Abstract
This article deals with the classes of approximate Minty- and Stampacchia-type vector variational inequalities on Hadamard manifolds and a class of nonsmooth interval-valued vector optimization problems. By using the Clarke subdifferentials, we define a new class of functions on Hadamard manifolds, namely, the geodesic -approximately convex functions. Under geodesic -approximate convexity hypothesis, we derive the relationship between the solutions of these approximate vector variational inequalities and nonsmooth interval-valued vector optimization problems. This paper extends and generalizes some existing results in the literature.
1. Introduction
In traditional mathematical programming problems, the coefficients are usually always considered as deterministic values. However, in many real-world optimization problems, this assumption is not satisfied. Since the coefficients of a programming problem are either subject to errors of measurements and estimators or vary with market fluctuations, it is therefore always difficult to obtain exact data. In order to solve optimization problems, three different approaches are employed, namely, the stochastic optimization problem, deterministic optimization problem, and interval-valued optimization problem. In interval-valued optimization, the coefficients of the objective and constraint functions are compact intervals. For recent development and updated surveys of interval-valued optimization, we refer to the refs. [1,2,3,4,5,6,7,8,9]. The assumption or specification of probabilistic distribution (as in stochastic programming) or possible distribution (as in fuzzy programming) is not required for interval programming. Antczak [10] derived optimality and duality conditions for the nonsmooth interval-valued vector optimization problems.
Convexity is very restrictive notion for the solution of several real-world problems, for instance, mathematical economics. Luc et al. [11] defined the class of -convex functions in order to generalize the notion of convexity. The class of approximately convex functions was introduced by Ngai et al. [12] using -convexity. Daniilidis and Georgiev [13] established that a locally Lipschitz function is approximately convex if, and only if its Clarke’s subdifferential is a submonotone operator. Ngai and Penot [12] derived several characterizations for approximate convex functions in terms of the generalized subdifferential. Amini-Harandi and Farajzadeh [14] extended and refined the results of Daniilidis and Georgiev [13] from Banach spaces to locally convex spaces.
Giannessi [15,16] introduced the vector versions of Minty [17] and Stampacchia [18] variational inequalities for finite dimensional Euclidean spaces. Since then, many researchers studied vector variational inequalities and their generalizations arduously as an efficient tool to find optimal solutions of vector optimization problems (see, for instance, the refs. [19,20,21,22,23,24] and the references cited therein). Németh [25] defined the notion of variational inequalities on Hadamard manifolds. Barani [26] proposed the concept of strong monotonicity for set-valued mappings and some notions of strong convexity for locally Lipschitz functions on Hadamard manifolds. Chen and Huang [27] derived the relationship between convex vector optimization problems and vector variational inequalities using the Clarke subdifferential and proved certain existence theorems. Recently, Chen and Fang [28] established the relationship between Minty and Stampacchia vector variational inequalities and nonsmooth vector optimization problems under pseudoconvexity assumptions. Upadhyay and Mishra [29] studied the equivalence among approximate vector variational inequalities and interval-valued vector optimization problems involving approximate -pseudoconvex functions. For other ideas on this topic, the reader can consult Ceng et al. [30].
Pareto optimal solutions or efficient solutions have been extensively used in vector optimization problems. Due to the complexity of vector optimization problems, many researchers have been studying several variants of efficient solutions in recent years (see, for instance, the refs. [31,32,33,34,35] and the references cited therein). For the vector optimization problem, Loridan [36] introduced the notion of -efficient solutions. Mishra and Laha [37] introduced the concept of approximate efficient solution for vector optimization problems using approximately star-shaped functions. The characterization and applications of approximate efficient solutions of vector optimization problems have been studied by several authors (see, for instance, the refs. [33,36,37,38] and the references cited therein).
The paper is organized as follows: In Section 2, we provide a few definitions and preliminaries. We consider approximate Stampacchia and Minty vector variational inequalities in Section 3 and derive a relationship between the approximate efficient solutions of nonsmooth interval-valued vector optimization problems on Hadamard manifolds using -approximately convex functions. The results are summarized in Section 4.
