Nondifferentiable minimax fractional programming problem with nonsmooth generalized convex functions

Anurag Jayswal; I. Ahmad; Dilip Kumar

Nondifferentiable minimax fractional programming problem with nonsmooth generalized convex functions

Anurag Jayswal^*, I. Ahmad, Dilip Kumar

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

The aim of this paper is to obtain sufficient optimality conditions for a nondifferentiable minimax fractional programming problem where the involved functions are nonsmooth (F, α, p, d)-convex. Subsequently, these optimality conditions are utilized as a basis for formulating dual problems. Weak, strong and strict converse duality theorems are also obtained for two types of dual models. Our results generalize some previously known results on this topic in the literature.

Original language	English
Pages (from-to)	57-75
Number of pages	19
Journal	Communications on Applied Nonlinear Analysis
Volume	18
Issue number	4
State	Published - Oct 2011

Keywords

D)-convexity
Duality
Minimax fractional programming problem
Nonsmooth (F
P
Sufficient optimality conditions
α

ASJC Scopus subject areas

Analysis
Applied Mathematics

Cite this

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title = "Nondifferentiable minimax fractional programming problem with nonsmooth generalized convex functions",

abstract = "The aim of this paper is to obtain sufficient optimality conditions for a nondifferentiable minimax fractional programming problem where the involved functions are nonsmooth (F, α, p, d)-convex. Subsequently, these optimality conditions are utilized as a basis for formulating dual problems. Weak, strong and strict converse duality theorems are also obtained for two types of dual models. Our results generalize some previously known results on this topic in the literature.",

keywords = "D)-convexity, Duality, Minimax fractional programming problem, Nonsmooth (F, P, Sufficient optimality conditions, α",

author = "Anurag Jayswal and I. Ahmad and Dilip Kumar",

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language = "English",

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T1 - Nondifferentiable minimax fractional programming problem with nonsmooth generalized convex functions

AU - Jayswal, Anurag

AU - Ahmad, I.

AU - Kumar, Dilip

PY - 2011/10

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N2 - The aim of this paper is to obtain sufficient optimality conditions for a nondifferentiable minimax fractional programming problem where the involved functions are nonsmooth (F, α, p, d)-convex. Subsequently, these optimality conditions are utilized as a basis for formulating dual problems. Weak, strong and strict converse duality theorems are also obtained for two types of dual models. Our results generalize some previously known results on this topic in the literature.

AB - The aim of this paper is to obtain sufficient optimality conditions for a nondifferentiable minimax fractional programming problem where the involved functions are nonsmooth (F, α, p, d)-convex. Subsequently, these optimality conditions are utilized as a basis for formulating dual problems. Weak, strong and strict converse duality theorems are also obtained for two types of dual models. Our results generalize some previously known results on this topic in the literature.

KW - D)-convexity

KW - Duality

KW - Minimax fractional programming problem

KW - Nonsmooth (F

KW - P

KW - Sufficient optimality conditions

KW - α

UR - https://www.scopus.com/pages/publications/80055082435

M3 - Article

AN - SCOPUS:80055082435

SN - 1074-133X

VL - 18

SP - 57

EP - 75

JO - Communications on Applied Nonlinear Analysis

JF - Communications on Applied Nonlinear Analysis

IS - 4

ER -

Nondifferentiable minimax fractional programming problem with nonsmooth generalized convex functions

Abstract

Keywords

ASJC Scopus subject areas

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Cite this