Abstract
Refining and reversing weighted arithmetic-geometric mean inequalities have been studied in many papers. In this paper, we provide some bounds for the differences between the weighted arithmetic and geometric means, using known inequalities. We improve the results given by Furuichi-Ghaemi-Gharakhanlu and Sababheh-Choi. We also give some bounds on entropies, applying the results in a different approach. We explore certain convex or concave functions, which are symmetric functions on the axis .
Keywords:
Shannon entropy; Tsallis entropy; Fermi–Dirac entropy; Bose–Einstein entropy; arithmetic mean; geometric mean; Young’s inequality MSC:
26D20; 94A15
1. Introduction
We denote a set of all probability distributions by
In this manuscript, for mathematical simplicity we remove the case for . For any , Shannon entropy , Rényi entropy and Tsallis entropy are defined as [1,2,3]
where is q-logarithmic function defined for and with . It is known that . An interesting differential relation of the Rényi entropy [4] is
which is proportional to Kullback–Leibler divergence, where .
In [5], the Fermi–Dirac-Tsallis entropy was introduced by
for and the Bose–Einstein–Tsallis entropy was given in [6] as
In the limit of , we have
and
where and are the Fermi–Dirac entropy and the Bose–Einstein entropy, respectively. See [6] and references therein for their details.
In [7], we used the expression that describes the difference between the arithmetic mean and the weighted geometric mean:
It is well known that as Young inequality or the weighted arithmetic–geometric mean inequality.
Next, we consider for . We easily find that the following properties:
and
In [8] Sababheh and Choi prove that if a and b are positive numbers with , then .
Some important results [9,10,11] on the studies used to estimate bounds on several entropies have been established, recently, via the use of mathematical inequalities. We provide some results on several entropies, applying new and improved inequalities in this paper.
2. Bounds of and Inequalities for Entropies
We first rewrite the Tsallis entropy, Rényi entropy, the Fermi–Dirac-Tsallis entropy, and the Bose–Einstein-Tsallis entropy by the use of the notation .
Lemma 1.
For and with , we have
- (i)
- ,
- (ii)
- ,
- (iii)
- ,
- (iv)
- .
Proof.
The proof can be done by the direct calculations.
- (i)
- Simple calculations
- (ii)
- Since we have the relation:
- (iii)
- We can calculate as
Thus, we have with the result of (i),
- (iv)
- We can calculate as
Thus, we have
□
We give relations on .
Lemma 2.
Let . If , then the following equalities hold:
Proof.
We note that .
- (i)
- Then,
- (ii)
- We also have
In several papers [7,12,13,14], we find estimations of the bounds of . For this purpose, we use the following inequalities from (a) to (d).
- (a)
- Kittaneh and Manasrah gave in [12]:
- (b)
- Cartwright and Field proved the inequality (see, e.g., [14]):
- (c)
- Alzer, da Fonseca, and Kovačec obtained the following inequalities (see, e.g., [13]):
Taking into account (1), (2) and taking and changing p by q in the above inequalities given in (a)–(c), we obtain the following.
- ()
- where and .
- ()
- for and .
- ()
- andfor and .
If we take , for all , in the above inequalities ()–() and passing to the sum from 1 to n, we deduce the following inequalities ()–() on .
- ()
- where .
- ()
- for .
- ()
- andfor .
Using the point (i) from Lemma 2 and inequalities ()–(), we deduce a series of inequalities for the Tsallis entropy in the following (A)–(C) as the theorem.
Theorem 1.
Let . Then we have the following (A)–(C).
- (A)
- (B)
- (C)
- and
If , then we have and , then we obtain
which implies that is decreasing related to q.
In the limit of , we find some bounds for Shannon entropy as a corollary of the above theorem.
Corollary 1.
We have the inequalities for Shannon entropy .
Using the points (ii) and (iii) from Lemma 2 and inequalities ()–(), we deduce several inequalities for Rényi entropy and for the Fermi–Dirac–Tsallis entropy in the following:
Theorem 2.
Let . Then we have
- ()
- ()
- ()
- ()
- ()
- ()
In the limit of , we find some bounds for the Fermi–Dirac–Tsallis entropy as a corollary of the above theorem.
Corollary 2.
We have the following inequalities for the Fermi–Dirac entropy :
Theorem 3.
Let . Then,
- ()
- ()
- ()
Proof.
From inequality (7), we find
Using inequalities (19), (20) and the definition of the Bose–Einstein–Tsallis entropy , given above, we find
which implies inequality (16). From inequality (8), we have:
and
Summing from 1 to n, we deduce inequality (17).
Summing from 1 to n, we deduce inequality (18). □
Corollary 3.
We have the following inequalities for the Bose–Einstein entropy :
3. New Characterizations of Young’s Inequality
The inequality of Young is given by:
which means
In this section, we give further bounds on .
Lemma 3.
Let a and b be positive real numbers, and let . Then,
Proof.
Using Lemma 2 for , then
We replace p by and a by , then we get
If we inductively repeat the above substitutions, for , then we have
Therefore, summarizing the above relations for , we obtain the relation of the statement. Applying equality (21) and taking into account that , we deduce equality (22). □
Remark 1.
From [8], if and , we have , so, we deduce , for and , for . Using the above equalities, we deduce the inequalities:
Proposition 1.
Let a and b be positive real numbers. We then have the following bounds on .
