Mixed symmetric duality in nondifferentiable mathematical programming

Xin Min Yang; Kok Lay Teo; Xiaoqi Yang

Mixed symmetric duality in nondifferentiable mathematical programming

Xin Min Yang, Kok Lay Teo, Xiaoqi Yang

Research output: Journal article publication › Journal article › Academic research › peer-review

Abstract

A mixed symmetric dual formulation is presented for a class of nondifferentiable nonlinear programming problems with multiple arguments. Weak, strong and converse duality theorems are established. The mixed symmetric dual formulation unifies the two exsting symmetric dual formulations in the literature.

Original language	English
Pages (from-to)	805-815
Number of pages	11
Journal	Indian Journal of Pure and Applied Mathematics
Volume	34
Issue number	5
Publication status	Published - 1 May 2003

Keywords

Generalized Convexity
Nondifferentiable Nonlinear Programming
Support Function
Symmetric Duality

ASJC Scopus subject areas

Mathematics(all)
Applied Mathematics

More information

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Cite this

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title = "Mixed symmetric duality in nondifferentiable mathematical programming",

abstract = "A mixed symmetric dual formulation is presented for a class of nondifferentiable nonlinear programming problems with multiple arguments. Weak, strong and converse duality theorems are established. The mixed symmetric dual formulation unifies the two exsting symmetric dual formulations in the literature.",

keywords = "Generalized Convexity, Nondifferentiable Nonlinear Programming, Support Function, Symmetric Duality",

author = "Yang, {Xin Min} and Teo, {Kok Lay} and Xiaoqi Yang",

year = "2003",

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AU - Teo, Kok Lay

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KW - Nondifferentiable Nonlinear Programming

KW - Support Function

KW - Symmetric Duality

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