Abstract
First, we show that the non-trivial fuzzy inner product space under the linearity condition does not exist, which means a fuzzy inner product space with linearity produces only a crisp real number for each pair of vectors. If the positive-definiteness is added to the condition, then the Cauchy–Schwartz inequality is also proved.
1. Introduction
In a real situation, there are many cases when vagueness or ambiguity are observed with numbers, such as, “a few”, “several” or “about 10”. Zadeh [] first introduced a fuzzy set to express such numbers which include vagueness. Since then, numerous concepts and theories of fuzzy logic and fuzzy mathematics are introduced by many authors.
Especially, the concept of fuzzy norm on a vector space was first introduced by Katsaras []. Since his works, many kinds of norm was made. For example, Felbin [] introduced an alternative definition of a fuzzy norm(namely, Felbin’s fuzzy norm) related to a fuzzy metric of Kaleva-Seikkala’s type []. For another example, Bag and Samanta defined another fuzzy norm [].
The concept of a fuzzy inner product was first introduced by Goudarzi et al []. Since their works, a study for fuzzy inner product has been progressed actively [,,]. In 2010, Hasankhani et al defined another fuzzy inner product that arises from Felbin’s fuzzy norm. In that paper, they considered a fuzzy inner product under both the linearity condition and the positive definite condition and provided the Cauchy–Schwartz inequality and completion in their contexts. In 2015, Saheli conducted a comparative study of the relationship between Goudarzia’s definition and Hasankhani’s definition for a fuzzy inner product (see [,,]). In 2017, Daraby et al. [] conducted a study about topological properties of fuzzy inner product spaces in Hasankhani’s contexts. In 2019, J. M. Kim and K. Y. Lee [] consider approximation properties in Felbin fuzzy normed spaces. They gave the characterizations of approximation properties in Felbin fuzzy normed spaces. In 2020, the authors in [] made topological tools to analyze such approximation properties. They gave dual problems for approximation properties.
In order to deal with fuzzy data in applications, the mathematical setting of the vector spaces is very important. Therefore basic definitions and theory for fuzzy inner product spaces are crucial and fundamental for fuzzy applications.
We are motivated by both the linearity and the positive definite condition of fuzzy inner product spaces in Hasankhani’s contexts because those conditions may be natural and basic in crisp inner product space. In the fuzzy sense, they are hardly dealt with so far because it is not easy to find a proper example. We show how those two conditions have an effect on the space fuzzy inner products, especially crisp inner products.
In this paper, the fuzzy inner product by Hasankhani’s et al is studied much more deeply, which can be naturally considered form the concept of the basic inner product. In fact, it is shown that the linear condition turns any inner product into a crisp one. Furthermore, if the positive-definiteness is added to the condition, then the Cauchy–Schwartz inequality is also satisfied.
2. Preliminaries
Definition 1
([]). A mapping is called a fuzzy real number with α-level set
if it satisfies the following conditions:
- (i)
- there exists such that
- (ii)
- for each , there exist real numbers such that the α-level set is equal to the closed interval
Remark 1.
The condition (ii) of Definition 1 is equivalent to convex and upper semi continuous:
- (1)
- a fuzzy real number η is convex if where
- (2)
- a fuzzy real number η is called upper semi-continuous if for all and with , there is such that i.e., for all and is open in the usual topology of
The set of all fuzzy real numbers is denoted by . If and whenever , then is called a non-negative fuzzy real number and denotes the set of all non-negative fuzzy real numbers. We note that real number for all and all . Each can be considered as the fuzzy real number denoted by
hence it follows that can be embedded in .
Definition 2
([,]). The arithmetic operations and ⊘ on are defined by
which are special cases of Zadeh’s extension principles.
Definition 3
([]). The absolute value of is defined by
Lemma 1
([,]). Let and . Then for all ,
We will use operations in ([] Page 1405).
Definition 4.
Let X be a vector space over . Assume the mappings are symmetric and non-decreasing in both arguments, and that and . Let . The quadruple is called a fuzzy normed space ([]) with the fuzzy norm , if the following conditions are satisfied:
- (F1)
- if , then
- (F2)
- if and only if ,
- (F3)
- for and ,
- (F4)
- for all ,
- (F4L)
- whenever and
- (F4R)
- whenever and
Here, we fix and for all and we write .
3. Results
Recall the definition of a real-valued fuzzy inner product on a vector space which is linear and positive-definite at the same time []:
Definition 5.
