xλin.

Recently, in a private communication with the third author, Huan-Nan Shi proved using ma- jorization that the best constant in the left hand side of (4.6) is

(4.7) βn = eαn

αnαn ifαn≥1. This means that the inequality

(4.8) exp 2

n

X

i=1

xi

!

≤n1−λn

n

X

i=1

xi

!λn n

X

i=1

xλin

holds forλn∈R. Ifxi = 1for1≤i≤n, then the above inequality becomes

(4.9) e2n≤n1−λnnλnn =n2

which is not valid. This prompts us to check the validity of the right-hand inequality in (4.6): If the right-hand inequality in (4.6) is valid, then it is clear that

(4.10) exp

n

X

i=1

xi

!

≤µn n

X

i=1

xλin ≤µn n

X

i=1

xi

!λn

, which is equivalent to

(4.11) ex ≤µnxλn

for x ≥ 0 and two constants λn ∈ R and 0 < µn < ∞. This must lead to a contradiction.

Therefore, Open Problem 1 in  should and can be modified as follows.

Open Problem 1. For(x1, x2, . . . , xn)∈Rnandn ∈N, determine the best possible constants αn, λn∈Rand0< βn, µn <∞such that

(4.12) βn

n

X

i=1

|xi|αn ≤exp

n

X

i=1

xi

!

≤µn

n

X

i=1

|xi|λn.

REFERENCES

 F. QI, Inequalities between the sum of squares and the exponential of sum of a nonnegative sequence, J. Inequal. Pure Appl. Math., 8(3) (2007), Art. 78. [ONLINE:http://jipam.vu.edu.au/

article.php?sid=895].

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