2. Definition and Preliminaries
Let be the p-dimensional Euclidean space, be the non-negative orthant of and 0 be the origin of the non-negative orthant. Let be the positive orthant of . For , the following notions for equality and inequalities will be used throughout the sequel:
- (i)
- (ii)
- (iii)
- (iv)
- for some .
Now, we recall the notions of interval analysis from Moore [39,40]. Let denote a closed interval, where and denote the lower and upper bounds of , respectively. Let be the class of all closed intervals in . For , we have
- (i)
- ;
- (ii)
- ;
- (iii)
- ,
where and .
Additionally, we have
where . can be represented as the closed interval .
Let . Then, we define
- and ,
- and , that is, one of the following is satisfied:
- (a)
- and ; or
- (b)
- and ; or
- (c)
- and .
Remark 1.
The intervals are comparable if and only if or .
Let be closed intervals and denotes an interval-valued vector. For the interval-valued vectors and , such that and are comparable for each , we have
- if and only if for all ;
- if and only if for all and for some .
The function is said to be an interval-valued function, where and are real valued functions satisfying , for all .
The following notions of Riemannian manifolds are from [26,28].
Let H be a connected manifold with finite dimension . For denotes the tangent space of H at z and denotes the tangent bundle of . H is a Riemannian manifold endowed with a Riemannian metric on the tangent space with an associated norm denoted by . Given a piecewise differentiable curve joining to , the length of is defined by
For any , the Riemannian distance between p and q is defined by , is the infimum over all piecewise differentiable curve joining p and . This distance function d induces the original topology on . On every Riemannian manifold, there exists exactly one covariant derivation called a Levi–Civita connection denoted by . We also recall that a geodesic is a smooth path whose tangent is parallel along the path , that is, satisfies the equation
It is known that a Levi–Civita connection ∇ induces an isometry , the so-called parallel translation along from to . Any path joining p and q in H such that is a geodesic, and it is called a minimal geodesic. If H is complete, then any points in H can be joined by a minimal geodesic.
In the following, let us suppose that H is complete. The exponential map at z is defined by for every where is the geodesic starting at z with velocity that is, and It is easy to see that for each real number We note that the map is differentiable on for every
A simply connected complete Riemannian manifold with nonpositive sectional curvature is called a Hadamard manifold. If H is a Hadamard manifold, then is a diffeomorphism for every , and if then there exists a unique minimal geodesic joining z and
Now, we recall the following notions of nonsmooth analysis from Barani [26] and Hosseini and Pouryayevali [41].
Definition 1.
A nonempty subset Γ of H is said to be a geodesic convex set if for all the geodesic joining z to y is contained in
Definition 2.
Let function be a proper function. The function Φ is said to be Lipschitz near if there exists a positive constant , and such that
where Moreover, the function Φ is locally Lipschitz on if it is Lipschitz near for any
From now onwards, let be an open geodesic convex subset of H and be a locally Lipschitz function on
Definition 3.
The Clarke generalized directional derivative of Φ at in the direction of a vector is defined as
where is a chart at Indeed,
Definition 4.
The Clarke generalized subdifferential of Φ at denoted by is the subset of defined by
Definition 5.
(Lebourg Mean Value Theorem [26]) Let and be a smooth path joining z and Let Φ be a locally Lipschitz function on for all Then, there exist and such that
Now, we consider the following notions of geodesic approximate convexity and geodesic approximate monotonicity on Hadamard manifolds.
Let be a multi-valued map such that for each and the domain of A is defined by
Definition 6.
The function Φ is said to be geodesic approximately convex at , if for any there exists such that for each we have
Definition 7.
Let be a multi-valued map. Then A is said to be geodesic submonotone at if for every there exists such that for every and for every one has
where
For locally Lipschitz geodesic approximately convex functions, we have the following characterization.
Theorem 1.
The function Φ is geodesic approximately convex at if and only if for every , there exists such that for any and one has
The following theorem establishes the relationship between a geodesic approximately convex function and geodesic submonotonicity of its Clarke subdifferential on Hadamard manifolds.
Theorem 2.
The function Φ is geodesic approximately convex at if and only if is geodesic submonotone at
Definition 8.
A function is said to be locally Lipschitz on if the real valued functions and are locally Lipschitz on
Definition 9.
