Abstract
This study aims to consider new kinds of generalized convex fuzzy mappings (convex-𝘍𝘔), which are called strongly -preinvex fuzzy mappings. We investigated the characterization of preinvex-𝘍𝘔s using -preinvex-𝘍𝘔s, which can be viewed as a novel and innovative application. Some different types of strongly -preinvex-𝘍𝘔s are introduced, and their properties are investigated. Under appropriate conditions, we establish the relationship between strongly -invex-𝘍𝘔s and strongly -monotone fuzzy operators. Then, the minimum of strongly -preinvex-𝘍𝘔s are characterized by strongly fuzzy -variational-like inequalities. Results obtained in this paper can be viewed as a refinement and improvement of previously known results.
Keywords:
strongly α-preinvex fuzzy mappings; strongly α-invex fuzzy mappings; strongly αj-monotone fuzzy operators; strongly fuzzy α-variational-like inequalities; fuzzy optimization MSC:
26A33; 26A51; 26D10
1. Introduction
For classical convexity, several generalizations and extensions have recently been researched. Strongly convex functions on convex sets are a key concept in optimization theory and related fields, developed and investigated by Polyak [1]. Karmardian [2] discussed how when utilizing highly convex functions, there is only one way to solve nonlinear complementarity issues. Zhao and Wang [3], Qu and Li [4], and Nikodem and Pales [5] researched convergence analysis for resolving equilibrium issues and variational inequalities with the use of strongly convex functions. Regarding the characteristics and applications of strongly convex functions, we recommend the reader to [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25] and its references for further information. Hanson [26] proposed invex functions for differentiable functions, and they played an important role in mathematical programming. Israel and Mond proposed and researched the idea of invex sets and preinvex functions. Differential preinvex functions are invex functions, as is common knowledge. The opposite is likewise true in light of Condition C [27]. Furthermore, Noor [28] has shown that the minimum may be described by variational inequalities by researching the optimality criteria of differentiable preinvex functions. Noor et al. [29,30,31,32,33] examined the uses of strongly preinvex functions and their characteristics. Jeyakumar and Mond [34] established a different class of V-invex mapping on the V-invex set (nonconvex function), which has important applications in multiobjective optimization and extended convex programming. See [35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57], as well as the references therein, for other uses and characteristics of strongly preinvex functions.
Operations research, computer science, management sciences, artificial intelligence, control engineering, and decision sciences are just a few of the applied sciences and pure mathematics problems that are studied in [58] as a result of the extensive research on fuzzy sets and systems that has been performed on the development of various fields. Convex analysis has contributed significantly and fundamentally to the development of several practical and pure scientific domains. Similar to this, fuzzy convex analysis is regarded as the core concept in fuzzy optimization. In order to describe a fuzzy number, it is an interval’s generalized form (in crisp set theory). Fuzzy numbers were first defined by Zadeh [58], and subsequently, Dubois and Prade [59] expanded on this work by adding new requirements for fuzzy numbers. Additionally, Goetschel and Voxman [60] adjusted many conditions on fuzzy numbers to make them easier to manage. For example, in [61], the first criterion for a fuzzy number is that it is a continuous function, but in [60], the fuzzy number is upper semi-continuous. The objective is to make it simple to establish a metric for a collection of fuzzy numbers by relaxing the requirements on them, which will then enable us to examine certain fundamental topological space features. The concepts of fuzzy mapping (𝘍𝘔) from to the set of fuzzy numbers, Lipschitz continuity of fuzzy values, fuzzy logarithmic convex and quasi-convex-FMs, and others were studied by Furukawa [61], Nanda, and Kar [62], and Syau [63]. Yan and Xu [64] proposed the notions of epigraphs and convexity of 𝘍𝘔s and detailed the characteristics of convex fuzzy and quasi-convex- 𝘍𝘔s based on the concept of ordering established by Goetschel and Voxman [65]. For more information, see [66,67,68,69,70,71,72,73,74,75,76] and the references therein.
