𝔖 Bobbio Scriptorium
✦   LIBER   ✦

On Mixed Duality in Mathematical Programming

✍ Scribed by C.R. Bector; Suresh Chandra; Abha

Publisher
Elsevier Science
Year
2001
Tongue
English
Weight
87 KB
Volume
259
Category
Article
ISSN
0022-247X

No coin nor oath required. For personal study only.

✦ Synopsis

An incomplete Lagrange function is introduced for a class of nonlinear programming problems which explains the reason behind the construction of the Mond᎐Weir᎐type dual. A mixed-type dual is presented for a class of fractional and generalized fractional programming problems, and various duality theorems are established. Several duals already reported in the literature follow as special cases of this study.

πŸ“œ SIMILAR VOLUMES

Higher-Order Generalized Invexity and Du
Higher-Order Generalized Invexity and Duality in Mathematical Programming
✍ Shashi K. Mishra; Norma G. Rueda πŸ“‚ Article πŸ“… 2000 πŸ› Elsevier Science 🌐 English βš– 124 KB

In this paper, we introduce the concepts of higher-order type-I, pseudo-type-I, and quasi-type-I functions and establish various higher-order duality results involving these functions.

Duality in fractional programming
Duality in fractional programming
✍ Finn Kydland πŸ“‚ Article πŸ“… 1972 πŸ› John Wiley and Sons 🌐 English βš– 262 KB
On Ratio Invexity in Mathematical Progra
On Ratio Invexity in Mathematical Programming
✍ Zulfiqar A. Khan; Morgan A. Hanson πŸ“‚ Article πŸ“… 1997 πŸ› Elsevier Science 🌐 English βš– 111 KB

The nonlinear fractional programming problem is considered. The functions involved in the objective function and constraints are assumed to be invex and differentiable. It is shown that the ratio of invex functions is invex. Sufficient optimality and duality theorems are presented for an invex fract

Duality in Nondifferentiable Vector Prog
Duality in Nondifferentiable Vector Programming
✍ R. Osuna-GΓ³mez; A. RufiΓ‘n-Lizana; P. Ruı́z-Canales πŸ“‚ Article πŸ“… 2001 πŸ› Elsevier Science 🌐 English βš– 108 KB

In this paper we study the saddle point optimality conditions and Lagrange duality in multiobjective optimization for generalized subconvex-like functions. We obtain results which will allow us to characterize the solutions for multiobjective programming problems from the saddle point conditions and