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Necessary Optimality Conditions for Interval Optimization Problems with Functional and Abstract Constraints

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Abstract

This work addresses interval optimization problems in which the objective function is interval-valued while the constraints are given in functional and abstract forms. The functional constraints are described by means of both inequalities and equalities. The abstract constraint is expressed through a closed and convex set with a nonempty interior. Necessary optimality conditions are derived, given in a multiplier rule structure involving the gH-gradient of the interval objective function along with the (classical) gradients of the constraint functions and the normal cone to the set related to the abstract constraint. The main tool is a specification of the Dubovitskii–Milyutin formalism. We defined an appropriated notion of directions of decrease to an interval-valued function, using the lower–upper partial ordering of the interval space (LU order).

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References

  1. Ahmad, I., Singh, D., Dar, B.A.: Optimality conditions in multiobjective programming problems with interval valued objective functions. Control Cybern. 44(1), 19–45 (2015). https://doi.org/10.2298/FIL1608121A

    Article  MathSciNet  MATH  Google Scholar 

  2. Ahmad, I., Singh, D., Dar, B.A.: Optimality conditions for invex interval valued nonlinear programming problems involving generalized H-derivative. Filomat 30(8), 2121–2138 (2016). https://doi.org/10.2298/FIL1608121A

    Article  MathSciNet  MATH  Google Scholar 

  3. Antczak, T.: Optimality conditions and duality results for nonsmooth vector optimization problems with the multiple interval-valued objective function. Acta Math. Sci. Ser. (Engl. Ed.) B37(4), 1133–1150 (2017). https://doi.org/10.1016/S0252-9602(17)30062-0

    Article  MathSciNet  MATH  Google Scholar 

  4. Aubin, J.P., Cellina, A.: Differential inclusions. In: Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences], vol. 264. Springer, Berlin. https://doi.org/10.1007/978-3-642-69512-4. Set-valued Maps And Viability Theory (1984)

  5. Bertsekas, D.P.: Nonlinear Programming, Athena Scientific Optimization and Computation Series, 2nd edn. Athena Scientific, Belmont (1999)

    Google Scholar 

  6. Brezis, H.: Functional Analysis, Sobolev Spaces and Partial Differential Equations. Universitext. Springer, New York (2011)

    Book  Google Scholar 

  7. Cao, E., Gao, R., Lai, M.: Research on the vehicle routing problem with interval demands. Appl. Math. Model. 54, 332–346 (2018). https://doi.org/10.1016/j.apm.2017.09.050

    Article  MathSciNet  MATH  Google Scholar 

  8. Chalco-Cano, Y., Lodwick, W.A., Rufián-Lizana, A.: Optimality conditions of type KKT for optimization problem with interval-valued objective function via generalized derivative. Fuzzy Optim. Decis. Mak. 12(3), 305–322 (2013). https://doi.org/10.1007/s10700-013-9156-y

    Article  MathSciNet  MATH  Google Scholar 

  9. Costa, T.M., Bouwmeester, H., Lodwick, W.A., Lavor, C.: Calculating the possible conformations arising from uncertainty in the molecular distance geometry problem using constraint interval analysis. Inf. Sci. 415(416), 41–5 (2017). https://doi.org/10.1016/j.ins.2017.06.015

    Article  MathSciNet  MATH  Google Scholar 

  10. Das, S., Mondal, R., Shaikh, A.A., Bhunia, A.K.: An application of control theory for imperfect production problem with carbon emission investment policy in interval environment. J. Frankl. Inst. 359(5), 1925–1970 (2022). https://doi.org/10.1016/j.jfranklin.2022.01.035

    Article  MathSciNet  MATH  Google Scholar 

  11. Diamond, P., Kloeden, P.E.: Metric Spaces of Fuzzy Sets: Theory and Applications. World Scientific, Singapore (1994)

    Book  Google Scholar 

  12. Girsanov, I.V.: Lecture Notes in Economics and Mathematical Systems, Operations Research, Computer Science, Social Science, vol. 67. Springer, Berlin (1972)

    Google Scholar 

  13. Hukuhara, M.: Intégration des applications mesurables dont la valeur est un compact convex. Funkcial Ekvac 10, 205–229 (1967)

    MathSciNet  MATH  Google Scholar 

  14. Ishibuchi, H., Tanaka, H.: Multiobjective programming in optimization of the interval objective function. Eur. J. Oper. Res. 48(2), 219–225 (1990)

    Article  Google Scholar 

  15. Kulisch, U.W., Miranker, W.L.: Computer Arithmetic in Theory and Practice. Academic Press, Inc. [Harcourt Brace Jovanovich, Publishers], New York-London. Computer Science and Applied Mathematics (1981)

  16. Kumar, P., Behera, J., Bhurjee, A.K.: Solving mean-VaR portfolio selection model with interval-typed random parameter using interval analysis. Opsearch 59(1), 41–77 (2022). https://doi.org/10.1007/s12597-021-00531-7