The concept of fuzzy convexity has been broadly applied and expanded in several ways, with important applications in numerous fields. Preinvex-𝘍𝘔 should be mentioned as one of the convex-𝘍𝘔 generalizations that are most often used. By introducing and researching the concept of fuzzy preinvex mapping on the fuzzy invex set, Noor [77] showed that the fuzzy optimality requirements of differentiable fuzzy preinvex mappings may be identified by variational-like inequalities. Preinvex-local 𝘍𝘔s minimums are also global minimums on invex sets, and an invex set’s epigraph is a necessary and sufficient condition for an 𝘍𝘔 to be preinvex. The preinvex-𝘍𝘔 notion that Noor [77] introduced was further developed by Syau in [78]. Additionally, Syau and Lee [79] covered the terms for continuity and convexity using metric definitions based on fuzzy numbers and linear ordering. Extension of the Weierstrass theorem from real-valued functions to 𝘍𝘔s is also one of their key contributions in the literature. See [80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98] and its references for contemporary applications.
Inspired and motivated by current research efforts, as well as by the significance of the concepts of the invexity and preinvexity of mappings, in Section 2, we review various concepts that will be useful for further research, including fuzzy sets, fuzzy numbers, 𝘍𝘔s, convex-𝘍𝘔s, and preinvex-𝘍𝘔s. Section 3 examines the primary findings. The concepts of strongly -preinvex, quasi -preinvex, and log -preinvex-𝘍𝘔s are presented in Section 3, along with some of their features. Section 4 studies brand-new connections between distinct strongly -preinvex-𝘍𝘔 ideas. Then, as an intriguing byproduct of our primary findings, the minimum of strongly -preinvexity is characterized by strongly fuzzy -variational-like inequalities.
2. Preliminaries
A fuzzy set on is a mapping , for each fuzzy set and , then -level sets of are denoted and defined as follows . If , then is considered support of . By , we define the closure of .
Definition 1.
A fuzzy set is considered a fuzzy number with the following properties:
- (a)
- is normal, i.e., there exists such that
- (b)
- for all ,;
- (c)
- sup is compact.
denotes the set of all fuzzy numbers. For a fuzzy number, it is convenient to distinguish the following -levels,
From this definition, we have
where
Since each is also a fuzzy number, it can be defined as
Now we discuss some properties of fuzzy numbers under addition and scalar multiplication. If and , then and can be defined as
It is also well known that for any and
for each From this definition, we have
Remark 1.
Obviously,. is closed under addition and nonnegative scalar multiplication. Furthermore, for each scalar number
For any , we say that if for all , , and . If then there exist such that or . We say comparable if for any , we have or ; otherwise, they are noncomparable. Sometimes we may write instead of and note that is a partial ordered set under the relation .
If there exist such that then by this result, we have the existence of the Hukuhara difference of and , and we say that is the H-difference of and denoted by (see [82]). If H-difference exists, then ,
A mapping is considered 𝘍𝘔. For each associated with , we define the family of interval-valued functions defined by and denoted by Now, for any the endpoint functions are called lower and upper functions, respectively.
Definition 2.
[80] Let and . Then 𝘍𝘔 is considered a generalized differentiable (G-differentiable) at if there exists an element such that for all , sufficiently small, there exist and the limits (in the metric )
or
or
or
where the limits are taken in the metric space for
and denotes the well-known Hausdorff metric on the space of intervals .
Definition 3.
[62] A 𝘍𝘔 is considered convex on the convex set if
Strictly fuzzy convex mapping if strict inequality holds for. is considered a fuzzy concave mapping ifis convex onStrictly fuzzy concave mapping if strict inequality holds for.
Definition 4.
[62]
A 𝘍𝘔 is considered quasi-convex on the convex set if
Definition 5.
[27]
The set in is considered an invex set pertaining to arbitrary bifunction if
The invex set is also called the -connected set if Note that, convex set is considered an invex set in the classical sense, but the converse is not valid. For instance, the following set is an invex set pertaining to nontrivial bifunction given as
Definition 6.
[27]
A 𝘍𝘔 is considered preinvex on invex set pertaining to bifunction if
where
Strictly fuzzy preinvex mapping if strict inequality holds for . is considered fuzzy preincave mapping if is preinvex on Strictly fuzzy preincave mapping if strict inequality holds for .