An interval-valued function is geodesic -approximately convex at if the real-valued functions and are geodesic approximately convex at
Consider the following nonsmooth interval-valued vector optimization problem:
where such that for each is a locally Lipschitz interval-valued function.
Definition 10.
A point is said to be an approximate efficient solution of (NIVOP):
(LUAES), if for each sufficiently small, there does not exist such that
(LUAES), if for each sufficiently small, there exists such that
(LUAES), if for each there exists such that
where
Remark 2.
If then In this particular case, the notions of (LUAES), (LUAES) and (LUAES) reduce to (ALUES), (ALUES) and (ALUES), respectively, as considered by Upadhyay and Mishra [29].
Now, we consider the following approximate Minty and Stampacchia vector variational inequalities which will be used in the sequel:
(AMVVI) To find such that, for each sufficiently small, there does not exist such that, for any and one has
(AMVVI) To find such that, for each sufficiently small , there exists such that, for any and one has
(AMVVI) To find such that, for each there exists such that, for any and one has
(ASVVI) To find such that, for each sufficiently small , there exist and such that
(ASVVI) To find such that, for each sufficiently small and for all and one has
(ASVVI) To find such that, for each there exists such that, for all and one has
where
Remark 3.
If then In this particular case, the notions of (AMVVI), (AMVVI) and (AMVVI) reduce to (AMVI), (AMVI) and (AMVI), respectively, as considered by Upadhyay and Mishra [29].
Remark 4.
In then In this particular case, the notions of (ASVVI), (ASVVI) and (ASVVI) reduce to (ASVI), (ASVI) and (ASVI), respectively, as considered by Upadhyay and Mishra [29].
3. Relationship among (NIVOP), (AMVVI) and (ASVVI)
In this section, we derive some equivalence relations between the nonsmooth interval-valued vector optimization problem (NIVOP) and approximate vector variational inequalities (AMVVI), (AMVVI), (AMVVI), (ASVVI), (ASVVI) and (ASVVI) under geodesic -approximate convexity.
Theorem 3.
For each let be geodesic -approximately convex at Then, the following statements hold:
- (a)
- If is a (LUAES) of the (NIVOP), then is a solution of the (AMVVI);
- (b)
- If is a (LUAES) of the (NIVOP), then is a solution of the (AMVVI);
- (c)
- If is a solution of the (AMVVI) then is a (LUAES) of the (NIVOP).
Proof.
- (a)
- Assume that is a (LUAES) of (NIVOP), but it is not a solution of (AMVVI) Then, for some sufficiently small , there exists such that for any and one hasthat is,Since each is geodesic -approximately convex at it follows that, for any there exists such that, for every and we get
- (b)
- On the contrary, suppose that does not solve (AMVVI) Then, there exists sufficiently small , such that for all there exists and we havethat is,Since each is geodesic -approximately convex at it follows that, for any there exists such that, for every and one has
- (c)
- Suppose that is a solution of (AMVVI) but not a (LUAES) of (NIVOP); then, for some and for each there exists such thatthat is,From (7) it follows thatSince each is a geodesic approximately convex function at therefore for any there exists such that for every we havethat is,where is a geodesic joining to Define asBy Lebourg mean value theorem, there exists and such thatwhere andChoosing we haveandSince each and are geodesic approximately convex at it follows that and is geodesic submonotone at . Hence, for any there exists such that for all we havefor all and and
This contradicts the assumption that is a solution of (AMVVI). The proof is complete. □
To illustrate the significance of Theorem 3, we consider the following interval-valued vector optimization problem on a Hadamard manifold.
Example 1.
where are interval-valued functions defined on and is the Riemannian manifold with Riemannian metric with and sectional curvature It is clear that the set Γ is a geodesic convex set.
The tangent plane at any point denoted by , equals The Riemannian distance function is given by
The geodesic curve starting from and with a tangent unit vector of Ω at the starting point z is given by
The inverse of an exponential map for any is given by
Consider the functions are given by
It is clear that the functions and are locally Lipschitz functions on The subdifferentials of and are given by
We can verify that the function is geodesic -approximately convex at as for each , we can get such that
Similarly, the function is geodesic -approximately convex at as for each we can get such that
Moreover, is an (LUAES) of the problem (P). Since, for each sufficiently small , there does not exist any such that for all we have
Similarly, is an (LUAES) of the problem (P). Since, for each sufficiently small , for all there does not exist any such that
Furthermore, solves (AMVVI). Since, for any sufficiently small , there does not exist any such that for all we have
for all and
Similarly, solves (AMVVI). Since, for each and sufficiently small , there does not exist any such that
for all and
Theorem 4.
