Convexity and generalized convexity play a central role in mathematical programming for duality results and in order to characterize the solutions set. In this paper, taking in mind Craven's notion of K-invexity function (when K is a cone in Rn\mathbb{R}^n) and Martin's notion of Karush-Kuhn-Tucker invexity (hereafter KKT-invexity), we define new notions of generalized convexity for a multiobjective problem with conic constraints. These new notions are both necessary and sufficient to ensure every Karush-Kuhn-Tucker point is a solution. The study of the solutions is also done through the solutions of an associated scalar problem. A Mond-Weir type dual problem is formulated and weak and strong duality results are provided. The notions and results that exist in the literature up to now are particular instances of the ones presented here.

Contact details are reproduced from the original publication and may be historical.

Beatriz Hernández-Jiménez

Depto Economía, Métodos Cuantitativos e Historia Económica, Area de Estadística e Investigación Operativa, Universidad Pablo de Olavide, Edificio 3 - Ctra Utrera - Km 1, 41013 Sevilla, Spain

mbherjim@upo.es

Rafaela Osuna-Gómez

Depto de Estadística e Investigación Operativa, Facultad de Matemáticas, Universidad de Sevilla, Aptdo. 1160, 41012 Sevilla, Spain

rafaela@us.es

Marko A. Rojas-Medar

Depto de Ciencias Básicas, Facultad de Ciencias, Universidad del Bío-Bío, Campus Fernando May, Chillán, Chile

marko@ueubiobio.cl
  1. J. F. Bonnans, A. Shapiro: Perturbation Analysis of Optimization Problems, Springer, New York (2000).https://doi.org/10.1007/978-1-4612-1394-9Google Scholar
  2. A. J. V. Brandão, M. A. Rojas-Medar, G. N. Silva: Optimality conditions for Pareto nonsmooth nonconvex programming in Banach spaces, J. Optim. Theory Appl. 103(1) (1999) 65–73.https://doi.org/10.1023/A:1021769232224Google Scholar
  3. F. H. Clarke: Optimization and Nonsmooth Analysis, Wiley, New York (1983).Google Scholar
  4. B. D. Craven: Lagrangean conditions and quasiduality, Bull. Aust. Math. 16 (1977) 325–339.https://doi.org/10.1017/S0004972700023431Google Scholar
  5. B. D. Craven: Invex functions and constrained local minima, Bull. Aust. Math. 24 (1981) 357–366.https://doi.org/10.1017/S0004972700004895Google Scholar
  6. B. D. Craven: Nonsmooth multiobjective programming, Numer. Funct. Anal. Optim. 10 (1989) 49–64.https://doi.org/10.1080/01630568908816290Google Scholar
  7. B. D. Craven: Control and Optimization, Chapman & Hall, London (1995).https://doi.org/10.1007/978-1-4899-7226-2Google Scholar
  8. L. B. Dos Santos, R. Osuna-Gómez, M. A. Rojas-Medar, A. Rufián-Lizana: Preinvex functions and weak efficient solutions for some vectorial optimization problem in Banach spaces, Comput. Math. Appl. 48 (2004) 885–895.https://doi.org/10.1016/j.camwa.2003.05.013Google Scholar
  9. B. El Abdouni, L. Thibault: Lagrange multipliers for Pareto nonsmooth programming problems in Banach spaces, Optimization 26 (1992) 277–285.https://doi.org/10.1080/02331939208843857Google Scholar
  10. I. V. Girsanov: Lectures on Mathematical Theory of Extremum Problems, Lecture Notes in Economics and Mathemathical Systems 67, Springer, Berlin (1972).https://doi.org/10.1007/978-3-642-80684-1Google Scholar
  11. M. A. Hanson: On sufficiency of the Kuhn-Tucker conditions, J. Math. Anal. Appl. 80 (1981) 545–550.https://doi.org/10.1016/0022-247X(81)90123-2Google Scholar
  12. B. Hernández-Jiménez, R. Osuna-Gómez, M. A. Rojas-Medar, M. Arana-Jiménez: Characterization of optimal solutions for nonlinear programming problems with conic constraints, Optimization 60(5) (2011) 619–626.https://doi.org/10.1080/02331930903578692Google Scholar
  13. D. M. Martin: The essence of invexity, J. Optim. Theory Appl. 17(1) (1985) 65–76.https://doi.org/10.1007/BF00941316Google Scholar
  14. M. Minami: Weak Pareto-optimal necessary conditions in a nondifferential multiobjective programm on a Banach space, J. Optim. Theory Appl. 41 (1983) 451–461.https://doi.org/10.1007/BF00935364Google Scholar
  15. S. K. Mishra, R. N. Mukherjee: On generalised convex multiobjective nonsmooth programming, J. Aust. Math. Soc., Ser. B 38 (1996) 140–148.https://doi.org/10.1017/S0334270000000515Google Scholar
  16. R. Osuna-Gómez: Programación con Objetivos Múltiples: Dualidad, Tesis Ddoctoral, Universidad de Sevilla (1995).Google Scholar
  17. R. Osuna-Gómez, A. Rufián-Lizana, P. Ruiz-Canales: Invex functions and generalized convexity in multiobjective programming, J. Optim. Theory Appl. 98(3) (1998) 651–661.https://doi.org/10.1023/A:1022628130448Google Scholar
  18. R. Osuna Gómez, A. Beato-Moreno, A. Rufián-Lizana: Generalized convexity in multiobjective programming, J. Math. Anal. Appl. 233 (1999) 205–220.https://doi.org/10.1006/jmaa.1999.6284Google Scholar
  19. R. Osuna-Gómez, A. Rufián-Lizana, P. Ruíz-Canales: Multiobjective fractional programming with generalized convexity, Top 8(1) (2000) 97–110.https://doi.org/10.1007/BF02564830Google Scholar
  20. S. K. Suneja, S. Khurana: Saddle-point type optimality criteria for optimization problem over cones, Opsearch 40(2) (2003) 81–96.https://doi.org/10.1007/BF03398686Google Scholar
  21. S. K. Suneja, M. Bathia: Cone convex and related functions in optimization over topological vector spaces, Asia-Pac. J. Oper. Res. 24(6) (2007) 741–754.https://doi.org/10.1142/S0217595907001504Google Scholar
  22. S. K. Suneja, S. Khurana, Vani: Generalized nonsmooth invexity over cones in vector optimization, Eur. J. Oper. Res. 186(1) (2008) 28–40.https://doi.org/10.1016/j.ejor.2007.01.047Google Scholar

B. Hernández-Jiménez, R. Osuna-Gómez, M. A. Rojas-Medar. “Characterization of Weakly Efficient Solutions for Nonlinear Multiobjective Programming Problems. Duality.” Journal of Convex Analysis 21 (2014), No. 4, 1007–1022. https://doi.org/10.68381/jca21054