Definition 7.
[27] 𝘍𝘔 is considered quasi-preinvex on invex set pertaining to if
Definition 8.
[27]
. A mapping is considered fuzzy log-preinvex on invex set pertaining to bifunction if there exists a positive number such that
where
Definition 9.
where and . The -invex set is also called the -connected set. Note that the convex set with and is considered an invex set in the classical sense, but the converse is not valid. For example, in the following set , is an invex set pertaining to nontrivial bifunction and given as
[34]. The set is considered an -invex set pertaining to arbitrary bifunctions and if
Remark 2.
- (i)
- If , then the set is an invex set.
- (ii)
- If and , then the set is considered star-shaped.
- (iii)
- If and then the set is considered convex.
3. Strongly -Preinvex Fuzzy Mappings
Let be a nonempty -invex subset of pertaining to Let be continuous mapping and be an arbitrary continuous bifunction. Let be a bifunction. We denote ‖‖ and ⟨⟩ as the norm and inner product, respectively.
Definition 10.
Let be an -invex set and be a positive number. Then 𝘍𝘔 is considered strongly -preinvex pertaining to bifunctions and if
strictly strongly -preinvex-𝘍𝘔 if strict inequality holds for and is considered strongly fuzzy preincave mapping if is strongly -preinvex on Strictly fuzzy preincave mapping if strict inequality holds for .
Remark 3.
Strongly α-preinvex-FMs have some very nice properties similar to fuzzy preinvex mapping,
- (1)
- If is strongly-preinvex-𝘍𝘔, then is also strongly -preinvex for .
- (2)
- If and both are strongly -preinvex-𝘍𝘔s, then is also strongly -preinvex-𝘍𝘔s.
Now we discuss some special cases of strongly -preinvex-𝘍𝘔s:
If then strongly -preinvex-𝘍𝘔 becomes -preinvex-𝘍𝘔, that is
If then strongly -preinvex-𝘍𝘔 becomes strongly preinvex-𝘍𝘔, that is
If then (13) reduces to
The mapping is considered preinvex-𝘍𝘔.
If then strongly -preinvex-𝘍𝘔 is considered star-shaped strongly -preinvex-𝘍𝘔.
If and then strongly -preinvex-𝘍𝘔 becomes strongly convex-𝘍𝘔, that is
If then Equation (15) becomes
The mapping is considered convex-𝘍𝘔.
If and , then Equation (11) becomes
The mapping is considered star-shaped J-strongly-preinvex-𝘍𝘔.
If , then (11) becomes
The 𝘍𝘔 is considered J-strongly -preinvex. For , Equation (16) reduces to
. Then 𝘍𝘔 is considered strongly J-preinvex. When , then Equation (17) becomes
Then 𝘍𝘔 is considered J-preinvex.
We also define the affine strongly-preinvex mapping.
Definition 11.
A mappingis considered strongly affine-preinvex-𝘍𝘔 on-invex setpertaining to bifunctionif there exists a positive numbersuch that
If , then we also say thatis strongly affine J--preinvex-𝘍𝘔such that
Theorem 1.
Let be an -invex set pertaining to and let be a 𝘍𝘔 parametrized by
Then is strongly -preinvex on with modulus if, and only if, for all and are strongly -preinvex pertaining to and modulus
Proof.
and
Then by Equations (21), (1), and (2), we obtain
for all and . Hence, is strongly -preinvex-𝘍𝘔 on with modulus
Assume that for each and are strongly -preinvex pertaining to and modulus on Then from Equation (21), we have
Conversely, let is strongly -preinvex-𝘍𝘔 on with modulus Then for all and we have From Equation (20), we have
From Equation (20) and Equations (1)–(3), we obtain
for all and Then by strongly -preinvexity of , we have for all and such that
And
for each Hence, the result follows. □
Example 1.
