Mixed Lagrange function in minimax fractional programming problems

Mixed Lagrange function in minimax fractional programming problems

In this paper, the aim of this work to study mixed Lagrange function for the minimax fractional programming problem with nonsmooth exponential (p, r) -invex functions with respect to η . We introduced a new concept of saddle point for a mixed Lagrange function. We present the equivalence between a saddle point of the mixed Lagrange function and an optimal solution in the considered minimax fractional programming problem under appropriate nonsmooth exponential (p, r) -invexity hypotheses.

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  • [1] Ahmad I, Husain Z. Duality in nondifferentiable minimax fractional programming with generalized convexity. Applied Mathematics and Computation 2006; 176: 545-551.
  • [2] Antczak T. Saddle point criteria and duality in multiobjective programming via an η -approximation method. ANZIAM Journal 2005; 47: 155-172.
  • [3] Antczak T. Saddle point criteria in semi-infinite minimax fractional programming under (Φ, ρ) -invexity. Filomat 2017; 31 (9): 2557-25574.
  • [4] Bector CR, Chandra S, Abha. On incomplete Lagrange function and saddle point optimality criteria in mathematical Programmong. Journal of Mathematical Analysis and Applications 2000; 251: 2-12.
  • [5] Bector CR, Chandra S, Bector MK. Generalized fractional programming duality: A parametric approach. Journal of Optimization Theory and Applications 1989; 60: 243-260.
  • [6] Clarke FH. Optimization and nonsmooth analysis. Wiley-Interscience, New York, 1983.
  • [7] Hiriart-Urruty JB. On optimality conditions in nondifferentiable programming. Mathematical Programming 1978; 14: 73-86.
  • [8] Ho SC. Saddle point criteria in multiobjective fractional programming involving exponential invexity. Bulletin of the Malaysian Mathematical Sciences Society 2018; 41 (4): 1923-1934.
  • [9] Ho SC, Lai HC. Optimality and duality for nonsmooth minimax fractional programming problem with exponential (p, r) -invexity. Journal Nonlinear and Convex Analysis 2012; 13 (3) 433-447.
  • [10] Ho SC, Lai HC. Duality for nonsmooth minimax fractional programming with exponential (p, r) -invexity. Journal Nonlinear and Convex Analysis 2014; 15 (4): 711-725.
  • [11] Ho SC, Lai HC. Mixed type duality on nonsmooth minimax fractional programming involving exponential (p, r) - invexity. Numerical Functional Analysis and Optimization 2014; 35 (12): 1560-1578.
  • [12] Lai HC, Chen HM. Duality on a nondifferentiable minimax fractional programming. Journal of Global Optimization 2012; 54: 295-306.
  • [13] Lai HC, Liu JC. A new characterization on optimality and duality for nondifferentiable minimax fractional programming problems. Journal Nonlinear and Convex Analysis 2011; 12 (1): 69-80.
  • [14] Liu JC. Optimality and duality for generalized fractional programming involving nonsmooth pseudoinvex functions. Journal of Mathematical Analysis and Applications 1996; 202: 667-685.
  • [15] Rockafellar RT. Convex Analysis. Princeton, NJ, USA: Princeton University. Press 1970.
  • [16] Xu ZK. Saddle-point type optimality criteria for generalized fractional programming. Journal of Optimization Theory and Applications 1988; 57: 189-196.
  • [17] Yang XM, Yang XQ, Teo KL. Duality and saddle-point type optimality for generalized nonlinear fractional programming. Journal of Mathematical Analysis and Applications 2004; 289: 100-109.
  • [18] Zalmai GJ. Optimality criteria and duality for a class of minimax programming problems with generalized invexity conditions. Utilitas Mathematica 1987; 32: 35-57.
  • [19] Zalmai GJ. Optimality conditions and duality models for generalized fractional programming problems containing locally subdifferentiable and ρ-convex functions. Optimization 1995; 32: 95-124.
  • [20] Zalmai GJ. Saddle point and Lagrangian-type duality for discrete minimax fraction subset programming problems with generalized convex functions. Journal of Mathematical Analysis and Applications 2006; 313: 484-503.