Let X be a vector space over A fuzzy scalar product on X is a mapping such that for all vectors and , we have
- (IP1)
- (IP2)
- (IP3)
- ,
Furthermore, if the following condition is added, then is called a fuzzy inner product:
- (IP4)
- ,
- (IP5)
- if
- (IP6)
- if and only if .
The vector space X with a real valued fuzzy scalar/inner product is called a fuzzy real scalar/inner product space.
In the sense of linear algebra, a scalar product on a vector space is also called a bilinear symmetric form. The following theorem says that a fuzzy scalar product produces only crisp real numbers.
Theorem 1.
Let X be a vector space over A fuzzy scalar product a fuzzy real number valued map satisfying conditions produces only crisp real numbers for each pair of vectors.
Proof.
For any
Denote by Then, for any real number we get
so Thus is a crisp real number. □
Theorem 1 shows that, on a given real-valued space, there is one-to-one correspondence between the space of fuzzy scalar products and that of crisp inner products. See the following example:
Example 1.
Let be a given inner product of some Hilbert space X over . Then let us define a fuzzy inner product given by
Therefore satisfies the linearity and positive-definite condition. However, it is clear that the given fuzzy inner product space turns into a crisp inner product space. To show the uniqueness, assume that is any real-valued fuzzy inner product satisfying Since is also a fuzzy scalar product, we get from Theorem 1, so
If positive-definite condition is added to the hypothesis of Theorem 1, i.e., if X is a real-valued fuzzy inner product space, then it can be shown that the Cauchy–Schwartz inequality holds in a more strong sense.
To do it, consider the following lemma which is easily checked:
Lemma 2.
Given a fuzzy real number
- (i)
- if then and
- (ii)
- if then and
Recall the Cauchy–Schwartz inequality with respect to a complex fuzzy inner product []. The one with respect to a real fuzzy inner product can also be shown as follows:
Theorem 2
(Cauchy–Schwartz inequality). For vectors , and for each we have
Hence, it holds that
Proof.
Since all of and are fuzzy real numbers, it suffices to show that the inequality holds for each If w is a zero vector , then
which implies and so the theorem holds. Assume that w is not a zero vector. Then from the definition 5, Denote by a fuzzy real number x. Let
Consider Then, the inequality
holds. Thus we can rewrite the equation as follows:
From Lemma 2,
This gives
and
□
Remark 2 indicates that a fuzzy scalar product is different from a crisp inner product because a crisp inner product always satisfies Cauchy–Schwartz inequality.
Remark 2.
Without the positive-definite condition, a fuzzy scalar product may not satisfy the Cauchy–Schwartz inequality: consider the real vector space and a fuzzy scalar product defined by
Then, and which implies that does not satisfy the Cauchy–Schwartz inequality even though it is non-degenerate: if for any we get so
Recall Theorem 1. Its result may be shown through the Cauchy–Schwartz inequality for a real-valued fuzzy inner product space, too:
Corollary 1.
Given , for some
Proof.
From the Cauchy–Schwartz inequality (see Theorem 2), for any
which implies for some Then for any ,
which gives for some □
Corollary 1 shows that, on a given real-valued space, there is one-to-one correspondence between the space of fuzzy inner products and that of crisp inner products with positive-definiteness. Furthermore, they are subspaces of a space of fuzzy number satisfying the Cauchy–Schwartz inequality.
Remark 3.
Corollary 1 might not hold if both conditions and in Definition 5 do not hold. In fact, M. Saheli and S. K. Gelousalar show that the modified linearity condition can produce a nontrivial fuzzy inner product space (see [] Example 2.9).
4. Conclusions
In this paper, we defined a fuzzy scalar product space and a fuzzy inner product space. We proved that there is no meaningful fuzzy inner product space under linearity condition, which turns them into a crisp inner product one. In addition, we proved that the Cauchy–Schwartz inequality holds under the positive-definite condition. In future study, the fuzzy sub-linearity and sub-bilinearity conditions will be defined and the Cauchy–Schwartz inequality will be dealt with under those conditions.
Author Contributions
Conceptualization, T.B., J.E.L., K.Y.L., and J.H.Y.; methodology, T.B., J.E.L., K.Y.L., and J.H.Y.; writing—original draft preparation, T.B., J.E.L.; writing—review and editing, J.E.L. All authors have read and agreed to the published version of the manuscript.
Funding
The first author was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2018R1D1A1A02047995). The second author was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(2019R1A2C1002653). The third author was supported by NRF-2017R1C1B5017026 funded by the Korean Government. The fourth author was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020R1A2C1A01011131).
Acknowledgments
The authors wish to thank the referees for their invaluable comments on the original draft.
Conflicts of Interest
The authors declare no conflict of interest.
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