For each let be geodesic -approximately convex at Then, the following statements hold:
- 1.
- If for some and are strictly geodesic approximately convex at and is a solution for the (ASVVI) then is an (LUAES) of the (NIVOP);
- 2.
- If is a solution the (ASVVI) then is an (LUAES) of the (NIVOP);
- 3.
- If is a solution of the (ASVVI) then is an (LUAES) of the (NIVOP).
Proof.
- Let be a solution of (ASVVI) and suppose, to the contrary, that it is not an (LUAES). There exist and such thatSince each is geodesic -approximately convex, then for any there exist such thatfor all and and for some and are strictly geodesic -approximately convex functions. Then for any , there exist such thatfor each and
- Let solves (ASVVI). Then, for any there exist such thatfor each andSince each is geodesic approximately convex at , for any there exist such thatfor all andHence, is an (LUAES) of the (NIVOP).
- First, we will show that if is a solution of (ASVVI), then solves (AMVVI). Consequently, from Theorem 3, is an (LUAES) of (NIVOP). On the contrary, assume that does not solve (AMVVI), then for all there exists and such thatSince each is geodesic -approximate convex, then and are geodesic approximate convex functions. Therefore, and are geodesic submonotone. For all and we haveFrom (30), it follows that
which contradicts our assumption. This completes the proof. □
4. Conclusions
In this paper, we have considered the classes of approximate Minty and Stampacchia type vector variational inequalities (AMVVI), (AMVVI), (AMVVI), (ASVVI), (ASVVI) and (ASVVI). Under geodesic -approximate convexity assumptions, we have proved the equivalence between the solutions of the considered approximate variational inequalities (AMVVI), (AMVVI), (AMVVI), (ASVVI), (ASVVI) and (ASVVI) and approximate efficient solutions (LUAES), (LUAES), (LUAES) of nonsmooth interval-valued vector optimization problem (NIVOP). The results of the paper extended and generalized some earlier results of [19,29,37,42,43,44].
Author Contributions
All authors contributed equally to this paper. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Ahmad, I.; Singh, D.; Dar, B.A. Optimality and duality in non-differentiable interval valued multiobjective programming. Int. J. Math. Oper. Res. 2017, 11, 332–356. [Google Scholar] [CrossRef]
- Dar, B.A.; Jayswal, A.; Singh, D. Optimality, duality and saddle point analysis for interval-valued nondifferentiable multiobjective fractional programming problems. Optimization 2021, 70, 1275–1305. [Google Scholar] [CrossRef]
- Debnath, I.P.; Gupta, S.K. The Karush-Kuhn-Tucker conditions for multiple fractional interval valued optimization problems. RAIRO-Oper. Res. 2020, 54, 1161–1188. [Google Scholar] [CrossRef]
- Ghosh, D.; Chauhan, R.S.; Mesiar, R.; Debnath, A.K. Generalized Hukuhara Gâteaux and Fréchet derivatives of interval-valued functions and their application in optimization with interval-valued functions. Inf. Sci. 2020, 510, 317–340. [Google Scholar] [CrossRef]
- Guo, Y.; Ye, G.; Liu, W.; Zhao, D.; Treanţă, S. Optimality conditions and duality for a class of generalized convex interval-valued optimization problems. Mathematics 2021, 9, 2979. [Google Scholar] [CrossRef]
- Treanţă, S. Characterization results of solutions in interval-valued optimization problems with mixed constraints. J. Global Optim. 2021. [Google Scholar] [CrossRef]
- Treanţă, S. On a class of constrained interval-valued optimization problems governed by mechanical work cost functionals. J. Optim. Theory Appl. 2021, 188, 913–924. [Google Scholar] [CrossRef]