    Article  MathSciNet  MATH  Google Scholar 

  17. Kummari, K., Ahmad, I.: Sufficient optimality conditions and duality for nonsmooth interval-valued optimization problems via \(L\)-invex-infine functions. Politehn. Univ. Bucharest Sci. Bull. Ser. A Appl. Math. Phys. 82(1), 45–54 (2020)

    MathSciNet  Google Scholar 

  18. Luhandjula, M.K., Rangoaga, M.J.: An approach for solving a fuzzy multiobjective programming problem. Eur. J. Oper. Res. 232, 249–255 (2014)

    Article  MathSciNet  Google Scholar 

  19. Singh, D., Dar, B., Kim, D.S.: KKT optimality conditions in interval valued multiobjective programming with generalized differentiable functions. Eur. J. Oper. Res. 254(1), 29–39 (2016)

    Article  MathSciNet  Google Scholar 

  20. Stefanini, L.: A generalization of Hukuhara difference and division for interval and fuzzy arithmetic. Fuzzy Sets Syst. 161(11), 1564–1584 (2010). https://doi.org/10.1016/j.fss.2009.06.009

    Article  MathSciNet  MATH  Google Scholar 

  21. Stefanini, L., Arana-Jiménez, M.: Karush–Kuhn–Tucker conditions for interval and fuzzy optimization in several variables under total and directional generalized differentiability. Fuzzy Sets Syst. 362, 1–34 (2019). https://doi.org/10.1016/j.fss.2018.04.009

    Article  MathSciNet  MATH  Google Scholar 

  22. Stefanini, L., Bede, B.: Generalized Hukuhara differentiability of interval-valued functions and interval differential equations. Nonlinear Anal. 71(3), 1311–1328 (2009). https://doi.org/10.1016/j.fss.2012.12.004

    Article  MathSciNet  MATH  Google Scholar 

  23. Tung, L.T.: Karush–Kuhn–Tucker optimality conditions and duality for convex semi-infinite programming with multiple interval-valued objective functions. J. Appl. Math. Comput. 62, 67–91 (2020). https://doi.org/10.1007/s12190-019-01274-x

    Article  MathSciNet  MATH  Google Scholar 

  24. Van Luu, D., Mai, T.T.: Optimality and duality in constrained interval-valued optimization. 4OR 16(3), 311–337 (2018). https://doi.org/10.1007/s10288-017-0369-8

    Article  MathSciNet  MATH  Google Scholar 

  25. Wen, S., Lan, H., Hong, Y.-Y., Yu, D.C., Zhang, L., Cheng, P.: Allocation of ESS by interval optimization method considering impact of ship swinging on hybrid PV/diesel ship power system. Appl. Energy 175, 158–167 (2016). https://doi.org/10.1016/j.apenergy.2016.05.003

    Article  Google Scholar 

  26. Wu, H.C.: The Karush–Kuhn–Tucker optimality conditions for the optimization problem with fuzzy-valued objective function. Math. Methods Oper. Res. 66(2), 203–224 (2007). https://doi.org/10.1007/s00186-007-0156-y

    Article  MathSciNet  MATH  Google Scholar 

  27. Wu, H.C.: The Karush–Kuhn–Tucker optimality conditions in multiobjective programming problems with interval-valued objective functions. Eur. J. Oper. Res. 196(1), 49–60 (2009). https://doi.org/10.1016/j.ejor.2008.03.012

    Article  MathSciNet  MATH  Google Scholar 

  28. Zhang, J., Liu, S., Li, L., Feng, Q.: The KKT optimality conditions in a class of generalized convex optimization problems with an interval-valued objective function. Optim. Lett. 8(2), 607–631 (2012). https://doi.org/10.1007/s11590-012-0601-6

    Article  MathSciNet  MATH  Google Scholar 

  29. Zhao, J., Bin, M.: Karush–Kuhn–Tucker optimality conditions for a class of robust optimization problems with an interval-valued objective function. Open Math. 18(1), 781–793 (2020). https://doi.org/10.1515/math-2020-0042

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was supported by National Council for the Improvement of Higher Education—CAPES/UNESP/IBILCE (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) [Finance Code 001]; São Paulo Research Foundation—FAPESP (Fundação de Amparo à Pesquisa do Estadode São Paulo) [Grant Number 2013/07375-0]; National Council for Scientific and Technological Development—CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) [Grant Number 305786/2018-0]; and by the Dean’s Office for Graduate Studies of the São Paulo State University—PROPG (Pró-Reitoria de Pós-Graduação) [Edital 6/2021]. This work was developed while F. R. Villanueva was a Ph.D. studant at the São Paulo State University (Unesp).

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Correspondence to Valeriano Antunes de Oliveira.

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Communicated by Juan-Enrique Martinez Legaz.

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Villanueva, F.R., de Oliveira, V.A. Necessary Optimality Conditions for Interval Optimization Problems with Functional and Abstract Constraints. J Optim Theory Appl 194, 896–923 (2022). https://doi.org/10.1007/s10957-022-02055-6

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