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Volume 18, Issue 6
Primal Perturbation Simplex Algorithms for Linear Programming

Ping-Qi Pan

J. Comp. Math., 18 (2000), pp. 587-596.

Published online: 2000-12

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  • Abstract

In this paper, we propose two new perturbation simplex variants. Solving linear programming problems without introducing artificial variables, each of the two uses the dual pivot rule to achieve primal feasibility, and then the primal pivot rule to achieve optimality. The second algorithm, a modification of the first, is designed to handle highly degenerate problems more efficiently. Some interesting results concerning merit of the perturbation are established. Numerical results from preliminary tests are also reported.  

  • Keywords

Linear programming, Perturbation, Primal simplex algorithm, Partially revised tableau.

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COPYRIGHT: © Global Science Press

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@Article{JCM-18-587, author = {Pan , Ping-Qi}, title = {Primal Perturbation Simplex Algorithms for Linear Programming}, journal = {Journal of Computational Mathematics}, year = {2000}, volume = {18}, number = {6}, pages = {587--596}, abstract = {

In this paper, we propose two new perturbation simplex variants. Solving linear programming problems without introducing artificial variables, each of the two uses the dual pivot rule to achieve primal feasibility, and then the primal pivot rule to achieve optimality. The second algorithm, a modification of the first, is designed to handle highly degenerate problems more efficiently. Some interesting results concerning merit of the perturbation are established. Numerical results from preliminary tests are also reported.  

}, issn = {1991-7139}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jcm/9069.html} }
TY - JOUR T1 - Primal Perturbation Simplex Algorithms for Linear Programming AU - Pan , Ping-Qi JO - Journal of Computational Mathematics VL - 6 SP - 587 EP - 596 PY - 2000 DA - 2000/12 SN - 18 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jcm/9069.html KW - Linear programming, Perturbation, Primal simplex algorithm, Partially revised tableau. AB -

In this paper, we propose two new perturbation simplex variants. Solving linear programming problems without introducing artificial variables, each of the two uses the dual pivot rule to achieve primal feasibility, and then the primal pivot rule to achieve optimality. The second algorithm, a modification of the first, is designed to handle highly degenerate problems more efficiently. Some interesting results concerning merit of the perturbation are established. Numerical results from preliminary tests are also reported.  

Pan , Ping-Qi. (2000). Primal Perturbation Simplex Algorithms for Linear Programming. Journal of Computational Mathematics. 18 (6). 587-596. doi:
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