Advertisement

Higher Order Parametric Duality Models

  • Ram U. VermaEmail author
Chapter
Part of the Infosys Science Foundation Series book series (ISFS)

Abstract

A hybrid class of second-order parametric duality theorems for a discrete minmax fractional programming problem regarding higher order necessary and sufficient optimality conditions is presented in this chapter.

References

  1. 1.
    Verma, R.U., Zalmai, G.J.: New formulations for higher order parametric duality models in discrete minmax fractional programming. PanAm. Math. J. 26(4), 24–43 (2016)Google Scholar
  2. 2.
    Zalmai, G.J.: Optimality conditions and duality for constrained measurable subset selection problems with minmax objective functions. Optimization 20, 377–395 (1989)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Zalmai, G.J.: Proper efficiency principles and duality models for a class of continuous-time multiobjective fractional programming problems with operator constraints. J. Stat. Manage. Syst. 1, 11–59 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Zalmai, G.J.: Hanson-Antczak-type generalized \((\alpha,\beta,\gamma,\xi,\eta,\rho,\theta )\)-V-invex functions in semiinfinite multiobjective fractional programming, Part III: Second order parametric duality models. Adv. Nonlinear Variational Inequalities 16(2), 91–126 (2013)zbMATHMathSciNetGoogle Scholar
  5. 5.
    Zalmai, G.J.: Hanson-Antczak-type generalized \((\alpha,\beta,\gamma,\xi,\eta,\rho,\theta )\)-V-invex functions in semiinfinite multiobjective fractional programming, Part I: Sufficient efficiency conditions. Adv. Nonlinear Variational Inequalities 16(1), 91–114 (2013)zbMATHMathSciNetGoogle Scholar
  6. 6.
    Zhian, L., Qingkai, Y.: Duality for a class of multiobjective control problems with generalized invexity. J. Math. Anal. Appl. 256, 446–461 (2001)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of MathematicsTexas State UniversitySan MarcosUSA

Personalised recommendations