Have a personal or library account? Click to login
Sufficient optimality condition and duality of nondifferentiable minimax ratio constraint problems under (p, r)-ρ-(η, θ)-invexity Cover

Sufficient optimality condition and duality of nondifferentiable minimax ratio constraint problems under (p, r)-ρ-(η, θ)-invexity

Open Access
|Aug 2022

Abstract

There are several classes of decision-making problems that explicitly or implicitly prompt fractional programming problems. Portfolio selection problems, agricultural planning, information transfer, numerical analysis of stochastic processes, and resource allocation problems are just a few examples. The huge number of applications of minimax fractional programming problems inspired us to work on this topic. This paper is concerned with a nondifferentiable minimax fractional programming problem. We study a parametric dual model, corresponding to the primal problem, and derive the sufficient optimality condition for an optimal solution to the considered problem. Further, we obtain the various duality results under (p, r)-ρ-(η, θ)-invexity assumptions. Also, we identify a function lying exclusively in the class of (−1, 1)-ρ-(η, θ)-invex functions but not in the class of (1, −1)-invex functions and convex function already existing in the literature. We have given a non-trivial model of nondifferentiable minimax problem and obtained its optimal solution using optimality results derived in this paper.

DOI: https://doi.org/10.2478/candc-2022-0005 | Journal eISSN: 2720-4278 | Journal ISSN: 0324-8569
Language: English
Page range: 71 - 89
Submitted on: Jul 1, 2021
Accepted on: Jan 1, 2022
Published on: Aug 12, 2022
Published by: Systems Research Institute Polish Academy of Sciences
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2022 Navdeep Kailey, Sonali Sethi, Shivani Saini, published by Systems Research Institute Polish Academy of Sciences
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.