- (i)
- For , we havewhere and are defined above,
- (ii)
- For , we have
- (iii)
- For , we have
Proof.
We use the inequalities from (a) to (c), where we replace p by and a by . For and , we have the following ()–().
- ()
- ()
- ()
4. The Connection between and Different Types of Convexity
In the following, we use the inequality by Kittaneh–Manasrah as noted in (3). We prepare some lemmas to state our results.
Lemma 4.
If , where J is an interval of , is a concave function, then
Proof.
If f is concave, then we have
The following result is given in ([15], Corollary 1). This is the supplemental to the first inequality of (3).
Lemma 5.
Let a and b be positive real numbers and let . Then,
Proof.
We set the function for and . From , we find that , for and for . Thus, we have . Putting and multiplying to both sides in the inequality , we have
We similarly have
Note that the supplemental to the second inequality of (3), never generally holds:
To state the following result, we review the log-convexity/log-concavity. For the function , where , and , if , then f is often called log-convex function. If the reversed inequality holds, then f is called log-concave function.
In the following two lemmas, we deal with the symmetric function on (i.e., , for every ). The results are applied to the concrete symmetric function related to entropy, in the end of this section.
Lemma 6.
Let be a convex function such that for every . Then
Proof.
By convexity of f, we have for ,
Thus, we have
For , by exchanging t with in the above inequality, we have
Thus, we have for
For , by exchanging t with in the above inequality, we have
Therefore, we have
which implies the first inequality in (33).
By log-convexity of f, is convex so that we have which is the forth inequality of (34). The third inequality is from (33) and the second one is obtained by the Young inequality. The last inequality of (34) is trivial. Since , we have . So we can use the first inequality of (3) as
which is the fifth inequality of (34). Finally, we prove the first inequality of (34). Since , we have and . Namely, we have . By using (30), we have
□
It is notable that the right inequalities in (33) and (34) are also found in ([17], Lemma 1.1). The following lemma is a counterpart by concavity. However, it does not completely corresponded to the above lemma. (See Remark 2 below).
Lemma 7.
Let be a concave function with for every . Then
If in addition, f is log-concave, then
Proof.
By concavity of f, we have for ,
For the case of , by exchanging t with , then we have from the above inequality
Thus, we have for and ,
which implies the first inequality of (35). For the proof of the second inequality of (35), we use Lemma 4. Putting in (29), we have
which means
For the case of , by exchanging t with , we have from the above inequality
By the symmetric property of f in , we obtain
which gives the right hand side in the inequalities (35).
If f is log-concave, then we have from the first inequality of (35) with concave function , , which show the forth inequality in (36). The third inequality is just from (35). The second and last inequalities in (36) are obtained by the Young inequality.
Since we have generally, we have for . Then we apply the second inequality of (3), we have
which shows the first inequality in (36). □
Remark 2.
In general, we have the supplement to the Young inequality:
Thus, we have
Therefore, it seems difficult to bound in (36) from the above by the use of the two terms and as a simple form.
We have some bounds on by applying (3)–(6). We here show one result by the use of (3). However, we omit the other cases.
Lemma 8.
Let a and b be positive real numbers and let . Then,
Proof.
Since , we can use (3) as
Here we have the relation:
Elementary calculations imply
Considering the cases and , we obtain the inequalities (37). □
As for the bounds on , we have the following result.
Proposition 2.
Let and a function . Then we have
Proof.
Since , we set , and in Lemma 8. □
Example 1.
The so-called binary entropy (e.g., [18], example 2.1.1) defined by
The standard convention is in information theory, since we have and is undefined for . In information theory, we use 2 as the base of the logarithmic function, but we here use e for mathematical simplicity. Its selection is not essential in mathematics. Applying (35) to function with convention , we have , which is equivalent to
The above inequalities are equivalent to
where is the usual binary entropy, whose base is 2.
If we do not adopt the standard convention in information theory, then we assume precisely. Applying the inequalities in (36):
we obtain
which implies the following result.
The Fermi–Dirac entropy is defined above by
5. Concluding Remarks
We close this paper by providing some remarks on the log-convex function.
Lemma 9.
For and , we have
Proof.
Since function is concave for , we use the Jensen inequality for positive real numbers x and y as
If we take and , then we obtain
which implies (42). □
Theorem 4.
If are log-convex functions, then function is log-convex, where and .
Proof.
Since are log-convex functions, we have for ,
where we used Lemma 9 in the last inequality. Therefore, is log-convex. □
Let be the set of all complex matrices, and let be the set of all positive semi-definite matrices in .
Corollary 4.
For , , and is the unitarily invariant norm, the following functions are log-convex:
Proof.
In [19], it was shown that functions , , and are log-convex on . Thus, we have the corollary from Theorem 4. □
Since the functions are log-convex and , we can apply Lemma 6 for the symmetric function on an axis . Therefore, we obtain the chain of inequalities for the functions in the following, for example. We can obtain the similar inequalities for the other functions , and . However, we omit them. For , and , we have
Author Contributions
This work was carried out in collaboration among all authors. All authors contributed equally and significantly in writing this manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
The first author was supported in part by JSPS KAKENHI grant number 21K03341.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Acknowledgments
The authors would like to thank the reviewers for their important suggestions and careful reading of our manuscript. The authors would like to thank M. Kian, who let us know the essential estimation for the symmetric function in Lemma 6.
Conflicts of Interest
The authors declare no conflict of interest.
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