- Treanţă, S. On a dual pair of multiobjective interval-valued variational control problems. Mathematics 2021, 9, 893. [Google Scholar] [CrossRef]
- Treanţă, S. Duality theorems for (ρ,ψ,d)-quasiinvex multiobjective optimization problems with interval-valued components. Mathematics 2021, 9, 894. [Google Scholar] [CrossRef]
- Antczak, T. Optimality conditions and duality results for nonsmooth vector optimization problems with the multiple interval-valued objective function. Acta Math. Sci. Ser. B Engl. Ed. 2017, 37, 1133–1150. [Google Scholar] [CrossRef]
- Luc, D.T.; Ngai, H.V.; Théra, M. On ϵ-monotonicity and ϵ-convexity. In Calculus of Variations and Differential Equations; Research Notes in Maths; Ioffe, A., Reich, S., Shafrir, I., Eds.; Chapman and Hall: London, UK 1999; pp. 82–100.ϵ-convexity. In Calculus of Variations and Differential Equations; Research Notes in Maths; Ioffe, A., Reich, S., Shafrir, I., Eds.; Chapman and Hall: London, UK, 1999; pp. 82–100. [Google Scholar]
- Ngai, H.V.; Luc, D.T.; Théra, M. Approximate convex functions. J. Nonlinear Convex Anal. 2000, 1, 155–176. [Google Scholar]
- Daniilidis, A.; Georgiev, P. Approximate convexity and submonotonicity. J. Math. Anal. Appl. 2004, 291, 292–301. [Google Scholar] [CrossRef][Green Version]
- Amini-Harandi, A.; Farajzadeh, A.P. Approximate convexity and submonotonicity in locally convex spaces. Bull. Iran. Math. Soc. 2010, 36, 69–82. [Google Scholar]
- Giannessi, F. Theorems of the alternative, quadratic programming and complementarity problems. In Variational Inequalities and Complementarity Problems; Cottle, R.W., Giannessi, F., Lions, J.L., Eds.; Wiley: New York, NY, USA, 1980; pp. 151–186. [Google Scholar]
- Giannessi, F. On Minty Variational Principle. New trends in Mathematical Programming; Kluwer Academic Publishers: Boston, MA, USA, 1998; pp. 93–99. [Google Scholar]
- Minty, G.J. On the generalization of a direct method of the calculus of variations. Bull. Amer. Math. Soc. 1967, 73, 315–321, Erratum in Bull. Amer. Math. Soc. 1968, 74, 768. [Google Scholar] [CrossRef]
- Stampacchia, G. Formes bilineaires coercitives sur les ensembles convexes. C. R. Acad. Sci. 1964, 258, 4413–4416. [Google Scholar]
- Giannessi, F. On Minty variational principle. In New Trends in Mathematical Programming, Homage to Steven Vajda; Giannessi, F., Komlósi, S., Rapcsák, T., Eds.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1998; pp. 93–99. [Google Scholar]
- Lee, G.M.; Kim, D.S.; Lee, B.S.; Yen, N.D. Vector variational inequality as a tool for studying vector optimization problems. Nonlinear Anal. 1998, 34, 745–765. [Google Scholar] [CrossRef]
- Lee, G.M.; Lee, K.B. Vector variational inequalities for nondifferentiable convex vector optimization problems. J. Glob. Optim. 2005, 32, 597–612. [Google Scholar] [CrossRef]
- Mishra, S.K.; Upadhyay, B.B. Some relations between vector variational inequality problems and nonsmooth vector optimization problems using quasi efficiency. Positivity 2013, 17, 1071–1083. [Google Scholar] [CrossRef]
- Upadhyay, B.B.; Mishra, P. On generalized Minty and Stampacchia vector variational-like inequalities and nonsmooth vector optimization problem involving higher order strong invexity. J. Sci. Res. Benaras Hindu Univ. 2020, 64, 282–291. [Google Scholar] [CrossRef]
- Upadhyay, B.B.; Mishra, P. On interval-valued multiobjective programming problems and vector variational-like inequalities using limiting subdifferential. In Computational Management; Patnaik, S., Tajeddini, K., Jain, V., Eds.; Springer: Cham, Switzerland, 2021; pp. 325–343. [Google Scholar]
