• Management Science and Financial Engineering
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• v.14 no.2
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• pp.25-44
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• 2008

This paper concentrates on reduction of a Quadratic Fractional Integer Programming Problem (QFIP) to a 0-1 Mixed Linear Programming Problem (0-1 MLP). The solution technique is based on converting the integer variables to binary variables and then the resulting Quadratic Fractional 0-1 Programming Problem is linearized to a 0-1 Mixed Linear Programming problem. It is illustrated with the help of a numerical example and is solved using the LINDO software.

• Journal of applied mathematics & informatics
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• v.31 no.5_6
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• pp.853-867
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• 2013

In this paper a methodology is developed to solve a nonlinear fractional programming problem, whose objective function and constraints are interval valued functions. Interval valued convex fractional programming problem is studied. This model is transformed to a general convex programming problem and relation between the original problem and the transformed problem is established. These theoretical developments are illustrated through a numerical example.

• Communications of the Korean Mathematical Society
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• v.20 no.4
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• pp.837-847
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• 2005

We consider multiobjective fractional programming problems with generalized invexity. An equivalent multiobjective programming problem is formulated by using a modification of the objective function due to Antczak. We give relations between a multiobjective fractional programming problem and an equivalent multiobjective fractional problem which has a modified objective function. And we present modified vector saddle point theorems.

• Journal of applied mathematics & informatics
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• v.25 no.1_2
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• pp.85-107
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• 2007

In this paper, we develop a fairly large number of sets of global semiparametric sufficient efficiency conditions under various generalized (${\theta},{\eta},{\rho}$)-V-invexity assumptions for a multiobjective fractional programming problem involving arbitrary norms.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.181-191
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• 2006

In this paper, multiobjective generalized fractional programming problems with set functions are considered, in which objective functions are maximum of finite fractional set functions. At first, optimality conditions are established. Then, saddle existence theorem is proved.

• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.281-292
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• 2007

A pair of symmetric fractional variational programming problems is formulated over cones. Weak, strong, converse and self duality theorems are discussed under pseudoinvexity. Static symmetric dual fractional programs are included as special case and corresponding symmetric duality results are merely stated.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.13 no.2
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• pp.101-108
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• 2009

In this paper, we introduce generalized multiobjective fractional programming problem with two kinds of inequality constraints. Kuhn-Tucker sufficient and necessary optimality conditions are given. We formulate a generalized multiobjective dual problem and establish weak and strong duality theorems for an efficient solution under generalized convexity conditions.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.151-160
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• 2008

A pair of multiobjective fractional symmetric dual programs is formulated over arbitrary cones. Weak, strong and converse duality theorems are proved under pseudoinvexity assumptions. A self duality theorem is also discussed.

• Journal of the Chungcheong Mathematical Society
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• v.29 no.3
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• pp.465-475
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• 2016

We establish necessary and sufficient optimality conditions for a class of generalized nondifferentiable fractional optimization programming problems. Moreover, we prove the weak and strong duality theorems under (V, ${\rho}$)-invexity assumption.

• Journal of applied mathematics & informatics
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• v.28 no.5_6
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• pp.1535-1544
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• 2010

Sufficient optimality conditions for a class of generalized non-differentiable fractional optimization programming problems are established. Moreover, we prove the weak and strong duality theorems under (V, $\rho$)-invexity assumption.