We consider the𝘍𝘔sdefined by,
Then, for eachwe have. Since endpoint functions are strongly generalized preinvex for each, thenis strongly generalized preinvex-𝘍𝘔pertaining to
and
We now establish a result for strongly -preinvex-𝘍𝘔, which shows that the difference of strongly -preinvex-𝘍𝘔 and strong affine -preinvex-𝘍𝘔 is again a strongly -preinvex-𝘍𝘔.
Theorem 2.
Let𝘍𝘔 be a strongly affine-preinvex pertaining toand. Thenis strongly-preinvex-𝘍𝘔 pertaining to the sameif, and only if,is-preinvex-𝘍𝘔 pertaining to.
Proof.
The “If” part is obvious. To prove the “only if”, assume that is a strongly affine -preinvex-𝘍𝘔 pertaining to and . Then
Since is strongly -preinvex-𝘍𝘔 pertaining to the same , then
From Equations (22) and (23), we have
from which it follows that
Showing that is strongly -preinvex-𝘍𝘔. □
Definition 12.
A𝘍𝘔 is considered strongly quasi-preinvex on-invex setpertaining toif there exists a positive numbersuch that
Similarly, A 𝘍𝘔 is considered strongly quasi-preincave if is strongly quasi -preinvex on
If, then we get the definition of strong quasi-preinvex-𝘍𝘔, that is
If and , then we get the definition of quasi-preinvex-𝘍𝘔 in the classical sense, that is
Theorem 3.
Let𝘍𝘔 be a strongly-preinvex andsuch thatThenis strictly strongly quasi-preinvex-𝘍𝘔.
Proof.
Let and be strongly -preinvex-𝘍𝘔. Then, for all and we have
since we have
Hence, is strictly strongly quasi -preinvex-𝘍𝘔 with □
Theorem 4.
Let be a -invex set pertaining to and let be a 𝘍𝘔 parametrized by
Then is strongly quasi -preinvex on with modulus if, and only if, for all
and are strongly quasi -preinvex pertaining to and modulus
Proof.
The demonstration is analogous to the demonstration of Theorem 1. □
Definition 13.
A𝘍𝘔 is considered strongly log-preinvex on-invex setpertaining to bifunctionsif there exists a positive numbersuch that
Similarly, A 𝘍𝘔 is considered strongly log -preincave if is strongly log -preinvex on
If, then we get the definition of log preinvex-𝘍𝘔, that is
, where . The mapping is strongly log-preinvex-𝘍𝘔 pertaining to .
If and , then we get the definition of log-preinvex-𝘍𝘔 in the classical sense, that is
, where The mapping is log-preinvex-𝘍𝘔 pertaining to .
From Definition 13, we have
It can easily be seen that strongly log -preinvex-𝘍𝘔 strongly -preinvex-𝘍𝘔 strongly quasi -preinvex-𝘍𝘔.
For , Definition 10 and Definition 13, reduces to:
Condition A.
Which plays an important role in studying the properties of -preinvex-𝘍𝘔s and 𝛼-invex-𝘍𝘔s. If , then Condition A reduces to the following for preinvex-𝘍𝘔s.
Condition B.
For the applications of Condition B, see [28,30,34,62].
Definition 14.
A𝘍𝘔 is considered pseudo-preinvex on the-invex setif there exists a strictly positive bifunctionsuch that
Theorem 5.
Letbe a strongly-preinvex-𝘍𝘔 onsuch thatThenis strongly pseudo-preinvex-𝘍𝘔.
Proof.
Let and be a strongly -preinvex-𝘍𝘔. Then
where . This prove that is strongly pseudo -preinvex-𝘍𝘔. □
4. G-Differentiable Strongly -Preinvex Fuzzy Mappings
In this section, we propose the concepts of strongly -invex-𝘍𝘔s and strongly -monotone fuzzy operators. With the help of these ideas, different properties of strongly -preinvex-𝘍𝘔s are characterized. At the end, it is proved that the minimum of strongly -preinvex-𝘍𝘔s can be distinguished by strongly fuzzy -variational-like inequalities.
Definition 15.
The G-differentiable 𝘍𝘔 on -invex set is considered strongly -invex pertaining to if there exists a constant such that
for all where is G-differentiable of at
From Definition 15, it enables us to define the following new definitions:
Definition 16.