- Németh, S.Z. Variational inequalities on Hadamard manifolds. Nonlinear Anal. Theory Methods Appl. Ser. A Theory Methods 2003, 52, 1491–1498. [Google Scholar] [CrossRef]
- Barani, A. Generalized monotonicity and convexity for locally Lipschitz functions on Hadamard manifolds. Diff. Geom. Dyn. Syst. 2013, 15, 26–37. [Google Scholar]
- Chen, S.-I.; Huang, N.-J. Vector variational inequalities and vector optimization problems on Hadamard manifolds. Optim. Lett. 2016, 10, 753–767. [Google Scholar] [CrossRef]
- Chen, S.-I.; Fang, C.-J. Vector variational inequality with pseudoconvexity on Hadamard manifolds. Optimization 2016, 65, 2067–2080. [Google Scholar] [CrossRef]
- Upadhyay, B.B.; Mishra, P. On Minty variational principle for nonsmooth interval-valued multiobjective programming problems. In Variational Analysis and Applications, Proceedings of the Indo-French Seminar on Optimization, Varanasi, India, 2–4 February 2020; Laha, V., Maréchals, P., Mishra, S.K., Eds.; Springer: Singapore, 2020; pp. 265–282. [Google Scholar]
- Ceng, L.C.; Shehu, Y.; Wang, Y. Parallel Tseng’s Extragradient Methods for Solving Systems of Variational Inequalities on Hadamard Manifolds. Symmetry 2020, 12, 43. [Google Scholar] [CrossRef]
- Chinchuluun, A.; Pardalos, P.M. A survey of recent developments in multiobjective optimization. Ann. Oper. Res. 2007, 154, 29–50. [Google Scholar] [CrossRef]
- Chinchuluun, A.; Pardalos, P.M.; Migdalas, A.; Pitsoulis, L. Pareto Optimality. Game Theory and Equilibria; Springer: Berlin, Germany, 2008. [Google Scholar]
- Gupta, A.; Mehra, A.; Bhatia, D. Approximate convexity in vector optimisation. Bull. Aust. Math. Soc. 2006, 74, 207–218. [Google Scholar] [CrossRef]
- Hanson, M.A.; Mond, B. Necessary and sufficient conditions in constrained optimization. Math. Program. 1987, 37, 51–58. [Google Scholar] [CrossRef]
- Jeyakumar, V.; Mond, B. On generalised convex mathematical programming. J. Austral. Math. Soc. Ser. B 1992, 34, 43–53. [Google Scholar] [CrossRef]
- Loridan, P. ϵ-solutions in vector minimization problems. J. Optim. Theory Appl. 1984, 43, 265–276. [Google Scholar] [CrossRef]
- Mishra, S.K.; Laha, V. On approximately star-shaped functions and approximate vector variational inequalities. J. Optim. Theory Appl. 2013, 156, 278–293. [Google Scholar] [CrossRef]
- Gupta, D.; Mehra, A. Two types of approximate saddle points. Numer. Funct. Anal. Optim. 2008, 29, 532–550. [Google Scholar] [CrossRef]
- Moore, R.E. Interval Analysis; Prentice-Hall: Englewood Cliffs, NJ, USA, 1966. [Google Scholar]
- Moore, R.E. Methods and Applications of Interval Analysis; SIAM Studies in Applied Mathematics; SIAM: Philadelphia, PA, USA, 1979. [Google Scholar]
- Hosseini, S.; Pouryayevali, M.R. Generalized gradients and characterization of epi-Lipschitz sets in Riemannian manifolds. Nonlinear Anal. Theory Methods Appl. Ser. A Theory Methods 2011, 74, 3884–3895. [Google Scholar] [CrossRef]
- Bhatia, G. Optimality and mixed saddle point criteria in multiobjective optimization. J. Math. Anal. Appl. 2008, 342, 135–145. [Google Scholar] [CrossRef]
- Yang, X.Q. Generalized convex functions and vector variational inequalities. J. Optim. Theory Appl. 1993, 79, 563–580. [Google Scholar] [CrossRef]
- Upadhyay, B.B.; Mohapatra, R.N.; Mishra, S.K. On relationships between vector variational inequality and nonsmooth vector optimization problems via strict minimizers. Adv. Nonlinear Var. Inequal. 2017, 20, 1–12. [Google Scholar]
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