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On Gradient Simplex Methods for Linear Programs (2004)

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by R. R. Vemuganti
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BibTeX

@MISC{Vemuganti04ongradient,
    author = {R. R. Vemuganti},
    title = {On Gradient Simplex Methods for Linear Programs},
    year = {2004}
}

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Abstract

A variety of pivot column selection rules based upon the gradient criteria (including the steepest edge) have been explored to improve the efficiency of the primal simplex method. Simplex-like algorithms have been proposed imbedding the gradient direction (GD) which includes all variables whose increase or decrease leads to an improvement in the objective function. Recently a frame work has been developed in the simplex method to incorporate the reduced-gradient direction (RGD) consisting of only variables whose increase leads to an improvement in the objective function. In this pa-per, the results are extended to embed GD in the simplex method based on the concept of combining directions. Also mathematical properties related to combining directions as well as deleting a variable from all basic directions are presented.

Keyphrases

linear program    gradient simplex method    objective function    simplex method    frame work    gradient criterion    basic direction    pivot column selection rule    simplex-like algorithm    gradient direction    mathematical property    primal simplex method    reduced-gradient direction   

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