The G-differentiable 𝘍𝘔 on is considered -invex pertaining to if there exists a constant such that
for all where is G-differentiable of at
Definition 17.
The G-differentiable 𝘍𝘔 on is considered strongly pseudo -invex if there exists a constant such that
for all
Definition 18.
The G-differentiable 𝘍𝘔 on is considered pseudo -invex if there exists a constant such that
for all
Definition 19.
The G-differentiable 𝘍𝘔 on is considered strongly quasi -invex if there exists a constant such that
for all
Definition 20.
The G-differentiable 𝘍𝘔 on is considered quasi -invex if there exists a constant such that
for all .
If then the Definitions 15–20 reduce to known ones. These definitions may play an important role in the fuzzy optimization problem and mathematical programming.
We need the following assumption regarding the bifunctions ,, which play an important role in the G-differentiation of the main results.
Condition C
[34]
Clearly for we have for all
It is well known that each G-differentiable preinvex-𝘍𝘔 is invex-𝘍𝘔, but to prove its converse, we need a special condition.
It can easily be seen that if , then Condition C collapses to the following condition:
Condition D
[27]
For the applications of Condition D, see [29,30,31,77,78].
Theorem 6.
Let be a G-differentiable strongly -preinvex-𝘍𝘔 pertaining to . Let Condition C hold and for all . Then is strongly -preinvex-𝘍𝘔 when, and only when, is strongly α-invex-𝘍𝘔.
Proof.
Let be G-differentiable -preinvex-𝘍𝘔. Since is -preinvex, then for each and , we have
which implies that
taking the limit in the above inequality as , we have
Conversely, let be a 𝛼-invex-𝘍𝘔. Since is an -invex set, we have, for all and . Taking = in (29), we get
using Condition C, we have
In a similar way, we have
Multiplying (35) by and (36) by , and adding the resultant, we have
which implies that
Hence, is strongly -preinvex-𝘍𝘔 pertaining to . □
Theorem 7.
Let be a G-differentiable𝘍𝘔 on . If the 𝘍𝘔 is strongly 𝛼-invex, then
Conversely, if Conditions A and C hold, and for all , then is strongly 𝛼-invex-𝘍𝘔 pertaining to .
Proof.
which implies that
since where .
Let is strongly 𝛼-invex-𝘍𝘔. Then,
replacing by and by in Equation (38), we get
Adding Equations (38) and (39), we have
Conversely, assume that (37) holds. Then
Since is an -invex set, we have, for all and . Taking = in (40), we get
Integrating Equation (42) between 0 to1 pertaining to , we get
which implies that
by using Condition, A
Showing that is -invex-𝘍𝘔 on □
With Theorem 6 and Theorem 7, we have the following new definitions.
Definition 21.
A G-differentiable mapping is considered:
- (i)
- Strongly -monotone fuzzy operator when, and only when, there exists a constant such that.
- (ii)
- -monotone fuzzy operator when, and only when, we have
- (iii)
- Strongly -pseudomonotone fuzzy operator when, and only when, there exists a constant such that
.
- (iv)
- Strongly relaxed -pseudomonotone fuzzy operator when, and only when, there exists a constant such that
- (v)
- Strictly -monotone fuzzy operator when, and only when, we have
- (vi)
- -pseudomonotone fuzzy operator when, and only when, there exists a constant such that
- (vii)
- Quasi -pseudomonotone fuzzy operator when, and only when, there exists a constant such that
- (viii)
- Strictly -pseudomonotone fuzzy operator when, and only when, there exists a constant such that
If then Definition 21, reduce to new one.
As special case of Theorem 7, we have the following:
Corollary 1.
Let be G-differentiable 𝘍𝘔 on and let Condition C hold. Then is strongly -invex-𝘍𝘔 if, and only if, is strongly -monotone fuzzy operator.
Theorem 8.
Let G-differential of mapping on be strongly -pseudomonotone fuzzy operator, and let Conditions A and C hold. Then is a strongly pseudo -invex-𝘍𝘔.
