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Lagrangian Approach to Quasiconvex Programing

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Abstract

For a mathematical programming problem, we consider a Lagrangian approach inspired by quasiconvex duality, but as close as possible to the usual convex Lagrangian. We focus our attention on the set of multipliers and we look for their interpretation as generalized derivatives of the performance function associated with a simple perturbation of the given problem. We do not use quasiconvex dualities, but simple direct arguments.

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References

  1. Martinez-legaz, J. E., On Lower Subdifferentiable Functions, Trends in Mathematical Optimization, Edited by K. H. Hoffmann et al., International 647 Series on Numerical Mathematics, Birkhäuser, Basel, Switzerland, Vol. 84, pp. 197–232, 1988.

    Google Scholar 

  2. Penot, J. P., and Volle, M., On Quasiconvex Duality, Mathematics of Operations Research, Vol. 15, pp. 597–625, 1990.

    Google Scholar 

  3. Luenberger, D., Quasiconvex Programming, SIAM Journal on Applied Analysis, Vol. 16, pp. 1090–1095, 1968.

    Google Scholar 

  4. Penot, J. P., and Volle, M., Surrogate Programming and Multipliers in Quasiconvex Programming, Preprint, University of Avignon, 1997, SIAM Journal on Optimization, to appear.

  5. Crouzeix, J. P., Conjugacy in Quasiconvex Analysis, Convex Analysis and Its Applications, Edited by A. Auslender, Lecture Notes in Economics and Mathematical Systems, Springer Verlag, Berlin, Germany, Vol. 144, pp. 66–99, 1977.

    Google Scholar 

  6. Penot, J.P., What is Quasiconvex Analysis?, Optimization, Vol. 47, pp. 35–110, 2000.

    Google Scholar 

  7. Penot, J.P., Characterization of Solution Sets of Quasiconvex Programs, Journal of Optimization Theory and Applications, Vol. 117, pp. 627–636, 2003.

    Google Scholar 

  8. Rubinov, A.M., Huang, X. X., and Yang, X.Q., The Zero Duality Gap Property and Lower Semicontinuity of the Perturbation Functions, Preprint, University of Ballarat, Ballarat, Victoria, Australia.

  9. Minoux, M., Programmation Mathématique: Théorie et Algorithmes, Dunod, Paris, France, Vol. 1, 1983.

    Google Scholar 

  10. Pshenichnyi, B.N., Necessary Conditions for an Extremum, Dekker, New York, NY, 1971.

    Google Scholar 

  11. Plastria, F., Lower Subdifferentiable Functions and Their Minimization by Cutting Planes, Journal of Optimization Theory and Applications Vol. 46, pp. 37–54, 1985.

    Google Scholar 

  12. GutiÉrrez J. M., Infragradientes y Direcciones de Decrecimiento, Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales, Madrid, Vol. 78, pp. 523–532, 1984.

    Google Scholar 

  13. Penot, J.P., Duality for Radiant and Shady Programs, Acta Mathematica Vietnamica, Vol. 22, pp. 541–566, 1997.

    Google Scholar 

  14. Rubinov, A., Abstract Convexity and Global Optimization, Kluwer, Dordrecht, Netherlands, 2000.

    Google Scholar 

  15. Greenberg, H. P., and Pierskalla, W.P., Quasiconjugate Function and Surrogate Quality, Cahiers du Centre d'Etude de Recherche Operationnelle, Vol. 15, pp. 437–448, 1973.

    Google Scholar 

  16. Penot, J. P., and Quang, P.H., On the Cutting Plane Algorithm, Preprint, University of Pau, Pau, France, 1992.

    Google Scholar 

  17. Crouzeix, J.P., Contribution à l'Etude des Fonctions Quasi-Convexes, Thèse d'Etat, University of Clermont II, 1977.

  18. Penot, J. P., Are Generalized Derivatives Useful for Generalized Convex Functions?, Generalized Convexity and Generalized Monotonicity, Edited by J. P. Crouzeix et al., Kluwer, Dordrecht, Netherlands, pp. 3–59, 1998.

    Google Scholar 

  19. Penot, J. P., and Zalinescu, C., Elements of Quasiconvex Subdifferential Calculus, Journal of Convex Analysis, Vol. 7, pp. 243–269, 2000.

    Google Scholar 

  20. Bachir, M., Daniilidis A., and Penot J. P., Lower Subdifferentiability and Integration, Set-Valued Analysis, Vol. 10, pp. 89–108, 2002.

    Google Scholar 

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Penot, J. Lagrangian Approach to Quasiconvex Programing. Journal of Optimization Theory and Applications 117, 637–647 (2003). https://doi.org/10.1023/A:1023957924086

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