Proof.
Let be a strongly -pseudomonotone fuzzy operator. Then for all we have
Then
which implies that
since is an -invex set so we have, for all and . Taking = in (4.15), we get
by using Condition C, we have
assume that
Taking G-derivative pertaining to , then using (44), we have
Integrating Equation (44) between 0 and 1 pertaining to , we get
By using Condition A, we have
Hence, is a strongly pseudo -invex-𝘍𝘔. □
Following results are special cases of Theorem 8, we have:
Corollary 2.
Let G-differential of 𝘍𝘔 on be -pseudomonotone fuzzy operator. Then, is a pseudo -invex-𝘍𝘔.
Corollary 3.
Let G-differential of 𝘍𝘔 on be strongly quasi -pseudomonotone fuzzy operator. Then is a strongly quasi -invex-𝘍𝘔.
Corollary 4.
Let G-differential of 𝘍𝘔 on be quasi -pseudomonotone fuzzy operator, and let Condition C hold. Then is a pseudo -invex-𝘍𝘔.
We now discuss the fuzzy optimality condition for G-differentiable strongly -preinvex-𝘍𝘔s, which is main motivation of our next result.
Theorem 9.
Let be a G-differentiable strongly -preinvex-𝘍𝘔 modulus . If is the minimum of the , then
Proof.
Let be a minimum of . Then
for all .
Since is an -invex set, for all , ,
Taking in (47), we get
taking limit in the above inequality as , we get
since is a G-differentiable strongly -preinvex-𝘍𝘔, so
Again, taking limit in the above inequality as , we get
from which, using Equation (48), we have
Hence, the result follows. □
Theorem 10.
Let be a G-differentiable strongly -preinvex-𝘍𝘔 modulus and
then is the minimum of the 𝘍𝘔
Proof.
Let be a G-differentiable strongly -preinvex-𝘍𝘔 and satisfies Equation (29), we have
from which, using Equation (32), we have
which implies that
Remark 4.
The inequality of the type Equation (49) is considered strongly variational-like inequality. We would like to emphasize that the optimality conditions of the strongly-preinvex-𝘍𝘔 can be characterized by the following inequality
which is considered a variational-like inequality.
5. Conclusions
In this paper, we generalized the concepts of convex-𝘍𝘔s and preinvex-𝘍𝘔s pertaining to ,, which is called strongly -preinvex-𝘍𝘔s pertaining to ,. It is shown that strongly convex-𝘍𝘔s and strongly preinvex-𝘍𝘔s are special cases of strongly -preinvex-𝘍𝘔s. Under certain conditions, it is also proved that differential strongly -preinvex-𝘍𝘔s are strongly -invex-𝘍𝘔s and vice versa. It is also proved that the minimum of strongly -preinvex-𝘍𝘔s can be characterized by strong -variational-like inequalities and -variational-like inequalities. This idea in fuzzy convex and nonconvex theory needs to be studied more thoroughly. Furthermore, multiobjective fuzzy optimization has large and significant applications. In the future, we will try to explore this concept for integral inequalities by using fuzzy Reimann and fuzzy Reimann–Liouville fractional integrals.
Author Contributions
Conceptualization, M.B.K.; methodology, M.B.K.; validation, M.A.N. and M.S.S.; formal analysis, G.S.-G.; investigation, M.A.N.; resources, M.B.K.; data curation, M.S.S.; writing—original draft preparation, M.B.K., G.S.-G. and M.S.S.; writing—review and editing, M.B.K.; visualization, M.S.S.; supervision, M.B.K. and M.A.N.; project administration, M.B.K.; funding acquisition, G.S.-G. All authors have read and agreed to the published version of the manuscript.
Funding
The research of Santos-García was funded by the project ProCode-UCM (PID2019-108528RB-C22) from the Spanish Ministerio de Ciencia e Innovación.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Acknowledgments
The authors would like to thank the Rector, COMSATS University Islamabad, Islamabad, Pakistan, for providing excellent research and academic environments.
Conflicts of Interest
The authors declare no conflict of interest.
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